fullrefs.bib

@misc{wandb,
title = {Experiment Tracking with Weights and Biases},
year = {2020},
note = {Software available from wandb.com},
url={https://www.wandb.com/},
author = {Biewald, Lukas},
}

@ARTICLE{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}

@inproceedings{hoffman2016elbo,
  title={Elbo surgery: yet another way to carve up the variational evidence lower bound},
  author={Hoffman, Matthew D and Johnson, Matthew J},
  booktitle={Workshop in Advances in Approximate Bayesian Inference, NIPS},
  volume={1},
  pages={2},
  year={2016}
}



@article{ebner_facesdatabase_2010,
	title = {{FACES}—{A} database of facial expressions in young, middle-aged, and older women and men: {Development} and validation},
	volume = {42},
	issn = {1554-351X, 1554-3528},
	shorttitle = {{FACES}—{A} database of facial expressions in young, middle-aged, and older women and men},
	url = {http://link.springer.com/10.3758/BRM.42.1.351},
	doi = {10.3758/BRM.42.1.351},
	language = {en},
	number = {1},
	urldate = {2021-01-07},
	journal = {Behavior Research Methods},
	author = {Ebner, Natalie C. and Riediger, Michaela and Lindenberger, Ulman},
	month = feb,
	year = {2010},
	pages = {351--362},
	file = {Ebner et al. - 2010 - FACES—A database of facial expressions in young, m.pdf:/home/wolf/Zotero/storage/ZCEE2PG2/Ebner et al. - 2010 - FACES—A database of facial expressions in young, m.pdf:application/pdf},
}

@article{belli_image-conditioned_2019,
	title = {Image-{Conditioned} {Graph} {Generation} for {Road} {Network} {Extraction}},
	url = {http://arxiv.org/abs/1910.14388},
	abstract = {Deep generative models for graphs have shown great promise in the area of drug design, but have so far found little application beyond generating graph-structured molecules. In this work, we demonstrate a proof of concept for the challenging task of road network extraction from image data. This task can be framed as image-conditioned graph generation, for which we develop the Generative Graph Transformer (GGT), a deep autoregressive model that makes use of attention mechanisms for image conditioning and the recurrent generation of graphs. We benchmark GGT on the application of road network extraction from semantic segmentation data. For this, we introduce the Toulouse Road Network dataset, based on real-world publicly-available data. We further propose the StreetMover distance: a metric based on the Sinkhorn distance for effectively evaluating the quality of road network generation. The code and dataset are publicly available.},
	urldate = {2020-05-08},
	journal = {arXiv:1910.14388 [cs, stat]},
	author = {Belli, Davide and Kipf, Thomas},
	month = oct,
	year = {2019},
	note = {arXiv: 1910.14388},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Presented at NeurIPS 2019 Workshop on Graph Representation Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/BA9GESIJ/1910.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/J62CRRMB/Belli and Kipf - 2019 - Image-Conditioned Graph Generation for Road Networ.pdf:application/pdf},
}

@article{kingma_introduction_2019,
	title = {An {Introduction} to {Variational} {Autoencoders}},
	volume = {12},
	issn = {1935-8237, 1935-8245},
	url = {https://www.nowpublishers.com/article/Details/MAL-056},
	doi = {10.1561/2200000056},
	abstract = {An Introduction to Variational Autoencoders},
	language = {English},
	number = {4},
	urldate = {2020-05-07},
	journal = {Foundations and Trends® in Machine Learning},
	author = {Kingma, Diederik P. and Welling, Max},
	month = nov,
	year = {2019},
	note = {Publisher: Now Publishers, Inc.},
	pages = {307--392},
	file = {Snapshot:/home/wolf/Zotero/storage/K2AB4SFQ/MAL-056.html:text/html;Full Text PDF:/home/wolf/Zotero/storage/8I69DBEQ/Kingma and Welling - 2019 - An Introduction to Variational Autoencoders.pdf:application/pdf},
}

@article{yang_feedback_2020,
	title = {Feedback {Recurrent} {AutoEncoder}},
	url = {http://arxiv.org/abs/1911.04018},
	abstract = {In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the redundancy along the time dimension and allows a compact discrete representation of the data to be learned. We demonstrate its effectiveness in speech spectrogram compression. Specifically, we show that the FRAE, paired with a powerful neural vocoder, can produce high-quality speech waveforms at a low, fixed bitrate. We further show that by adding a learned prior for the latent space and using an entropy coder, we can achieve an even lower variable bitrate.},
	urldate = {2020-05-07},
	journal = {arXiv:1911.04018 [cs, eess, stat]},
	author = {Yang, Yang and Sautière, Guillaume and Ryu, J. Jon and Cohen, Taco S.},
	month = feb,
	year = {2020},
	note = {arXiv: 1911.04018},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/UJVMX3D7/1911.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/8X92ZTZG/Yang et al. - 2020 - Feedback Recurrent AutoEncoder.pdf:application/pdf},
}

@article{rezende_variational_2016,
	title = {Variational {Inference} with {Normalizing} {Flows}},
	url = {http://arxiv.org/abs/1505.05770},
	abstract = {The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.},
	urldate = {2020-05-05},
	journal = {arXiv:1505.05770 [cs, stat]},
	author = {Rezende, Danilo Jimenez and Mohamed, Shakir},
	month = jun,
	year = {2016},
	note = {arXiv: 1505.05770},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Computation, Statistics - Methodology},
	annote = {Comment: Proceedings of the 32nd International Conference on Machine Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/RK8HUUL2/1505.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/ZYV38DAF/Rezende and Mohamed - 2016 - Variational Inference with Normalizing Flows.pdf:application/pdf},
}

@inproceedings{jin_unsupervised_2019,
	address = {Florence, Italy},
	title = {Unsupervised {Learning} of {PCFGs} with {Normalizing} {Flow}},
	url = {https://www.aclweb.org/anthology/P19-1234},
	doi = {10.18653/v1/P19-1234},
	abstract = {Unsupervised PCFG inducers hypothesize sets of compact context-free rules as explanations for sentences. PCFG induction not only provides tools for low-resource languages, but also plays an important role in modeling language acquisition (Bannard et al., 2009; Abend et al. 2017). However, current PCFG induction models, using word tokens as input, are unable to incorporate semantics and morphology into induction, and may encounter issues of sparse vocabulary when facing morphologically rich languages. This paper describes a neural PCFG inducer which employs context embeddings (Peters et al., 2018) in a normalizing flow model (Dinh et al., 2015) to extend PCFG induction to use semantic and morphological information. Linguistically motivated sparsity and categorical distance constraints are imposed on the inducer as regularization. Experiments show that the PCFG induction model with normalizing flow produces grammars with state-of-the-art accuracy on a variety of different languages. Ablation further shows a positive effect of normalizing flow, context embeddings and proposed regularizers.},
	urldate = {2020-05-05},
	booktitle = {Proceedings of the 57th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Jin, Lifeng and Doshi-Velez, Finale and Miller, Timothy and Schwartz, Lane and Schuler, William},
	month = jul,
	year = {2019},
	pages = {2442--2452},
	file = {Full Text PDF:/home/wolf/Zotero/storage/IYM2VFG9/Jin et al. - 2019 - Unsupervised Learning of PCFGs with Normalizing Fl.pdf:application/pdf},
}

@article{che_towards_2018,
	title = {Towards {Better} {UD} {Parsing}: {Deep} {Contextualized} {Word} {Embeddings}, {Ensemble}, and {Treebank} {Concatenation}},
	shorttitle = {Towards {Better} {UD} {Parsing}},
	url = {http://arxiv.org/abs/1807.03121},
	abstract = {This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies. We base our submission on Stanford's winning system for the CoNLL 2017 shared task and make two effective extensions: 1) incorporating deep contextualized word embeddings into both the part of speech tagger and parser; 2) ensembling parsers trained with different initialization. We also explore different ways of concatenating treebanks for further improvements. Experimental results on the development data show the effectiveness of our methods. In the final evaluation, our system was ranked first according to LAS (75.84\%) and outperformed the other systems by a large margin.},
	urldate = {2020-05-05},
	journal = {arXiv:1807.03121 [cs]},
	author = {Che, Wanxiang and Liu, Yijia and Wang, Yuxuan and Zheng, Bo and Liu, Ting},
	month = jul,
	year = {2018},
	note = {arXiv: 1807.03121},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: System description paper of our system (HIT-SCIR) for the CoNLL 2018 shared task on Universal Dependency parsing, which was ranked first in the LAS evaluation. Fix typos and grammar errors. Add the results of parser without ensemble},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/KL6352GX/1807.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/IMFY9XFP/Che et al. - 2018 - Towards Better UD Parsing Deep Contextualized Wor.pdf:application/pdf},
}

@article{shazeer_swivel_2016,
	title = {Swivel: {Improving} {Embeddings} by {Noticing} {What}'s {Missing}},
	shorttitle = {Swivel},
	url = {http://arxiv.org/abs/1602.02215},
	abstract = {We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mutual information matrix via stochastic gradient descent. It uses a piecewise loss with special handling for unobserved co-occurrences, and thus makes use of all the information in the matrix. While this requires computation proportional to the size of the entire matrix, we make use of vectorized multiplication to process thousands of rows and columns at once to compute millions of predicted values. Furthermore, we partition the matrix into shards in order to parallelize the computation across many nodes. This approach results in more accurate embeddings than can be achieved with methods that consider only observed co-occurrences, and can scale to much larger corpora than can be handled with sampling methods.},
	urldate = {2020-05-04},
	journal = {arXiv:1602.02215 [cs]},
	author = {Shazeer, Noam and Doherty, Ryan and Evans, Colin and Waterson, Chris},
	month = feb,
	year = {2016},
	note = {arXiv: 1602.02215},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: 9 pages, 4 figures},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/8IXFYWWP/1602.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/D2WWGN24/Shazeer et al. - 2016 - Swivel Improving Embeddings by Noticing What's Mi.pdf:application/pdf},
}

@article{vrandevcic2014wikidata,
  title={Wikidata: a free collaborative knowledgebase},
  author={Vrande{\v{c}}i{\'c}, Denny and Kr{\"o}tzsch, Markus},
  journal={Communications of the ACM},
  volume={57},
  number={10},
  pages={78--85},
  year={2014},
  publisher={ACM New York, NY, USA}
}

@inproceedings{perozzi2014deepwalk,
  title={Deepwalk: Online learning of social representations},
  author={Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
  booktitle={Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={701--710},
  year={2014}
}

@article{goodfellow2014generative,
  title={Generative adversarial nets},
  author={Goodfellow, Ian and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
  journal={Advances in neural information processing systems},
  volume={27},
  pages={2672--2680},
  year={2014}
}

@inproceedings{glorot2010understanding,
  title={Understanding the difficulty of training deep feedforward neural networks},
  author={Glorot, Xavier and Bengio, Yoshua},
  booktitle={Proceedings of the thirteenth international conference on artificial intelligence and statistics},
  pages={249--256},
  year={2010}
}

@article{fengvaleriu,
  title={Valeriu Codreanu, SURFsara, Netherlands Ian Foster, UChicago \& ANL, USA Zhao Zhang, TACC, USA},
  author={Feng, Song and Torsten Hoefler, ETH and Li, Switzerland Jessy and Podareanu, Damian and Pu, Qifan and Qiu, Judy and Saletore, Vikram and Smorkalov, Mikhail E and Torres, Jordi}
}

@article{tang2011leveraging,
  title={Leveraging social media networks for classification},
  author={Tang, Lei and Liu, Huan},
  journal={Data Mining and Knowledge Discovery},
  volume={23},
  number={3},
  pages={447--478},
  year={2011},
  publisher={Springer}
}

@article{lerer_pytorch-biggraph_nodate,
	title = {{PyTorch}-{BigGraph}: {A} {Large}-scale {Graph} {Embedding} {System}},
	abstract = {Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks. Modern graphs, particularly in industrial applications, contain billions of nodes and trillions of edges, which exceeds the capability of existing embedding systems. We present PyTorch-BigGraph (PBG), an embedding system that incorporates several modifications to traditional multi-relation embedding systems that allow it to scale to graphs with billions of nodes and trillions of edges. PBG uses graph partitioning to train arbitrarily large embeddings on either a single machine or in a distributed environment. We demonstrate comparable performance with existing embedding systems on common benchmarks, while allowing for scaling to arbitrarily large graphs and parallelization on multiple machines. We train and evaluate embeddings on several large social network graphs as well as the full Freebase dataset, which contains over 100 million nodes and 2 billion edges.},
	language = {en},
	author = {Lerer, Adam and Wu, Ledell and Shen, Jiajun and Lacroix, Timothee and Wehrstedt, Luca and Bose, Abhijit and Peysakhovich, Alex},
	pages = {12},
	file = {Lerer et al. - PyTorch-BigGraph A Large-scale Graph Embedding Sy.pdf:/home/wolf/Zotero/storage/3K2HXJB3/Lerer et al. - PyTorch-BigGraph A Large-scale Graph Embedding Sy.pdf:application/pdf},
}

@inproceedings{karim_drug-drug_2019,
	title = {Drug-drug interaction prediction based on knowledge graph embeddings and convolutional-{LSTM} network},
	url = {https://research.vu.nl/en/publications/drug-drug-interaction-prediction-based-on-knowledge-graph-embeddi},
	doi = {10.1145/3307339.3342161},
	language = {English},
	urldate = {2020-05-01},
	booktitle = {{ACM}-{BCB} 2019 - {Proceedings} of the 10th {ACM} {International} {Conference} on {Bioinformatics}, {Computational} {Biology} and {Health} {Informatics}},
	publisher = {Association for Computing Machinery, Inc},
	author = {Karim, Md Rezaul and Cochez, Michael and Jares, Joao Bosco and Uddin, Mamtaz and Beyan, Oya and Decker, Stefan},
	month = sep,
	year = {2019},
	pages = {113--123},
	file = {Snapshot:/home/wolf/Zotero/storage/TBI6IG9Z/drug-drug-interaction-prediction-based-on-knowledge-graph-embeddi.html:text/html},
}

@incollection{groth_rdf2vec_2016,
	address = {Cham},
	title = {{RDF2Vec}: {RDF} {Graph} {Embeddings} for {Data} {Mining}},
	volume = {9981},
	isbn = {978-3-319-46522-7 978-3-319-46523-4},
	shorttitle = {{RDF2Vec}},
	url = {http://link.springer.com/10.1007/978-3-319-46523-4_30},
	abstract = {Linked Open Data has been recognized as a valuable source for background information in data mining. However, most data mining tools require features in propositional form, i.e., a vector of nominal or numerical features associated with an instance, while Linked Open Data sources are graphs by nature. In this paper, we present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. We generate sequences by leveraging local information from graph substructures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Our evaluation shows that such vector representations outperform existing techniques for the propositionalization of RDF graphs on a variety of different predictive machine learning tasks, and that feature vector representations of general knowledge graphs such as DBpedia and Wikidata can be easily reused for different tasks.},
	language = {en},
	urldate = {2020-05-01},
	booktitle = {The {Semantic} {Web} – {ISWC} 2016},
	publisher = {Springer International Publishing},
	author = {Ristoski, Petar and Paulheim, Heiko},
	editor = {Groth, Paul and Simperl, Elena and Gray, Alasdair and Sabou, Marta and Krötzsch, Markus and Lecue, Freddy and Flöck, Fabian and Gil, Yolanda},
	year = {2016},
	doi = {10.1007/978-3-319-46523-4_30},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {498--514},
	file = {Ristoski and Paulheim - 2016 - RDF2Vec RDF Graph Embeddings for Data Mining.pdf:/home/wolf/Zotero/storage/SNHAH8XG/Ristoski and Paulheim - 2016 - RDF2Vec RDF Graph Embeddings for Data Mining.pdf:application/pdf},
}

@article{wilcke_knowledge_2017,
	title = {The knowledge graph as the default data model for learning on heterogeneous knowledge},
	volume = {1},
	issn = {2451-8484},
	url = {https://research.vu.nl/en/publications/the-knowledge-graph-as-the-default-data-model-for-machine-learnin},
	doi = {10.3233/DS-170007},
	language = {English},
	number = {1-2},
	urldate = {2020-05-01},
	journal = {Data Science},
	author = {Wilcke, Xander and Bloem, Peter and Boer, Victor De},
	month = dec,
	year = {2017},
	note = {Publisher: IOS Press},
	pages = {39--57},
	file = {Full Text:/home/wolf/Zotero/storage/WXXSCZZ7/Wilcke et al. - 2017 - The knowledge graph as the default data model for .pdf:application/pdf;Snapshot:/home/wolf/Zotero/storage/GDBR87DG/the-knowledge-graph-as-the-default-data-model-for-machine-learnin.html:text/html},
}

@inproceedings{wilcke_knowledge_2018,
	title = {The {Knowledge} {Graph} for {End}-to-{End} {Learning} on {Heterogeneous} {Knowledge}},
	url = {https://research.vu.nl/en/publications/the-knowledge-graph-for-end-to-end-learning-on-heterogeneous-know-2},
	language = {English},
	urldate = {2020-05-01},
	author = {Wilcke, W. X. and Bloem, P. and Boer, Viktor de},
	month = mar,
	year = {2018},
	file = {Snapshot:/home/wolf/Zotero/storage/XMFFDMBU/the-knowledge-graph-for-end-to-end-learning-on-heterogeneous-know-2.html:text/html;Full Text PDF:/home/wolf/Zotero/storage/W9K67T7R/Wilcke et al. - 2018 - The Knowledge Graph for End-to-End Learning on Het.pdf:application/pdf},
}

@incollection{socher_reasoning_2013,
	title = {Reasoning {With} {Neural} {Tensor} {Networks} for {Knowledge} {Base} {Completion}},
	url = {http://papers.nips.cc/paper/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.pdf},
	urldate = {2020-04-20},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 26},
	publisher = {Curran Associates, Inc.},
	author = {Socher, Richard and Chen, Danqi and Manning, Christopher D and Ng, Andrew},
	editor = {Burges, C. J. C. and Bottou, L. and Welling, M. and Ghahramani, Z. and Weinberger, K. Q.},
	year = {2013},
	pages = {926--934},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/CRWB7IY5/5028-reasoning-with-neural-tensor-networks-for-knowledge-base-completion.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/WINTUXFA/Socher et al. - 2013 - Reasoning With Neural Tensor Networks for Knowledg.pdf:application/pdf},
}

@article{serafini_logic_2016,
	title = {Logic {Tensor} {Networks}: {Deep} {Learning} and {Logical} {Reasoning} from {Data} and {Knowledge}},
	shorttitle = {Logic {Tensor} {Networks}},
	url = {http://arxiv.org/abs/1606.04422},
	abstract = {We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google's tensorflow primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge completion.},
	urldate = {2020-04-17},
	journal = {arXiv:1606.04422 [cs]},
	author = {Serafini, Luciano and Garcez, Artur d'Avila},
	month = jul,
	year = {2016},
	note = {arXiv: 1606.04422},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Logic in Computer Science, Computer Science - Neural and Evolutionary Computing},
	annote = {Comment: 12 pages, 2 figs, 1 table, 27 references},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/UB6KLTXA/1606.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/52JYXFJI/Serafini and Garcez - 2016 - Logic Tensor Networks Deep Learning and Logical R.pdf:application/pdf},
}

@article{van_krieken_integrated_nodate,
	title = {Integrated {Learning} and {Reasoning} using {Gradient} {Descent}},
	language = {en},
	author = {van Krieken, Emile},
	pages = {85},
	file = {f106624807.pdf:/home/wolf/Zotero/storage/Q74UGEP2/van Krieken - Integrated Learning and Reasoning using Gradient D.pdf:application/pdf},
}

@article{wilcke_end--end_2020,
	title = {End-to-{End} {Entity} {Classification} on {Multimodal} {Knowledge} {Graphs}},
	url = {http://arxiv.org/abs/2003.12383},
	abstract = {End-to-end multimodal learning on knowledge graphs has been left largely unaddressed. Instead, most end-to-end models such as message passing networks learn solely from the relational information encoded in graphs' structure: raw values, or literals, are either omitted completely or are stripped from their values and treated as regular nodes. In either case we lose potentially relevant information which could have otherwise been exploited by our learning methods. To avoid this, we must treat literals and non-literals as separate cases. We must also address each modality separately and accordingly: numbers, texts, images, geometries, et cetera. We propose a multimodal message passing network which not only learns end-to-end from the structure of graphs, but also from their possibly divers set of multimodal node features. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities, including images and geometries, which are projected into a joint representation space together with their relational information. We demonstrate our model on a node classification task, and evaluate the effect that each modality has on the overall performance. Our result supports our hypothesis that including information from multiple modalities can help our models obtain a better overall performance.},
	urldate = {2020-04-15},
	journal = {arXiv:2003.12383 [cs]},
	author = {Wilcke, W. X. and Bloem, P. and de Boer, V. and Veer, R. H. van t and van Harmelen, F. A. H.},
	month = mar,
	year = {2020},
	note = {arXiv: 2003.12383},
	keywords = {Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: Submitted to the 17th International Conference on Principles of Knowledge Representation and Reasoning (2020)},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/DAZZK22X/2003.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/DGIQXPX7/Wilcke et al. - 2020 - End-to-End Entity Classification on Multimodal Kno.pdf:application/pdf},
}

@article{wang_submassive_nodate,
	title = {{SUBMASSIVE}: {Resolving} {Subclass} {Cycles} in {Very} {Large} {Knowledge} {Graphs}},
	abstract = {Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually present and expressed using the rdfs:subClassOf construct. From our examination, publicly available knowledge graphs contain many potentially erroneous cyclic subclass relations, a problem that can be exacerbated when different knowledge graphs are integrated as Linked Open Data. This paper presents an automatic approach for resolving such cycles at scale using automated reasoning by encoding the problem of cycle-resolving to a MAXSAT solver. The approach is tested on the LOD-a-lot dataset, and compared against a semi-automatic version of our algorithm. We show how the number of removed triples is a trade-off against the efficiency of the algorithm. The code and the resulting cycle-free class hierarchy of the LOD-a-lot are published at www.submassive.cc.},
	language = {en},
	author = {Wang, Shuai and Bloem, Peter and Raad, Joe and van Harmelen, Frank},
	pages = {10},
	file = {Wang et al. - SUBMASSIVE Resolving Subclass Cycles in Very Larg.pdf:/home/wolf/Zotero/storage/7RUKLRUJ/Wang et al. - SUBMASSIVE Resolving Subclass Cycles in Very Larg.pdf:application/pdf},
}

@inproceedings{grainger_semantic_2016,
	title = {The {Semantic} {Knowledge} {Graph}: {A} {Compact}, {Auto}-{Generated} {Model} for {Real}-{Time} {Traversal} and {Ranking} of any {Relationship} within a {Domain}},
	shorttitle = {The {Semantic} {Knowledge} {Graph}},
	doi = {10.1109/DSAA.2016.51},
	abstract = {This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain. The source code for our Semantic Knowledge Graph implementation is being published along with this paper to facilitate further research and extensions of this work.},
	booktitle = {2016 {IEEE} {International} {Conference} on {Data} {Science} and {Advanced} {Analytics} ({DSAA})},
	author = {Grainger, Trey and Aljadda, Khalifeh and Korayem, Mohammed and Smith, Andries},
	month = oct,
	year = {2016},
	keywords = {anomaly detection, Anomaly Detection, auto-generated model, compact graph representation, compact graphical representation, Context, corpus statistics, data classification, data cleansing, data mining, Data models, Graph Compression, graph theory, indexing, Information Retrieval, inverted index, Knowledge Graph, knowledge modeling, knowledge representation, Learning systems, mining system, natural language processing, Natural Language Processing, Natural languages, Ontologies, Ontology Learning, Pragmatics, real-time relationship traversal, recommendations systems, Relationship Extraction, relationship ranking, root cause analysis, semantic knowledge graph, semantic search, Semantic Search, semantic Web, Semantics, Text Analytics},
	pages = {420--429},
	file = {IEEE Xplore Abstract Record:/home/wolf/Zotero/storage/7JDYA2HY/references.html:text/html;IEEE Xplore Full Text PDF:/home/wolf/Zotero/storage/TTT5Z28P/Grainger et al. - 2016 - The Semantic Knowledge Graph A Compact, Auto-Gene.pdf:application/pdf},
}

@inproceedings{grainger_semantic_2016-1,
	title = {The {Semantic} {Knowledge} {Graph}: {A} {Compact}, {Auto}-{Generated} {Model} for {Real}-{Time} {Traversal} and {Ranking} of any {Relationship} within a {Domain}},
	shorttitle = {The {Semantic} {Knowledge} {Graph}},
	doi = {10.1109/DSAA.2016.51},
	abstract = {This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain. The source code for our Semantic Knowledge Graph implementation is being published along with this paper to facilitate further research and extensions of this work.},
	booktitle = {2016 {IEEE} {International} {Conference} on {Data} {Science} and {Advanced} {Analytics} ({DSAA})},
	author = {Grainger, Trey and Aljadda, Khalifeh and Korayem, Mohammed and Smith, Andries},
	month = oct,
	year = {2016},
	keywords = {anomaly detection, Anomaly Detection, auto-generated model, compact graph representation, compact graphical representation, Context, corpus statistics, data classification, data cleansing, data mining, Data models, Graph Compression, graph theory, indexing, Information Retrieval, inverted index, Knowledge Graph, knowledge modeling, knowledge representation, Learning systems, mining system, natural language processing, Natural Language Processing, Natural languages, Ontologies, Ontology Learning, Pragmatics, real-time relationship traversal, recommendations systems, Relationship Extraction, relationship ranking, root cause analysis, semantic knowledge graph, semantic search, Semantic Search, semantic Web, Semantics, Text Analytics},
	pages = {420--429},
	file = {IEEE Xplore Abstract Record:/home/wolf/Zotero/storage/CXYHJUKJ/7796928.html:text/html;IEEE Xplore Full Text PDF:/home/wolf/Zotero/storage/BNULC8WD/Grainger et al. - 2016 - The Semantic Knowledge Graph A Compact, Auto-Gene.pdf:application/pdf},
}

@article{byun_chronograph_2020,
	title = {{ChronoGraph}: {Enabling} {Temporal} {Graph} {Traversals} for {Efficient} {Information} {Diffusion} {Analysis} over {Time}},
	volume = {32},
	issn = {1558-2191},
	shorttitle = {{ChronoGraph}},
	doi = {10.1109/TKDE.2019.2891565},
	abstract = {ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-oriented systems, this traversal-oriented system is suitable for analyzing information diffusion over time without violating a time constraint on temporal paths. The cornerstone of ChronoGraph aims at bridging the chasm between point-based semantics and period-based semantics and the gap between temporal graph traversals and static graph traversals. Therefore, our graph model and traversal language provide the temporal syntax for both semantics, and we present a method converting point-based semantics to period-based ones. Also, ChronoGraph exploits the temporal support and parallelism to handle the temporal degree, which explosively increases compared to static graphs. We demonstrate how three traversal recipes can be implemented on top of our system: temporal breadth-first search (tBFS), temporal depth-first search (tDFS), and temporal single source shortest path (tSSSP). According to our evaluation, our temporal support and parallelism enhance temporal graph traversals in terms of convenience and efficiency. Also, ChronoGraph outperforms existing property graph databases in terms of temporal graph traversals. We prototype ChronoGraph by extending Tinkerpop, a de facto standard for property graphs. Therefore, we expect that our system would be readily accessible to existing property graph users.},
	number = {3},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Byun, Jaewook and Woo, Sungpil and Kim, Daeyoung},
	month = mar,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Knowledge and Data Engineering},
	keywords = {graph theory, Semantics, ChronoGraph, Databases, graph model, graph traversal language, Parallel processing, parallelism, point-based semantics, programming language semantics, Prototypes, Standards, static graph traversals, Syntactics, temporal aggregation, temporal breadth-first search, temporal degree, temporal depth-first search, temporal graph, temporal graph traversals, temporal networks, temporal paths, temporal single source shortest path, temporal support, temporal syntax, Time factors, Tinkerpop, traversal language, traversal-oriented system, tree searching},
	pages = {424--437},
	file = {IEEE Xplore Abstract Record:/home/wolf/Zotero/storage/PCGCTDJX/8606161.html:text/html;IEEE Xplore Full Text PDF:/home/wolf/Zotero/storage/6T2NPKPT/Byun et al. - 2020 - ChronoGraph Enabling Temporal Graph Traversals fo.pdf:application/pdf},
}

@article{chen_review_2020,
	title = {A review: {Knowledge} reasoning over knowledge graph},
	volume = {141},
	shorttitle = {A review},
	doi = {10.1016/j.eswa.2019.112948},
	abstract = {Mining valuable hidden knowledge from large-scale data relies on the support of reasoning technology. Knowledge graphs, as a new type of knowledge representation, have gained much attention in natural language processing. Knowledge graphs can effectively organize and represent knowledge so that it can be efficiently utilized in advanced applications. Recently, reasoning over knowledge graphs has become a hot research topic, since it can obtain new knowledge and conclusions from existing data. Herein we review the basic concept and definitions of knowledge reasoning and the methods for reasoning over knowledge graphs. Specifically, we dissect the reasoning methods into three categories: rule-based reasoning, distributed representation-based reasoning and neural network-based reasoning. We also review the related applications of knowledge graph reasoning, such as knowledge graph completion, question answering, and recommender systems. Finally, we discuss the remaining challenges and research opportunities for knowledge graph reasoning. © 2019 Elsevier Ltd},
	journal = {Expert Systems with Applications},
	author = {Chen, X. and Jia, S. and Xiang, Y.},
	year = {2020},
	keywords = {Distributed representation-based reasoning, Knowledge graph, Neural network-based reasoning, Reasoning, Rule-based reasoning},
	annote = {Cited By :2},
	file = {SCOPUS Snapshot:/home/wolf/Zotero/storage/IHHPGCZP/display.html:text/html;Chen et al. - 2020 - A review Knowledge reasoning over knowledge graph.pdf:/home/wolf/Zotero/storage/T4CDVCYP/Chen et al. - 2020 - A review Knowledge reasoning over knowledge graph.pdf:application/pdf},
}

@inproceedings{kertkeidkachorn_t2kg_2018,
	title = {{T2KG}: {A} demonstration of knowledge graph population from text and its challenges},
	volume = {2293},
	shorttitle = {{T2KG}},
	abstract = {Knowledge Graphs play an important role in many AI applications as prior knowledge. In recent years, there are many existing Knowledge Graphs such as DBpedia, Freebase, YAGO. Nevertheless, massive amounts of knowledge are being produced every day. Consequently, Knowledge Graphs become more obsolete over time. It is therefore necessary to populate new knowledge into Knowledge Graphs in order to keep them useable. In this study, we present our end-to-end system for populating knowledge graph from natural language text, namely T2KG. Also, we demonstrate use-cases, achievements, challenges, and lessons learned of the system in practice. © 2018 CEUR-WS. All Rights Reserved.},
	author = {Kertkeidkachorn, N. and Ichise, R.},
	year = {2018},
	pages = {110--113},
	file = {SCOPUS Snapshot:/home/wolf/Zotero/storage/SJUD6MD3/display.html:text/html;Kertkeidkachorn and Ichise - T2KG  A Demonstration of Knowledge Graph Populati.pdf:/home/wolf/Zotero/storage/VMMCIQF4/Kertkeidkachorn and Ichise - T2KG  A Demonstration of Knowledge Graph Populati.pdf:application/pdf},
}

@article{kertkeidkachorn_gtranse_2020,
	title = {{GTransE}: {Generalizing} {Translation}-{Based} {Model} on {Uncertain} {Knowledge} {Graph} {Embedding}},
	volume = {1128 AISC},
	shorttitle = {{GTransE}},
	doi = {10.1007/978-3-030-39878-1_16},
	abstract = {This is an extension from a selected paper from JSAI2019. Knowledge graphs are useful for many AI applications. Many recent studies have been focused on learning numerical representations of a knowledge graph in a low-dimensional vector space. Learning representations benefits the deep learning framework for encoding real-world knowledge. However, most of the studies do not consider uncertain knowledge graphs. Uncertain knowledge graphs, e.g., NELL, are valuable because they can express the likelihood of triples. In this study, we proposed a novel loss function for translation-based models, GTransE, to deal with uncertainty on knowledge graphs. Experimental results show that GTransE can robustly learn representations on uncertain knowledge graphs. © 2020, Springer Nature Switzerland AG.},
	journal = {Advances in Intelligent Systems and Computing},
	author = {Kertkeidkachorn, N. and Liu, X. and Ichise, R.},
	year = {2020},
	keywords = {Knowledge graph, Knowledge representation, Uncertainty},
	pages = {170--178},
	file = {SCOPUS Snapshot:/home/wolf/Zotero/storage/Q2M9X3E4/display.html:text/html;Kertkeidkachorn et al. - 2020 - GTransE Generalizing Translation-Based Model on U.pdf:/home/wolf/Zotero/storage/JXC44RB7/Kertkeidkachorn et al. - 2020 - GTransE Generalizing Translation-Based Model on U.pdf:application/pdf},
}

@article{kertkeidkachorn_t2kg_nodate,
	title = {{T2KG} : {A} {Demonstration} of {Knowledge} {Graph} {Population} from {Text} and {Its} {Challenges}},
	abstract = {Knowledge Graphs play an important role in many AI applications as prior knowledge. In recent years, there are many existing Knowledge Graphs such as DBpedia, Freebase, YAGO. Nevertheless, massive amounts of knowledge are being produced every day. Consequently, Knowledge Graphs become more obsolete over time. It is therefore necessary to populate new knowledge into Knowledge Graphs in order to keep them useable. In this study, we present our end-to-end system for populating knowledge graph from natural language text, namely T2KG. Also, we demonstrate use-cases, achievements, challenges, and lessons learned of the system in practice.},
	language = {en},
	author = {Kertkeidkachorn, Natthawut and Ichise, Ryutaro},
	pages = {4},
}

@inproceedings{mehta_scalable_2019,
	title = {Scalable knowledge graph construction over text using deep learning based predicate mapping},
	doi = {10.1145/3308560.3317708},
	abstract = {Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work. © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.},
	author = {Mehta, A. and Singhal, A. and Karlapalem, K.},
	year = {2019},
	keywords = {Knowledge Graph, Deep Learning, Predicate Mapping, Scalability, Sentence Simplification},
	pages = {705--713},
	annote = {Cited By :1},
	file = {SCOPUS Snapshot:/home/wolf/Zotero/storage/64T8MVY3/display.html:text/html},
}

@article{trotzek_utilizing_2020,
	title = {Utilizing {Neural} {Networks} and {Linguistic} {Metadata} for {Early} {Detection} of {Depression} {Indications} in {Text} {Sequences}},
	volume = {32},
	issn = {1558-2191},
	doi = {10.1109/TKDE.2018.2885515},
	abstract = {Depression is ranked as the largest contributor to global disability and is also a major reason for suicide. Still, many individuals suffering from forms of depression are not treated for various reasons. Previous studies have shown that depression also has an effect on language usage and that many depressed individuals use social media platforms or the internet in general to get information or discuss their problems. This paper addresses the early detection of depression using machine learning models based on messages on a social platform. In particular, a convolutional neural network based on different word embeddings is evaluated and compared to a classification based on user-level linguistic metadata. An ensemble of both approaches is shown to achieve state-of-the-art results in a current early detection task. Furthermore, the currently popular ERDE score as metric for early detection systems is examined in detail and its drawbacks in the context of shared tasks are illustrated. A slightly modified metric is proposed and compared to the original score. Finally, a new word embedding was trained on a large corpus of the same domain as the described task and is evaluated as well.},
	number = {3},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Trotzek, Marcel and Koitka, Sven and Friedrich, Christoph M.},
	month = mar,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Knowledge and Data Engineering},
	keywords = {behavioural sciences computing, convolutional neural nets, convolutional neural network, depressed individuals, Depression, depression indication detection, early detection, early detection systems, early detection task, ERDE score, Europe, Internet, learning (artificial intelligence), linguistic metadata, Linguistics, Machine learning, machine learning models, meta data, Metadata, Natural language processing, shared tasks, social media platforms, Social network services, social networking (online), Task analysis, text sequences, user-level linguistic metadata, word embeddings},
	pages = {588--601},
	file = {IEEE Xplore Abstract Record:/home/wolf/Zotero/storage/GFBJYSYW/8580405.html:text/html;IEEE Xplore Full Text PDF:/home/wolf/Zotero/storage/DJUBBMLG/Trotzek et al. - 2020 - Utilizing Neural Networks and Linguistic Metadata .pdf:application/pdf},
}

@inproceedings{noauthor_struc2vec_nodate,
	title = {struc2vec},
	url = {https://arxiv.org/pdf/1704.03165.pdf},
}

@inproceedings{grover_node2vec_2016,
	address = {San Francisco, California, USA},
	series = {{KDD} '16},
	title = {node2vec: {Scalable} {Feature} {Learning} for {Networks}},
	isbn = {978-1-4503-4232-2},
	shorttitle = {node2vec},
	url = {https://doi.org/10.1145/2939672.2939754},
	doi = {10.1145/2939672.2939754},
	abstract = {Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations. We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.},
	urldate = {2020-04-08},
	booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
	publisher = {Association for Computing Machinery},
	author = {Grover, Aditya and Leskovec, Jure},
	month = aug,
	year = {2016},
	keywords = {feature learning, graph representations, information networks, node embeddings},
	pages = {855--864},
	file = {Full Text PDF:/home/wolf/Zotero/storage/X8MYYMZP/Grover and Leskovec - 2016 - node2vec Scalable Feature Learning for Networks.pdf:application/pdf},
}

@inproceedings{perozzi_deepwalk_2014,
	address = {New York, New York, USA},
	series = {{KDD} '14},
	title = {{DeepWalk}: online learning of social representations},
	isbn = {978-1-4503-2956-9},
	shorttitle = {{DeepWalk}},
	url = {https://doi.org/10.1145/2623330.2623732},
	doi = {10.1145/2623330.2623732},
	abstract = {We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide F1 scores up to 10\% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60\% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.},
	urldate = {2020-04-08},
	booktitle = {Proceedings of the 20th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining},
	publisher = {Association for Computing Machinery},
	author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
	month = aug,
	year = {2014},
	keywords = {deep learning, latent representations, learning with partial labels, network classification, online learning, social networks},
	pages = {701--710},
	file = {Full Text PDF:/home/wolf/Zotero/storage/PS3DVJIA/Perozzi et al. - 2014 - DeepWalk online learning of social representation.pdf:application/pdf},
}

@article{rossi_deep_2020,
	title = {Deep {Inductive} {Graph} {Representation} {Learning}},
	volume = {32},
	issn = {1558-2191},
	doi = {10.1109/TKDE.2018.2878247},
	abstract = {This paper presents a general inductive graph representation learning framework called \${\textbackslash}textDeepGL\$DeepGL for learning deep node and edge features that generalize across-networks. In particular, \${\textbackslash}textDeepGL\$DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a multi-layered hierarchical graph representation where each successive layer leverages the output from the previous layer to learn features of a higher-order. Contrary to previous work, \${\textbackslash}textDeepGL\$DeepGL learns relational functions (each representing a feature) that naturally generalize across-networks and are therefore useful for graph-based transfer learning tasks. Moreover, \${\textbackslash}textDeepGL\$DeepGL naturally supports attributed graphs, learns interpretable inductive graph representations, and is space-efficient (by learning sparse feature vectors). In addition, \${\textbackslash}textDeepGL\$DeepGL is expressive, flexible with many interchangeable components, efficient with a time complexity of \${\textbackslash}mathcal O({\textbar}E{\textbar})\$O({\textbar}E{\textbar}), and scalable for large networks via an efficient parallel implementation. Compared with recent methods, \${\textbackslash}textDeepGL\$DeepGL is (1) effective for across-network transfer learning tasks and large (attributed) graphs, (2) space-efficient requiring up to 6x less memory, (3) fast with up to 106x speedup in runtime performance, and (4) accurate with an average improvement in AUC of 20 percent or more on many learning tasks and across a wide variety of networks.},
	number = {3},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Rossi, Ryan A. and Zhou, Rong and Ahmed, Nesreen K.},
	month = mar,
	year = {2020},
	note = {Conference Name: IEEE Transactions on Knowledge and Data Engineering},
	keywords = {graph theory, learning (artificial intelligence), Natural language processing, Task analysis, attributed graphs, deep inductive graph representation learning, DeepGL, Electronic mail, graph based transfer learning tasks, Graph representation learning, graph-based feature learning, graphlet features, higher-order structures, inductive representation learning, interpretable inductive graph representations, multilayered hierarchical graph representation, Orbits, relational function learning, Runtime, sparse feature vectors, transfer learning},
	pages = {438--452},
	file = {IEEE Xplore Abstract Record:/home/wolf/Zotero/storage/E2I4CVZ7/8519335.html:text/html;IEEE Xplore Full Text PDF:/home/wolf/Zotero/storage/NRM7T4Q6/Rossi et al. - 2020 - Deep Inductive Graph Representation Learning.pdf:application/pdf},
}

@article{kilgarriff_review_2000,
	title = {Review of {WordNet}: {An} {Electronic} {Lexical} {Database}},
	volume = {76},
	issn = {0097-8507},
	shorttitle = {Review of {WordNet}},
	url = {https://www.jstor.org/stable/417141},
	doi = {10.2307/417141},
	number = {3},
	urldate = {2020-04-07},
	journal = {Language},
	author = {Kilgarriff, Adam},
	collaborator = {Fellbaum, Christiane},
	year = {2000},
	note = {Publisher: Linguistic Society of America},
	pages = {706--708},
	file = {Submitted Version:/home/wolf/Zotero/storage/2C2FSA9P/Kilgarriff - 2000 - Review of WordNet An Electronic Lexical Database.pdf:application/pdf},
}

@article{kertkeidkachorn_automatic_2018,
	title = {An {Automatic} {Knowledge} {Graph} {Creation} {Framework} from {Natural} {Language} {Text}},
	volume = {E101.D},
	issn = {0916-8532, 1745-1361},
	url = {https://www.jstage.jst.go.jp/article/transinf/E101.D/1/E101.D_2017SWP0006/_article},
	doi = {10.1587/transinf.2017SWP0006},
	abstract = {Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50\% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.},
	language = {en},
	number = {1},
	urldate = {2020-04-06},
	journal = {IEICE Transactions on Information and Systems},
	author = {Kertkeidkachorn, Natthawut and Ichise, Ryutaro},
	year = {2018},
	pages = {90--98},
	file = {Kertkeidkachorn and Ichise - 2018 - An Automatic Knowledge Graph Creation Framework fr.pdf:/home/wolf/Zotero/storage/WP9AAHMA/Kertkeidkachorn and Ichise - 2018 - An Automatic Knowledge Graph Creation Framework fr.pdf:application/pdf},
}

@article{kertkeidkachorn_automatic_2018-1,
	title = {An {Automatic} {Knowledge} {Graph} {Creation} {Framework} from {Natural} {Language} {Text}},
	volume = {E101-D},
	issn = {1745-1361, 0916-8532},
	url = {https://search.ieice.org/bin/summary.php?id=e101-d_1_90&category=D&year=2018&lang=E&abst=},
	abstract = {Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50\% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.},
	number = {1},
	urldate = {2020-04-06},
	journal = {IEICE TRANSACTIONS on Information and Systems},
	author = {Kertkeidkachorn, Natthawut and Ichise, Ryutaro},
	month = jan,
	year = {2018},
	note = {Publisher: The Institute of Electronics, Information and Communication Engineers},
	pages = {90--98},
	file = {Snapshot:/home/wolf/Zotero/storage/VTTW8LL4/summary.html:text/html},
}

@article{kertkeidkachorn_automatic_2018-2,
	title = {An {Automatic} {Knowledge} {Graph} {Creation} {Framework} from {Natural} {Language} {Text}},
	volume = {E101.D},
	issn = {0916-8532, 1745-1361},
	url = {https://www.jstage.jst.go.jp/article/transinf/E101.D/1/E101.D_2017SWP0006/_article},
	doi = {10.1587/transinf.2017SWP0006},
	abstract = {Knowledge graphs (KG) play a crucial role in many modern applications. However, constructing a KG from natural language text is challenging due to the complex structure of the text. Recently, many approaches have been proposed to transform natural language text to triples to obtain KGs. Such approaches have not yet provided efficient results for mapping extracted elements of triples, especially the predicate, to their equivalent elements in a KG. Predicate mapping is essential because it can reduce the heterogeneity of the data and increase the searchability over a KG. In this article, we propose T2KG, an automatic KG creation framework for natural language text, to more effectively map natural language text to predicates. In our framework, a hybrid combination of a rule-based approach and a similarity-based approach is presented for mapping a predicate to its corresponding predicate in a KG. Based on experimental results, the hybrid approach can identify more similar predicate pairs than a baseline method in the predicate mapping task. An experiment on KG creation is also conducted to investigate the performance of the T2KG. The experimental results show that the T2KG also outperforms the baseline in KG creation. Although KG creation is conducted in open domains, in which prior knowledge is not provided, the T2KG still achieves an F1 score of approximately 50\% when generating triples in the KG creation task. In addition, an empirical study on knowledge population using various text sources is conducted, and the results indicate the T2KG could be used to obtain knowledge that is not currently available from DBpedia.},
	language = {en},
	number = {1},
	urldate = {2020-04-06},
	journal = {IEICE Transactions on Information and Systems},
	author = {Kertkeidkachorn, Natthawut and Ichise, Ryutaro},
	year = {2018},
	pages = {90--98},
	file = {Kertkeidkachorn and Ichise - 2018 - An Automatic Knowledge Graph Creation Framework fr.pdf:/home/wolf/Zotero/storage/96LRXMRC/Kertkeidkachorn and Ichise - 2018 - An Automatic Knowledge Graph Creation Framework fr.pdf:application/pdf},
}

@incollection{you_graph_2018,
	title = {Graph {Convolutional} {Policy} {Network} for {Goal}-{Directed} {Molecular} {Graph} {Generation}},
	url = {http://papers.nips.cc/paper/7877-graph-convolutional-policy-network-for-goal-directed-molecular-graph-generation.pdf},
	urldate = {2020-03-27},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 31},
	publisher = {Curran Associates, Inc.},
	author = {You, Jiaxuan and Liu, Bowen and Ying, Zhitao and Pande, Vijay and Leskovec, Jure},
	editor = {Bengio, S. and Wallach, H. and Larochelle, H. and Grauman, K. and Cesa-Bianchi, N. and Garnett, R.},
	year = {2018},
	pages = {6410--6421},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/IU79DNYN/7877-graph-convolutional-policy-network-for-goal-directed-molecular-graph-generation.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/GPW9RRLY/You et al. - 2018 - Graph Convolutional Policy Network for Goal-Direct.pdf:application/pdf},
}

@inproceedings{svetlik_automatic_2017,
	title = {Automatic {Curriculum} {Graph} {Generation} for {Reinforcement} {Learning} {Agents}},
	url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14961},
	abstract = {In recent years, research has shown that transfer learning methods can be leveraged to construct curricula that sequence a series of simpler tasks such that performance on a final target task is improved. A major limitation of existing approaches is that such curricula are handcrafted by humans that are typically domain experts. To address this limitation, we introduce a method to generate a curriculum based on task descriptors and a novel metric of transfer potential. Our method automatically generates a  curriculum as a directed acyclic graph (as opposed to a linear sequence as done in existing work). Experiments in both discrete and continuous domains show that our method produces curricula that improve the agent's learning performance when compared to the baseline condition of learning on the target task from scratch.},
	language = {en},
	urldate = {2020-03-27},
	booktitle = {Thirty-{First} {AAAI} {Conference} on {Artificial} {Intelligence}},
	author = {Svetlik, Maxwell and Leonetti, Matteo and Sinapov, Jivko and Shah, Rishi and Walker, Nick and Stone, Peter},
	month = feb,
	year = {2017},
	file = {Snapshot:/home/wolf/Zotero/storage/WRTKNPRX/14961.html:text/html;Full Text PDF:/home/wolf/Zotero/storage/HNTXER68/Svetlik et al. - 2017 - Automatic Curriculum Graph Generation for Reinforc.pdf:application/pdf},
}

@article{zhang_deep_2020,
	title = {Deep {Learning} on {Graphs}: {A} {Survey}},
	shorttitle = {Deep {Learning} on {Graphs}},
	url = {http://arxiv.org/abs/1812.04202},
	abstract = {Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.},
	urldate = {2020-03-27},
	journal = {arXiv:1812.04202 [cs, stat]},
	author = {Zhang, Ziwei and Cui, Peng and Zhu, Wenwu},
	month = mar,
	year = {2020},
	note = {arXiv: 1812.04202},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Social and Information Networks},
	annote = {Comment: Accepted by Transactions on Knowledge and Data Engineering. 24 pages, 11 figures},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/UVTXS9Z7/1812.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/WQ3NVAPL/Zhang et al. - 2020 - Deep Learning on Graphs A Survey.pdf:application/pdf},
}

@inproceedings{zhuang_dual_2018,
	address = {Lyon, France},
	series = {{WWW} '18},
	title = {Dual {Graph} {Convolutional} {Networks} for {Graph}-{Based} {Semi}-{Supervised} {Classification}},
	isbn = {978-1-4503-5639-8},
	url = {https://doi.org/10.1145/3178876.3186116},
	doi = {10.1145/3178876.3186116},
	abstract = {The problem of extracting meaningful data through graph analysis spans a range of different fields, such as the internet, social networks, biological networks, and many others. The importance of being able to effectively mine and learn from such data continues to grow as more and more structured data become available. In this paper, we present a simple and scalable semi-supervised learning method for graph-structured data in which only a very small portion of the training data are labeled. To sufficiently embed the graph knowledge, our method performs graph convolution from different views of the raw data. In particular, a dual graph convolutional neural network method is devised to jointly consider the two essential assumptions of semi-supervised learning: (1) local consistency and (2) global consistency. Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively. Given the different data transformations from the two networks, we then introduce an unsupervised temporal loss function for the ensemble. In experiments using both unsupervised and supervised loss functions, our method outperforms state-of-the-art techniques on different datasets.},
	urldate = {2020-03-27},
	booktitle = {Proceedings of the 2018 {World} {Wide} {Web} {Conference}},
	publisher = {International World Wide Web Conferences Steering Committee},
	author = {Zhuang, Chenyi and Ma, Qiang},
	month = apr,
	year = {2018},
	keywords = {adjacency matrix, graph convolutional networks, graph diffusion, pointwise mutual information, semi-supervised learning},
	pages = {499--508},
	file = {Submitted Version:/home/wolf/Zotero/storage/2YVUNFFD/Zhuang and Ma - 2018 - Dual Graph Convolutional Networks for Graph-Based .pdf:application/pdf},
}

@article{wei_eda_2019,
	title = {{EDA}: {Easy} {Data} {Augmentation} {Techniques} for {Boosting} {Performance} on {Text} {Classification} {Tasks}},
	shorttitle = {{EDA}},
	url = {http://arxiv.org/abs/1901.11196},
	abstract = {We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50\% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.},
	urldate = {2020-02-19},
	journal = {arXiv:1901.11196 [cs]},
	author = {Wei, Jason and Zou, Kai},
	month = aug,
	year = {2019},
	note = {arXiv: 1901.11196},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: EMNLP-IJCNLP 2019 short paper},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/6827NNJE/1901.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/Z7CGKHNG/Wei and Zou - 2019 - EDA Easy Data Augmentation Techniques for Boostin.pdf:application/pdf},
}

@inproceedings{heinerman_evolution_2015,
	title = {Evolution, {Individual} {Learning}, and {Social} {Learning} in a {Swarm} of {Real} {Robots}},
	doi = {10.1109/SSCI.2015.152},
	abstract = {We investigate a novel adaptive system based on evolution, individual learning, and social learning in a swarm of physical Thymio II robots. The system is based on distinguishing inheritable and learnable features in the robots and defining appropriate operators for both categories. In this study we choose to make the sensory layout of the robots inheritable, thus evolvable, and the robot controllers learnable. We run tests with a basic system that employs only evolution and individual learning and compare this with an extended system where robots can disseminate their learned controllers. Results show that social learning increases the learning speed and leads to better controllers.},
	booktitle = {2015 {IEEE} {Symposium} {Series} on {Computational} {Intelligence}},
	author = {Heinerman, Jacqueline and Rango, Massimiliano and Eiben, A.E.},
	month = dec,
	year = {2015},
	note = {ISSN: null},
	keywords = {learning (artificial intelligence), adaptive system, Bioinformatics, Collision avoidance, evolutionary computation, Genomics, individual learning, Layout, mobile robots, multi-robot systems, physical Thymio II robots, robot controllers, Robot sensing systems, sensory layout, social learning, swarm},
	pages = {1055--1062},
	file = {IEEE Xplore Full Text PDF:/home/wolf/Zotero/storage/3ADCLDVQ/Heinerman et al. - 2015 - Evolution, Individual Learning, and Social Learnin.pdf:application/pdf},
}

@article{heinerman_importance_2019,
	title = {Importance of {Parameter} {Settings} on the {Benefits} of {Robot}-to-{Robot} {Learning} in {Evolutionary} {Robotics}},
	volume = {6},
	issn = {2296-9144},
	url = {https://www.frontiersin.org/articles/10.3389/frobt.2019.00010/full},
	doi = {10.3389/frobt.2019.00010},
	abstract = {Robot-to-robot learning, a specific case of social learning in robotics, enables multiple robots to share learned skills while completing a task. The literature offers various statements of its benefits. Robots using this type of social learning can reach a higher performance, an increased learning speed, or both, compared to robots using individual learning only. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this paper, we perform a detailed analysis into the effects of robot-to-robot learning. As a result, we show that this type of social learning can reduce the sensitivity of the learning process to the choice of parameters in two ways. First, robot-to-robot learning can reduce the number of bad performing individuals in the population. Second, robot-to-robot learning can increase the chance of having a successful run, where success is defined as the presence of a high performing individual. Additionally, we show that robot-to-robot learning results in an increased learning speed for almost all parameter settings. Our results indicate that robot-to-robot learning is a powerful mechanism which leads to benefits in both performance and learning speed.},
	language = {English},
	urldate = {2019-12-30},
	journal = {Frontiers in Robotics and AI},
	author = {Heinerman, Jacqueline and Haasdijk, Evert and Eiben, A. E.},
	year = {2019},
	keywords = {evolutionary algorithm, Evolutionary Robotics, parameter tuning, Robot-to-robot learning, Social learning},
	file = {Full Text PDF:/home/wolf/Zotero/storage/DLW2RUA4/Heinerman et al. - 2019 - Importance of Parameter Settings on the Benefits o.pdf:application/pdf},
}

@misc{noauthor_unsupervised_nodate,
	title = {Unsupervised identification and recognition of situations for high-dimensional sensori-motor streams {\textbar} {Elsevier} {Enhanced} {Reader}},
	url = {https://reader.elsevier.com/reader/sd/pii/S0925231217309840?token=FA834DAEFF1933B4D5FC4F184CF41CDFB96C90399B57F8F9B3E29C5D738EDBFE655A91B4654AFD5BC5241CFC3188B930},
	language = {en},
	urldate = {2019-12-30},
	doi = {10.1016/j.neucom.2017.02.090},
	file = {Snapshot:/home/wolf/Zotero/storage/9VVGVM4H/S0925231217309840.html:text/html},
}

@inproceedings{ratner_snorkel_2018,
	address = {Houston, TX, USA},
	title = {Snorkel {MeTaL}: {Weak} {Supervision} for {Multi}-{Task} {Learning}},
	isbn = {978-1-4503-5828-6},
	shorttitle = {Snorkel {MeTaL}},
	url = {http://dl.acm.org/citation.cfm?doid=3209889.3209898},
	doi = {10.1145/3209889.3209898},
	abstract = {Many real-world machine learning problems are challenging to tackle for two reasons: (i) they involve multiple sub-tasks at different levels of granularity; and (ii) they require large volumes of labeled training data. We propose Snorkel MeTaL, an end-toend system for multi-task learning that leverages weak supervision provided at multiple levels of granularity by domain expert users. In MeTaL, a user specifies a problem consisting of multiple, hierarchically-related sub-tasks—for example, classifying a document at multiple levels of granularity—and then provides labeling functions for each sub-task as weak supervision. MeTaL learns a re-weighted model of these labeling functions, and uses the combined signal to train a hierarchical multi-task network which is automatically compiled from the structure of the sub-tasks. Using MeTaL on a radiology report triage task and a fine-grained news classification task, we achieve average gains of 11.2 accuracy points over a baseline supervised approach and 9.5 accuracy points over the predictions of the user-provided labeling functions.},
	language = {en},
	urldate = {2019-12-27},
	booktitle = {Proceedings of the {Second} {Workshop} on {Data} {Management} for {End}-{To}-{End} {Machine} {Learning} - {DEEM}'18},
	publisher = {ACM Press},
	author = {Ratner, Alex and Hancock, Braden and Dunnmon, Jared and Goldman, Roger and Ré, Christopher},
	year = {2018},
	pages = {1--4},
	file = {Ratner et al. - 2018 - Snorkel MeTaL Weak Supervision for Multi-Task Lea.pdf:/home/wolf/Zotero/storage/DFCMGMK7/Ratner et al. - 2018 - Snorkel MeTaL Weak Supervision for Multi-Task Lea.pdf:application/pdf},
}

@article{joulin_fasttext.zip:_2016,
	title = {{FastText}.zip: {Compressing} text classification models},
	shorttitle = {{FastText}.zip},
	url = {http://arxiv.org/abs/1612.03651},
	abstract = {We consider the problem of producing compact architectures for text classification, such that the full model fits in a limited amount of memory. After considering different solutions inspired by the hashing literature, we propose a method built upon product quantization to store word embeddings. While the original technique leads to a loss in accuracy, we adapt this method to circumvent quantization artefacts. Our experiments carried out on several benchmarks show that our approach typically requires two orders of magnitude less memory than fastText while being only slightly inferior with respect to accuracy. As a result, it outperforms the state of the art by a good margin in terms of the compromise between memory usage and accuracy.},
	urldate = {2019-12-23},
	journal = {arXiv:1612.03651 [cs]},
	author = {Joulin, Armand and Grave, Edouard and Bojanowski, Piotr and Douze, Matthijs and Jégou, Hérve and Mikolov, Tomas},
	month = dec,
	year = {2016},
	note = {arXiv: 1612.03651},
	keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language},
	annote = {Comment: Submitted to ICLR 2017},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/P67INB7F/1612.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/JSHEYQQ9/Joulin et al. - 2016 - FastText.zip Compressing text classification mode.pdf:application/pdf},
}

@article{higgins_beta-vae_2016,
	title = {beta-{VAE}: {Learning} {Basic} {Visual} {Concepts} with a {Constrained} {Variational} {Framework}},
	shorttitle = {beta-{VAE}},
	url = {https://openreview.net/forum?id=Sy2fzU9gl},
	abstract = {We introduce beta-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner.},
	language = {en},
	urldate = {2021-01-02},
	author = {Higgins, Irina and Matthey, Loic and Pal, Arka and Burgess, Christopher and Glorot, Xavier and Botvinick, Matthew and Mohamed, Shakir and Lerchner, Alexander},
	month = nov,
	year = {2016},
	file = {Snapshot:/home/wolf/Zotero/storage/7X3MUUFS/forum.html:text/html;Full Text PDF:/home/wolf/Zotero/storage/VEBUUZTQ/Higgins et al. - 2016 - beta-VAE Learning Basic Visual Concepts with a Co.pdf:application/pdf},
}

@article{paszke_pytorch_2019,
	title = {{PyTorch}: {An} {Imperative} {Style}, {High}-{Performance} {Deep} {Learning} {Library}},
	shorttitle = {{PyTorch}},
	url = {http://arxiv.org/abs/1912.01703},
	abstract = {Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.},
	urldate = {2021-01-02},
	journal = {arXiv:1912.01703 [cs, stat]},
	author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Köpf, Andreas and Yang, Edward and DeVito, Zach and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
	month = dec,
	year = {2019},
	note = {arXiv: 1912.01703},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Mathematical Software},
	annote = {Comment: 12 pages, 3 figures, NeurIPS 2019},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/CLTGJQQA/1912.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/8E2CLU5P/Paszke et al. - 2019 - PyTorch An Imperative Style, High-Performance Dee.pdf:application/pdf},
}

@online{nickel_three-way_nodate,
	title = {A {Three}-{Way} {Model} for {Collective} {Learning} on {Multi}-{Relational} {Data}},
	abstract = {Relational learning is becoming increasingly important in many areas of application. Here, we present a novel approach to relational learning based on the factorization of a three-way tensor. We show that unlike other tensor approaches, our method is able to perform collective learning via the latent components of the model and provide an efficient algorithm to compute the factorization. We substantiate our theoretical considerations regarding the collective learning capabilities of our model by the means of experiments on both a new dataset and a dataset commonly used in entity resolution. Furthermore, we show on common benchmark datasets that our approach achieves better or on-par results, if compared to current state-of-the-art relational learning solutions, while it is significantly faster to compute.},
	language = {en},
	author = {Nickel, Maximilian and Tresp, Volker and Kriegel, Hans-Peter},
	pages = {8},
	file = {Nickel et al. - A Three-Way Model for Collective Learning on Multi.pdf:/home/wolf/Zotero/storage/VKHTK2Y9/Nickel et al. - A Three-Way Model for Collective Learning on Multi.pdf:application/pdf},
}

@article{mills-tettey_dynamic_nodate,
	title = {The {Dynamic} {Hungarian} {Algorithm} for the {Assignment} {Problem} with {Changing} {Costs}},
	language = {en},
	author = {Mills-Tettey, G Ayorkor and Stentz, Anthony and Dias, M Bernardine},
	pages = {19},
	year = {2007},
	file = {Mills-Tettey et al. - The Dynamic Hungarian Algorithm for the Assignment.pdf:/home/wolf/Zotero/storage/EC48KYH8/Mills-Tettey et al. - The Dynamic Hungarian Algorithm for the Assignment.pdf:application/pdf},
}

@misc{mills-tettey_dynamic_2007,
	title = {The {Dynamic} {Hungarian} {Algorithm} for the {Assignment} {Problem} with {Changing} {Costs}},
	url = {/paper/The-Dynamic-Hungarian-Algorithm-for-the-Assignment-Mills-Tettey-Stentz/23a53ffca21c4d4aebd085fc426a7a68137bcf90},
	abstract = {In this paper, we present the dynamic Hungarian algorithm, applicable to optimally solving the assignment problem in situations with changing edge costs or weights. This problem is relevant, for example, in a transportation domain where the unexpected closing of a road translates to changed transportation costs. When such cost changes occur after an initial assignment has been made, the new problem, like the original problem, may be solved from scratch using the well-known Hungarian algorithm. However, the dynamic version of the algorithm which we present solves the new problem more efficiently by repairing the initial solution obtained before the cost changes. We present proofs of the correctness and efficiency of our algorithm and present simulation results illustrating its efficiency.},
	language = {en},
	urldate = {2020-12-27},
	journal = {undefined},
	author = {Mills-Tettey, G. A. and Stentz, Anthony and Dias, M.},
	year = {2007},
	file = {Snapshot:/home/wolf/Zotero/storage/CVVAKYUA/23a53ffca21c4d4aebd085fc426a7a68137bcf90.html:text/html},
}

@book{chung1997spectral,
  title={Spectral graph theory},
  author={Chung, Fan RK and Graham, Fan Chung},
  number={92},
  year={1997},
  publisher={American Mathematical Soc.}
}

@article{yong_gradient_2020,
	title = {Gradient {Centralization}: {A} {New} {Optimization} {Technique} for {Deep} {Neural} {Networks}},
	shorttitle = {Gradient {Centralization}},
	url = {http://arxiv.org/abs/2004.01461},
	abstract = {Optimization techniques are of great importance to effectively and efficiently train a deep neural network (DNN). It has been shown that using the first and second order statistics (e.g., mean and variance) to perform Z-score standardization on network activations or weight vectors, such as batch normalization (BN) and weight standardization (WS), can improve the training performance. Different from these existing methods that mostly operate on activations or weights, we present a new optimization technique, namely gradient centralization (GC), which operates directly on gradients by centralizing the gradient vectors to have zero mean. GC can be viewed as a projected gradient descent method with a constrained loss function. We show that GC can regularize both the weight space and output feature space so that it can boost the generalization performance of DNNs. Moreover, GC improves the Lipschitzness of the loss function and its gradient so that the training process becomes more efficient and stable. GC is very simple to implement and can be easily embedded into existing gradient based DNN optimizers with only one line of code. It can also be directly used to fine-tune the pre-trained DNNs. Our experiments on various applications, including general image classification, fine-grained image classification, detection and segmentation, demonstrate that GC can consistently improve the performance of DNN learning. The code of GC can be found at https://github.com/Yonghongwei/Gradient-Centralization.},
	urldate = {2020-12-24},
	journal = {arXiv:2004.01461 [cs]},
	author = {Yong, Hongwei and Huang, Jianqiang and Hua, Xiansheng and Zhang, Lei},
	month = apr,
	year = {2020},
	note = {arXiv: 2004.01461},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: 20 pages, 7 figures, conference},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/FDVVQHVS/2004.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/GLC88XY6/Yong et al. - 2020 - Gradient Centralization A New Optimization Techni.pdf:application/pdf},
}

@article{paulheim_knowledge_2016,
	title = {Knowledge graph refinement: {A} survey of approaches and evaluation methods},
	volume = {8},
	issn = {22104968, 15700844},
	shorttitle = {Knowledge graph refinement},
	url = {https://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/SW-160218},
	doi = {10.3233/SW-160218},
	abstract = {In the recent years, different Web knowledge graphs, both free and commercial, have been created. While Google coined the term “Knowledge Graph” in 2012, there are also a few openly available knowledge graphs, with DBpedia, YAGO, and Freebase being among the most prominent ones. Those graphs are often constructed from semi-structured knowledge, such as Wikipedia, or harvested from the web with a combination of statistical and linguistic methods. The result are large-scale knowledge graphs that try to make a good trade-off between completeness and correctness. In order to further increase the utility of such knowledge graphs, various refinement methods have been proposed, which try to infer and add missing knowledge to the graph, or identify erroneous pieces of information. In this article, we provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used.},
	language = {en},
	number = {3},
	urldate = {2020-12-24},
	journal = {Semantic Web},
	author = {Paulheim, Heiko},
	editor = {Cimiano, Philipp},
	month = dec,
	year = {2016},
	pages = {489--508},
	file = {Paulheim - 2016 - Knowledge graph refinement A survey of approaches.pdf:/home/wolf/Zotero/storage/WXA6WR5W/Paulheim - 2016 - Knowledge graph refinement A survey of approaches.pdf:application/pdf},
}

@article{zhang_lookahead_2019,
	title = {Lookahead {Optimizer}: k steps forward, 1 step back},
	shorttitle = {Lookahead {Optimizer}},
	url = {http://arxiv.org/abs/1907.08610},
	abstract = {The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms. Recent attempts to improve SGD can be broadly categorized into two approaches: (1) adaptive learning rate schemes, such as AdaGrad and Adam, and (2) accelerated schemes, such as heavy-ball and Nesterov momentum. In this paper, we propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. Intuitively, the algorithm chooses a search direction by looking ahead at the sequence of fast weights generated by another optimizer. We show that Lookahead improves the learning stability and lowers the variance of its inner optimizer with negligible computation and memory cost. We empirically demonstrate Lookahead can significantly improve the performance of SGD and Adam, even with their default hyperparameter settings on ImageNet, CIFAR-10/100, neural machine translation, and Penn Treebank.},
	urldate = {2020-12-24},
	journal = {arXiv:1907.08610 [cs, stat]},
	author = {Zhang, Michael R. and Lucas, James and Hinton, Geoffrey and Ba, Jimmy},
	month = jul,
	year = {2019},
	note = {arXiv: 1907.08610
version: 1},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing},
	annote = {Comment: Accepted to Neural Information Processing Systems 2019. Code available at: https://github.com/michaelrzhang/lookahead},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/92MZWPIM/1907.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/V2E575NK/Zhang et al. - 2019 - Lookahead Optimizer k steps forward, 1 step back.pdf:application/pdf},
}

@article{liu_variance_2020,
	title = {On the {Variance} of the {Adaptive} {Learning} {Rate} and {Beyond}},
	url = {http://arxiv.org/abs/1908.03265},
	abstract = {The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam. Here, we study its mechanism in details. Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis. We further propose RAdam, a new variant of Adam, by introducing a term to rectify the variance of the adaptive learning rate. Extensive experimental results on image classification, language modeling, and neural machine translation verify our intuition and demonstrate the effectiveness and robustness of our proposed method. All implementations are available at: https://github.com/LiyuanLucasLiu/RAdam.},
	urldate = {2020-12-24},
	journal = {arXiv:1908.03265 [cs, stat]},
	author = {Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
	month = apr,
	year = {2020},
	note = {arXiv: 1908.03265},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computation and Language},
	annote = {Comment: ICLR 2020. Fix several typos in the previous version},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/AQUQBQVE/1908.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/PDKA3GF8/Liu et al. - 2020 - On the Variance of the Adaptive Learning Rate and .pdf:application/pdf},
}

@article{nguyen_survey_2020,
	title = {A survey of embedding models of entities and relationships for knowledge graph completion},
	url = {http://arxiv.org/abs/1703.08098},
	abstract = {Knowledge graphs (KGs) of real-world facts about entities and their relationships are useful resources for a variety of natural language processing tasks. However, because knowledge graphs are typically incomplete, it is useful to perform knowledge graph completion or link prediction, i.e. predict whether a relationship not in the knowledge graph is likely to be true. This paper serves as a comprehensive survey of embedding models of entities and relationships for knowledge graph completion, summarizing up-to-date experimental results on standard benchmark datasets and pointing out potential future research directions.},
	urldate = {2020-12-24},
	journal = {arXiv:1703.08098 [cs]},
	author = {Nguyen, Dat Quoc},
	month = oct,
	year = {2020},
	note = {arXiv: 1703.08098},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Information Retrieval},
	annote = {Comment: In Proceedings of the 14th Workshop on Graph-Based Natural Language Processing (TextGraphs 2020); 16 pages, 2 figures, 6 tables},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/3CVD5S4W/1703.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/IDKD8TQJ/Nguyen - 2020 - A survey of embedding models of entities and relat.pdf:application/pdf},
}

@article{nickel_review_2016,
	title = {A {Review} of {Relational} {Machine} {Learning} for {Knowledge} {Graphs}},
	volume = {104},
	issn = {0018-9219, 1558-2256},
	url = {http://arxiv.org/abs/1503.00759},
	doi = {10.1109/JPROC.2015.2483592},
	abstract = {Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's Knowledge Vault project as an example of such combination.},
	number = {1},
	urldate = {2020-12-24},
	journal = {Proceedings of the IEEE},
	author = {Nickel, Maximilian and Murphy, Kevin and Tresp, Volker and Gabrilovich, Evgeniy},
	month = jan,
	year = {2016},
	note = {arXiv: 1503.00759},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {11--33},
	annote = {Comment: To appear in Proceedings of the IEEE},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/77TE68FB/1503.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/GSGA8N7P/Nickel et al. - 2016 - A Review of Relational Machine Learning for Knowle.pdf:application/pdf},
}

@article{tong_calibrating_2019,
	title = {Calibrating the {Adaptive} {Learning} {Rate} to {Improve} {Convergence} of {ADAM}},
	url = {http://arxiv.org/abs/1908.00700},
	abstract = {Adaptive gradient methods (AGMs) have become popular in optimizing the nonconvex problems in deep learning area. We revisit AGMs and identify that the adaptive learning rate (A-LR) used by AGMs varies significantly across the dimensions of the problem over epochs (i.e., anisotropic scale), which may lead to issues in convergence and generalization. All existing modified AGMs actually represent efforts in revising the A-LR. Theoretically, we provide a new way to analyze the convergence of AGMs and prove that the convergence rate of {\textbackslash}textsc\{Adam\} also depends on its hyper-parameter \${\textbackslash}epsilon\$, which has been overlooked previously. Based on these two facts, we propose a new AGM by calibrating the A-LR with an activation (\{{\textbackslash}em softplus\}) function, resulting in the {\textbackslash}textsc\{Sadam\} and {\textbackslash}textsc\{SAMSGrad\} methods {\textbackslash}footnote\{Code is available at https://github.com/neilliang90/Sadam.git.\}. We further prove that these algorithms enjoy better convergence speed under nonconvex, non-strongly convex, and Polyak-\{{\textbackslash}L\}ojasiewicz conditions compared with {\textbackslash}textsc\{Adam\}. Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks.},
	urldate = {2020-11-27},
	journal = {arXiv:1908.00700 [cs, math, stat]},
	author = {Tong, Qianqian and Liang, Guannan and Bi, Jinbo},
	month = sep,
	year = {2019},
	note = {arXiv: 1908.00700
version: 2},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Mathematics - Optimization and Control},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/ZPJI32AH/1908.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/EVJQCVCC/Tong et al. - 2019 - Calibrating the Adaptive Learning Rate to Improve .pdf:application/pdf},
}

@inproceedings{toutanova_representing_2015,
	address = {Lisbon, Portugal},
	title = {Representing {Text} for {Joint} {Embedding} of {Text} and {Knowledge} {Bases}},
	url = {https://www.aclweb.org/anthology/D15-1174},
	doi = {10.18653/v1/D15-1174},
	urldate = {2020-10-22},
	booktitle = {Proceedings of the 2015 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Toutanova, Kristina and Chen, Danqi and Pantel, Patrick and Poon, Hoifung and Choudhury, Pallavi and Gamon, Michael},
	month = sep,
	year = {2015},
	pages = {1499--1509},
	file = {Full Text PDF:/home/wolf/Zotero/storage/Z3RFNDLV/Toutanova et al. - 2015 - Representing Text for Joint Embedding of Text and .pdf:application/pdf},
}

@incollection{bordes_translating_2013,
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	url = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf},
	urldate = {2020-10-22},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 26},
	publisher = {Curran Associates, Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
	editor = {Burges, C. J. C. and Bottou, L. and Welling, M. and Ghahramani, Z. and Weinberger, K. Q.},
	year = {2013},
	pages = {2787--2795},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/2UDIEXWE/5071-translating-embeddings-for-modeling-multi-relational-data.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/DASKLYKA/Bordes et al. - 2013 - Translating Embeddings for Modeling Multi-relation.pdf:application/pdf},
}

@incollection{liu_stein_2016,
	title = {Stein {Variational} {Gradient} {Descent}: {A} {General} {Purpose} {Bayesian} {Inference} {Algorithm}},
	shorttitle = {Stein {Variational} {Gradient} {Descent}},
	url = {http://papers.nips.cc/paper/6338-stein-variational-gradient-descent-a-general-purpose-bayesian-inference-algorithm.pdf},
	urldate = {2020-09-30},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 29},
	publisher = {Curran Associates, Inc.},
	author = {Liu, Qiang and Wang, Dilin},
	editor = {Lee, D. D. and Sugiyama, M. and Luxburg, U. V. and Guyon, I. and Garnett, R.},
	year = {2016},
	pages = {2378--2386},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/CTRLGD5A/6338-stein-variational-gradient-descent-a-general-purpose-bayesian-inference-algorithm.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/JQWBY7N2/Liu and Wang - 2016 - Stein Variational Gradient Descent A General Purp.pdf:application/pdf},
}

@article{date_gpu-accelerated_2016,
	title = {{GPU}-accelerated {Hungarian} algorithms for the {Linear} {Assignment} {Problem}},
	volume = {57},
	issn = {0167-8191},
	url = {http://www.sciencedirect.com/science/article/pii/S016781911630045X},
	doi = {10.1016/j.parco.2016.05.012},
	abstract = {In this paper, we describe parallel versions of two different variants (classical and alternating tree) of the Hungarian algorithm for solving the Linear Assignment Problem (LAP). We have chosen Compute Unified Device Architecture (CUDA) enabled NVIDIA Graphics Processing Units (GPU) as the parallel programming architecture because of its ability to perform intense computations on arrays and matrices. The main contribution of this paper is an efficient parallelization of the augmenting path search phase of the Hungarian algorithm. Computational experiments on problems with up to 25 million variables reveal that the GPU-accelerated versions are extremely efficient in solving large problems, as compared to their CPU counterparts. Tremendous parallel speedups are achieved for problems with up to 400 million variables, which are solved within 13 seconds on average. We also tested multi-GPU versions of the two variants on up to 16 GPUs, which show decent scaling behavior for problems with up to 1.6 billion variables and dense cost matrix structure.},
	language = {en},
	urldate = {2020-09-27},
	journal = {Parallel Computing},
	author = {Date, Ketan and Nagi, Rakesh},
	month = sep,
	year = {2016},
	keywords = {CUDA, Graphics processing unit, Linear assignment problem, Parallel algorithm},
	pages = {52--72},
	file = {ScienceDirect Snapshot:/home/wolf/Zotero/storage/AALTXAAE/S016781911630045X.html:text/html},
}

@misc{bishop_pattern_2006,
	title = {Pattern recognition and machine learning},
	url = {https://cds.cern.ch/record/998831},
	abstract = {This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.},
	language = {en},
	urldate = {2020-09-25},
	journal = {CERN Document Server},
	author = {Bishop, Christopher M.},
	year = {2006},
	note = {ISBN: 9781493938438 9780387310732
Publisher: Springer},
	file = {Snapshot:/home/wolf/Zotero/storage/PY8N6H9M/998831.html:text/html;Full Text PDF:/home/wolf/Zotero/storage/T8TZRCQB/Bishop - 2006 - Pattern recognition and machine learning.pdf:application/pdf},
}

@inproceedings{de_boer_supporting_2012,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Supporting {Linked} {Data} {Production} for {Cultural} {Heritage} {Institutes}: {The} {Amsterdam} {Museum} {Case} {Study}},
	isbn = {978-3-642-30284-8},
	shorttitle = {Supporting {Linked} {Data} {Production} for {Cultural} {Heritage} {Institutes}},
	doi = {10.1007/978-3-642-30284-8_56},
	abstract = {Within the cultural heritage field, proprietary metadata and vocabularies are being transformed into public Linked Data. These efforts have mostly been at the level of large-scale aggregators such as Europeana where the original data is abstracted to a common format and schema. Although this approach ensures a level of consistency and interoperability, the richness of the original data is lost in the process. In this paper, we present a transparent and interactive methodology for ingesting, converting and linking cultural heritage metadata into Linked Data. The methodology is designed to maintain the richness and detail of the original metadata. We introduce the XMLRDF conversion tool and describe how it is integrated in the ClioPatria semantic web toolkit. The methodology and the tools have been validated by converting the Amsterdam Museum metadata to a Linked Data version. In this way, the Amsterdam Museum became the first ‘small’ cultural heritage institution with a node in the Linked Data cloud.},
	language = {en},
	booktitle = {The {Semantic} {Web}: {Research} and {Applications}},
	publisher = {Springer},
	author = {de Boer, Victor and Wielemaker, Jan and van Gent, Judith and Hildebrand, Michiel and Isaac, Antoine and van Ossenbruggen, Jacco and Schreiber, Guus},
	editor = {Simperl, Elena and Cimiano, Philipp and Polleres, Axel and Corcho, Oscar and Presutti, Valentina},
	year = {2012},
	keywords = {Cultural Heritage, Link Data, Link Open Data, Metadata Schema, Metadata Standard},
	pages = {733--747},
	file = {Springer Full Text PDF:/home/wolf/Zotero/storage/7QRTBCT5/de Boer et al. - 2012 - Supporting Linked Data Production for Cultural Her.pdf:application/pdf},
}

@inproceedings{de_vries_fast_2013,
	address = {Berlin, Heidelberg},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {A {Fast} {Approximation} of the {Weisfeiler}-{Lehman} {Graph} {Kernel} for {RDF} {Data}},
	isbn = {978-3-642-40988-2},
	doi = {10.1007/978-3-642-40988-2_39},
	abstract = {In this paper we introduce an approximation of the Weisfeiler-Lehman graph kernel algorithm aimed at improving the computation time of the kernel when applied to Resource Description Framework (RDF) data. Typically, applying graph kernels to RDF is done by extracting subgraphs from a large RDF graph and computing the kernel on this set of subgraphs. In contrast, our algorithm computes the Weisfeiler-Lehman kernel directly on the large RDF graph, but still retains the subgraph information. We show that this algorithm is faster than the regular Weisfeiler-Lehman kernel for RDF data and has at least the same performance. Furthermore, we show that our method has similar or better performance, and is faster, than other recently introduced graph kernels for RDF.},
	language = {en},
	booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},
	publisher = {Springer},
	author = {de Vries, Gerben K. D.},
	editor = {Blockeel, Hendrik and Kersting, Kristian and Nijssen, Siegfried and Železný, Filip},
	year = {2013},
	keywords = {Graph Kernels, Resource Description Framework (RDF), Weisfeiler-Lehman},
	pages = {606--621},
	file = {Springer Full Text PDF:/home/wolf/Zotero/storage/4CRG9IR4/de Vries - 2013 - A Fast Approximation of the Weisfeiler-Lehman Grap.pdf:application/pdf},
}

@misc{debnath_structure-activity_2002,
	title = {Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. {Correlation} with molecular orbital energies and hydrophobicity},
	url = {https://pubs.acs.org/doi/pdf/10.1021/jm00106a046},
	language = {EN},
	urldate = {2020-09-25},
	author = {Debnath, Asim Kumar and Compadre, Rosa L. Lopez de and Debnath, Gargi and Shusterman, Alan J. and Hansch, Corwin},
	month = may,
	year = {2002},
	doi = {10.1021/jm00106a046},
	note = {Archive Location: world
Publisher: American Chemical Society},
}

@incollection{hutchison_kernel_2007,
	address = {Berlin, Heidelberg},
	title = {Kernel {Methods} for {Mining} {Instance} {Data} in {Ontologies}},
	volume = {4825},
	isbn = {978-3-540-76297-3 978-3-540-76298-0},
	url = {http://link.springer.com/10.1007/978-3-540-76298-0_5},
	abstract = {The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.},
	language = {en},
	urldate = {2020-09-25},
	booktitle = {The {Semantic} {Web}},
	publisher = {Springer Berlin Heidelberg},
	author = {Bloehdorn, Stephan and Sure, York},
	editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Mizoguchi, Riichiro and Schreiber, Guus and Cudré-Mauroux, Philippe},
	year = {2007},
	doi = {10.1007/978-3-540-76298-0_5},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {58--71},
	file = {Bloehdorn and Sure - 2007 - Kernel Methods for Mining Instance Data in Ontolog.pdf:/home/wolf/Zotero/storage/LV24SCJT/Bloehdorn and Sure - 2007 - Kernel Methods for Mining Instance Data in Ontolog.pdf:application/pdf},
}

@book{noauthor_notitle_nodate,
}

@article{gardner_allennlp_2018,
	title = {{AllenNLP}: {A} {Deep} {Semantic} {Natural} {Language} {Processing} {Platform}},
	shorttitle = {{AllenNLP}},
	url = {http://arxiv.org/abs/1803.07640},
	abstract = {This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding. AllenNLP is designed to support researchers who want to build novel language understanding models quickly and easily. It is built on top of PyTorch, allowing for dynamic computation graphs, and provides (1) a flexible data API that handles intelligent batching and padding, (2) high-level abstractions for common operations in working with text, and (3) a modular and extensible experiment framework that makes doing good science easy. It also includes reference implementations of high quality approaches for both core semantic problems (e.g. semantic role labeling (Palmer et al., 2005)) and language understanding applications (e.g. machine comprehension (Rajpurkar et al., 2016)). AllenNLP is an ongoing open-source effort maintained by engineers and researchers at the Allen Institute for Artificial Intelligence.},
	urldate = {2020-09-24},
	journal = {arXiv:1803.07640 [cs]},
	author = {Gardner, Matt and Grus, Joel and Neumann, Mark and Tafjord, Oyvind and Dasigi, Pradeep and Liu, Nelson and Peters, Matthew and Schmitz, Michael and Zettlemoyer, Luke},
	month = may,
	year = {2018},
	note = {arXiv: 1803.07640},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: Describes the initial version of AllenNLP. Many features and models have been added since the first release. This is the paper to cite if you use AllenNLP in your research. Updated 5/31/2018 with version accepted to the NLP OSS workshop help at ACL 2018},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/TV4KSKNB/1803.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/IIYWCUQS/Gardner et al. - 2018 - AllenNLP A Deep Semantic Natural Language Process.pdf:application/pdf},
}

@inproceedings{angeli_leveraging_2015,
	address = {Beijing, China},
	title = {Leveraging {Linguistic} {Structure} {For} {Open} {Domain} {Information} {Extraction}},
	url = {http://aclweb.org/anthology/P15-1034},
	doi = {10.3115/v1/P15-1034},
	abstract = {Relation triples produced by open domain information extraction (open IE) systems are useful for question answering, inference, and other IE tasks. Traditionally these are extracted using a large set of patterns; however, this approach is brittle on out-of-domain text and long-range dependencies, and gives no insight into the substructure of the arguments. We replace this large pattern set with a few patterns for canonically structured sentences, and shift the focus to a classifier which learns to extract self-contained clauses from longer sentences. We then run natural logic inference over these short clauses to determine the maximally specific arguments for each candidate triple. We show that our approach outperforms a state-of-the-art open IE system on the end-to-end TAC-KBP 2013 Slot Filling task.},
	language = {en},
	urldate = {2020-09-22},
	booktitle = {Proceedings of the 53rd {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} and the 7th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Angeli, Gabor and Johnson Premkumar, Melvin Jose and Manning, Christopher D.},
	year = {2015},
	pages = {344--354},
	file = {Angeli et al. - 2015 - Leveraging Linguistic Structure For Open Domain In.pdf:/home/wolf/Zotero/storage/3V44RITR/Angeli et al. - 2015 - Leveraging Linguistic Structure For Open Domain In.pdf:application/pdf},
}

@article{authors_end--end_nodate,
	title = {End-to-end permutation learning with {Hungarian} algorithm},
	language = {en},
	author = {Authors, Anonymous},
	pages = {9},
	file = {Authors - End-to-end permutation learning with Hungarian alg.pdf:/home/wolf/Zotero/storage/MIEGUB5T/Authors - End-to-end permutation learning with Hungarian alg.pdf:application/pdf},
}

@inproceedings{cho_finding_2014,
	title = {Finding {Matches} in a {Haystack}: {A} {Max}-{Pooling} {Strategy} for {Graph} {Matching} in the {Presence} of {Outliers}},
	isbn = {978-1-4799-5118-5},
	shorttitle = {Finding {Matches} in a {Haystack}},
	url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6909665},
	doi = {10.1109/CVPR.2014.268},
	language = {en},
	urldate = {2020-07-14},
	booktitle = {2014 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	publisher = {IEEE},
	author = {Cho, Minsu and Sun, Jian and Duchenne, Olivier and Ponce, Jean},
	month = jun,
	year = {2014},
	pages = {2091--2098},
	file = {Cho et al. - 2014 - Finding Matches in a Haystack A Max-Pooling Strat.pdf:/home/wolf/Zotero/storage/L35ZST5Y/Cho et al. - 2014 - Finding Matches in a Haystack A Max-Pooling Strat.pdf:application/pdf},
}

@inproceedings{stewart_end--end_2016,
	address = {Las Vegas, NV, USA},
	title = {End-to-{End} {People} {Detection} in {Crowded} {Scenes}},
	isbn = {978-1-4673-8851-1},
	url = {http://ieeexplore.ieee.org/document/7780624/},
	doi = {10.1109/CVPR.2016.255},
	abstract = {Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post-processing steps such as nonmaximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end-to-end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challenging task of detecting people in crowded scenes1.},
	language = {en},
	urldate = {2020-07-14},
	booktitle = {2016 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition} ({CVPR})},
	publisher = {IEEE},
	author = {Stewart, Russell and Andriluka, Mykhaylo and Ng, Andrew Y.},
	month = jun,
	year = {2016},
	pages = {2325--2333},
	file = {Stewart et al. - 2016 - End-to-End People Detection in Crowded Scenes.pdf:/home/wolf/Zotero/storage/MSXCI9F3/Stewart et al. - 2016 - End-to-End People Detection in Crowded Scenes.pdf:application/pdf},
}

@article{simonovsky_deep_2019,
	title = {Deep {Learning} on {Attributed} {Graphs}: {A} {Journey} from {Graphs} to {Their} {Embeddings} and {Back}},
	shorttitle = {Deep {Learning} on {Attributed} {Graphs}},
	url = {http://arxiv.org/abs/1901.08296},
	abstract = {A graph is a powerful concept for representation of relations between pairs of entities. Data with underlying graph structure can be found across many disciplines and there is a natural desire for understanding such data better. Deep learning (DL) has achieved significant breakthroughs in a variety of machine learning tasks in recent years, especially where data is structured on a grid, such as in text, speech, or image understanding. However, surprisingly little has been done to explore the applicability of DL on arbitrary graph-structured data directly. The goal of this thesis is to investigate architectures for DL on graphs and study how to transfer, adapt or generalize concepts that work well on sequential and image data to this domain. We concentrate on two important primitives: embedding graphs or their nodes into a continuous vector space representation (encoding) and, conversely, generating graphs from such vectors back (decoding). To that end, we make the following contributions. First, we introduce Edge-Conditioned Convolutions (ECC), a convolution-like operation on graphs performed in the spatial domain where filters are dynamically generated based on edge attributes. The method is used to encode graphs with arbitrary and varying structure. Second, we propose SuperPoint Graph, an intermediate point cloud representation with rich edge attributes encoding the contextual relationship between object parts. Based on this representation, ECC is employed to segment large-scale point clouds without major sacrifice in fine details. Third, we present GraphVAE, a graph generator allowing us to decode graphs with variable but upper-bounded number of nodes making use of approximate graph matching for aligning the predictions of an autoencoder with its inputs. The method is applied to the task of molecule generation.},
	urldate = {2020-07-14},
	journal = {arXiv:1901.08296 [cs, stat]},
	author = {Simonovsky, Martin},
	month = jan,
	year = {2019},
	note = {arXiv: 1901.08296},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition},
	annote = {Comment: PhD Thesis},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/E2TVFQNT/1901.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/TLV6YQ7J/Simonovsky - 2019 - Deep Learning on Attributed Graphs A Journey from.pdf:application/pdf},
}

@article{kipf_variational_2016,
	title = {Variational {Graph} {Auto}-{Encoders}},
	url = {http://arxiv.org/abs/1611.07308},
	abstract = {We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model using a graph convolutional network (GCN) encoder and a simple inner product decoder. Our model achieves competitive results on a link prediction task in citation networks. In contrast to most existing models for unsupervised learning on graph-structured data and link prediction, our model can naturally incorporate node features, which significantly improves predictive performance on a number of benchmark datasets.},
	urldate = {2020-07-06},
	journal = {arXiv:1611.07308 [cs, stat]},
	author = {Kipf, Thomas N. and Welling, Max},
	month = nov,
	year = {2016},
	note = {arXiv: 1611.07308},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Bayesian Deep Learning Workshop (NIPS 2016)},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/DETZF848/1611.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/GJDZUEIW/Kipf and Welling - 2016 - Variational Graph Auto-Encoders.pdf:application/pdf},
}

@article{arora_iterefine_2020,
	title = {{IterefinE}: {Iterative} {KG} {Refinement} {Embeddings} using {Symbolic} {Knowledge}},
	shorttitle = {{IterefinE}},
	url = {http://arxiv.org/abs/2006.04509},
	abstract = {Knowledge Graphs (KGs) extracted from text sources are often noisy and lead to poor performance in downstream application tasks such as KG-based question answering.While much of the recent activity is focused on addressing the sparsity of KGs by using embeddings for inferring new facts, the issue of cleaning up of noise in KGs through KG refinement task is not as actively studied. Most successful techniques for KG refinement make use of inference rules and reasoning over ontologies. Barring a few exceptions, embeddings do not make use of ontological information, and their performance in KG refinement task is not well understood. In this paper, we present a KG refinement framework called IterefinE which iteratively combines the two techniques - one which uses ontological information and inferences rules, PSL-KGI, and the KG embeddings such as ComplEx and ConvE which do not. As a result, IterefinE is able to exploit not only the ontological information to improve the quality of predictions, but also the power of KG embeddings which (implicitly) perform longer chains of reasoning. The IterefinE framework, operates in a co-training mode and results in explicit type-supervised embedding of the refined KG from PSL-KGI which we call as TypeE-X. Our experiments over a range of KG benchmarks show that the embeddings that we produce are able to reject noisy facts from KG and at the same time infer higher quality new facts resulting in up to 9\% improvement of overall weighted F1 score},
	urldate = {2020-07-02},
	journal = {arXiv:2006.04509 [cs, stat]},
	author = {Arora, Siddhant and Bedathur, Srikanta and Ramanath, Maya and Sharma, Deepak},
	month = jun,
	year = {2020},
	note = {arXiv: 2006.04509},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Databases},
	annote = {Comment: 16 pages, 7 figures, AKBC 2020 Conference},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/XV78I9GU/2006.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/NCTIAC5X/Arora et al. - 2020 - IterefinE Iterative KG Refinement Embeddings usin.pdf:application/pdf},
}

@inproceedings{grohe_word2vec_2020,
	address = {Portland, OR, USA},
	series = {{PODS}'20},
	title = {word2vec, node2vec, graph2vec, {X2vec}: {Towards} a {Theory} of {Vector} {Embeddings} of {Structured} {Data}},
	isbn = {978-1-4503-7108-7},
	shorttitle = {word2vec, node2vec, graph2vec, {X2vec}},
	url = {https://doi.org/10.1145/3375395.3387641},
	doi = {10.1145/3375395.3387641},
	abstract = {Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.},
	urldate = {2020-07-02},
	booktitle = {Proceedings of the 39th {ACM} {SIGMOD}-{SIGACT}-{SIGAI} {Symposium} on {Principles} of {Database} {Systems}},
	publisher = {Association for Computing Machinery},
	author = {Grohe, Martin},
	month = jun,
	year = {2020},
	keywords = {graph kernel, graph neural net, homomorphism, representation learning, vector embedding, weisfeiler leman},
	pages = {1--16},
	file = {Full Text PDF:/home/wolf/Zotero/storage/9SVAMCGX/Grohe - 2020 - word2vec, node2vec, graph2vec, X2vec Towards a Th.pdf:application/pdf},
}

@article{cowen-rivers_neural_2019,
	title = {Neural {Variational} {Inference} {For} {Estimating} {Uncertainty} in {Knowledge} {Graph} {Embeddings}},
	url = {http://arxiv.org/abs/1906.04985},
	abstract = {Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions. The new framework results in a highly-scalable method. Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduce training time at a cost of an additional approximation to the variational lower bound. We introduce two models from this highly scalable probabilistic framework, namely the Latent Information and Latent Fact models, for reasoning over knowledge graph-based representations. Our Latent Information and Latent Fact models improve upon baseline performance under certain conditions. We use the learnt embedding variance to estimate predictive uncertainty during link prediction, and discuss the quality of these learnt uncertainty estimates. Our source code and datasets are publicly available online at https://github.com/alexanderimanicowenrivers/Neural-Variational-Knowledge-Graphs.},
	urldate = {2020-06-18},
	journal = {arXiv:1906.04985 [cs, stat]},
	author = {Cowen-Rivers, Alexander I. and Minervini, Pasquale and Rocktaschel, Tim and Bosnjak, Matko and Riedel, Sebastian and Wang, Jun},
	month = aug,
	year = {2019},
	note = {arXiv: 1906.04985},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Symbolic Computation},
	annote = {Comment: Accepted at IJCAI 19 Neural-Symbolic Learning and Reasoning Workshop},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/Y3ARRDSN/1906.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/5M7JNCLI/Cowen-Rivers et al. - 2019 - Neural Variational Inference For Estimating Uncert.pdf:application/pdf},
}

@article{yang_embedding_2015,
	title = {Embedding {Entities} and {Relations} for {Learning} and {Inference} in {Knowledge} {Bases}},
	url = {http://arxiv.org/abs/1412.6575},
	abstract = {We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (Socher et al., 2013) and TransE (Bordes et al., 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and/or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2\% vs. 54.7\% by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) ={\textgreater} Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning.},
	urldate = {2020-06-18},
	journal = {arXiv:1412.6575 [cs]},
	author = {Yang, Bishan and Yih, Wen-tau and He, Xiaodong and Gao, Jianfeng and Deng, Li},
	month = aug,
	year = {2015},
	note = {arXiv: 1412.6575},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: 12 pages, 4 figures},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/KPKP4469/1412.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/69LHKH8D/Yang et al. - 2015 - Embedding Entities and Relations for Learning and .pdf:application/pdf},
}

@article{battaglia_relational_2018,
	title = {Relational inductive biases, deep learning, and graph networks},
	url = {http://arxiv.org/abs/1806.01261},
	abstract = {Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.},
	urldate = {2020-06-18},
	journal = {arXiv:1806.01261 [cs, stat]},
	author = {Battaglia, Peter W. and Hamrick, Jessica B. and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and Gulcehre, Caglar and Song, Francis and Ballard, Andrew and Gilmer, Justin and Dahl, George and Vaswani, Ashish and Allen, Kelsey and Nash, Charles and Langston, Victoria and Dyer, Chris and Heess, Nicolas and Wierstra, Daan and Kohli, Pushmeet and Botvinick, Matt and Vinyals, Oriol and Li, Yujia and Pascanu, Razvan},
	month = oct,
	year = {2018},
	note = {arXiv: 1806.01261},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/DK3PV6SZ/1806.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/VS86M86Z/Battaglia et al. - 2018 - Relational inductive biases, deep learning, and gr.pdf:application/pdf},
}

@article{duan_graph_2019,
	title = {Graph {Embedding} {VAE}: {A} {Permutation} {Invariant} {Model} of {Graph} {Structure}},
	abstract = {Generative models of graph structure have applications in biology and social sciences. The state of the art is GraphRNN, which decomposes the graph generation process into a series of sequential steps. While effective for modest sizes, it loses its permutation invariance for larger graphs. Instead, we present a permutation invariant latent-variable generative model relying on graph embeddings to encode structure. Using tools from the random graph literature, our model is highly scalable to large graphs with likelihood evaluation and generation in O({\textbar}V {\textbar} + {\textbar}E{\textbar}).},
	language = {en},
	author = {Duan, Tony and Lee, Juho},
	year = {2019},
	pages = {8},
	file = {Duan and Lee - Graph Embedding VAE A Permutation Invariant Model.pdf:/home/wolf/Zotero/storage/F4HCGMJX/Duan and Lee - Graph Embedding VAE A Permutation Invariant Model.pdf:application/pdf},
}

@article{kipf_semi-supervised_2017,
	title = {Semi-{Supervised} {Classification} with {Graph} {Convolutional} {Networks}},
	url = {http://arxiv.org/abs/1609.02907},
	abstract = {We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.},
	language = {en},
	urldate = {2020-06-15},
	journal = {arXiv:1609.02907 [cs, stat]},
	author = {Kipf, Thomas N. and Welling, Max},
	month = feb,
	year = {2017},
	note = {arXiv: 1609.02907},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Published as a conference paper at ICLR 2017},
	file = {Kipf and Welling - 2017 - Semi-Supervised Classification with Graph Convolut.pdf:/home/wolf/Zotero/storage/QLRREG7I/Kipf and Welling - 2017 - Semi-Supervised Classification with Graph Convolut.pdf:application/pdf},
}

@article{chami_low-dimensional_nodate,
	title = {Low-{Dimensional} {Knowledge} {Graph} {Embeddings} via {Hyperbolic} {Rotations}},
	abstract = {Knowledge graphs (KGs) capture rich relationships between a large number of entities. Embeddings of these structures must preserve these relationships with high fidelity. Recently, hyperbolic embedding methods have achieved state-ofthe-art quality in graph representation learning tasks; when embedding certain graphs, they can produce parsimonious embeddings that have higher fidelity while using much fewer dimensions than their Euclidean counterparts. Mirroring work in Euclidean space, we are the first to leverage trainable hyperbolic rotations, a key notion in providing sufficiently rich representations for the complex logical patterns found in KGs. Coupled with trainable curvature, this approach yields improved embeddings in fewer dimensions. We evaluate our method, ROTATIONH, on the WN18RR link prediction task; in the low-dimensional setting, we improve on previous Euclidean-based efforts by 2.2\% in mean reciprocal rank (MRR), and in the high-dimensional setting, we achieve a new state-of-the-art MRR of 49.2\%, improving on existing methods by 1.1\%.},
	language = {en},
	author = {Chami, Ines and Wolf, Adva and Sala, Frederic and Ré, Christopher},
	pages = {6},
	file = {Chami et al. - Low-Dimensional Knowledge Graph Embeddings via Hyp.pdf:/home/wolf/Zotero/storage/W6ZDCUUB/Chami et al. - Low-Dimensional Knowledge Graph Embeddings via Hyp.pdf:application/pdf},
}

@article{zelinka_building_nodate,
	title = {Building {Dynamic} {Knowledge} {Graphs} from {Text}-based {Games}},
	abstract = {We are interested in learning how to update Knowledge Graphs (KG) from text. In this preliminary work, we propose a novel Sequence-to-Sequence (Seq2Seq) architecture to generate elementary KG operations. Furthermore, we introduce a new dataset for KG extraction built upon text-based game transitions (over 300k data points). We conduct experiments and discuss the results.},
	language = {en},
	author = {Zelinka, Mikuláš and Yuan, Xingdi and Côté, Marc-Alexandre and Laroche, Romain and Trischler, Adam},
	pages = {6},
	file = {Zelinka et al. - Building Dynamic Knowledge Graphs from Text-based .pdf:/home/wolf/Zotero/storage/D8ZWAF8E/Zelinka et al. - Building Dynamic Knowledge Graphs from Text-based .pdf:application/pdf},
}

@article{salha_keep_nodate,
	title = {Keep {It} {Simple}: {Graph} {Autoencoders} {Without} {Graph} {Convolutional} {Networks}},
	abstract = {Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering. Graph AE, VAE and most of their extensions rely on graph convolutional networks (GCN) to learn vector space representations of nodes. In this paper, we propose to replace the GCN encoder by a simple linear model w.r.t. the adjacency matrix of the graph. For the two aforementioned tasks, we empirically show that this approach consistently reaches competitive performances w.r.t. GCN-based models for numerous real-world graphs, including the widely used Cora, Citeseer and Pubmed citation networks that became the de facto benchmark datasets for evaluating graph AE and VAE. This result questions the relevance of repeatedly using these three datasets to compare complex graph AE and VAE models. It also emphasizes the effectiveness of simple node encoding schemes for many real-world applications.},
	language = {en},
	author = {Salha, Guillaume and Hennequin, Romain and Vazirgiannis, Michalis},
	pages = {8},
	file = {Salha et al. - Keep It Simple Graph Autoencoders Without Graph C.pdf:/home/wolf/Zotero/storage/AVFBTWG6/Salha et al. - Keep It Simple Graph Autoencoders Without Graph C.pdf:application/pdf},
}

@article{maheshwari_dyngan_nodate,
	title = {{DynGAN}: {Generative} {Adversarial} {Networks} for {Dynamic} {Network} {Embedding}},
	abstract = {Embedding large graphs in a low-dimensional space has proven useful in various applications. However, there is a limited focus on real-world networks that are dynamic in nature and continuously evolving with time. In this paper, we propose a novel adversarial algorithm to learn representation of dynamic networks. We leverage generative adversarial networks and recurrent networks to capture temporal and structural information. We conduct extensive experiments on the task of graph reconstruction, link prediction and graph prediction. Experimental results demonstrate consistent, stable, and better results against state-of-the-art methods in many cases.},
	language = {en},
	author = {Maheshwari, Ayush and Goyal, Ayush and Hanawal, Manjesh Kumar and Ramakrishnan, Ganesh},
	pages = {8},
	file = {Maheshwari et al. - DynGAN Generative Adversarial Networks for Dynami.pdf:/home/wolf/Zotero/storage/IHKF7KLI/Maheshwari et al. - DynGAN Generative Adversarial Networks for Dynami.pdf:application/pdf},
}

@article{su_graph_nodate,
	title = {Graph {Generation} with {Variational} {Recurrent} {Neural} {Network}},
	abstract = {Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurrent Neural Network (GraphVRNN), a probabilistic autoregressive model for graph generation. Through modeling the latent variables of graph data, GraphVRNN can capture the joint distributions of graph structures and the underlying node attributes. We conduct experiments on the proposed GraphVRNN in both graph structure learning and attribute generation tasks. The evaluation results show that the variational component allows our network to model complicated distributions, as well as generate plausible structures and node attributes.},
	language = {en},
	author = {Su, Shih-Yang and Hajimirsadeghi, Hossein and Mori, Greg},
	pages = {8},
	file = {Su et al. - Graph Generation with Variational Recurrent Neural.pdf:/home/wolf/Zotero/storage/4YPY3CGP/Su et al. - Graph Generation with Variational Recurrent Neural.pdf:application/pdf},
}

@article{stoehr_disentangling_nodate,
	title = {Disentangling {Interpretable} {Generative} {Parameters} of {Random} and {Real}-{World} {Graphs}},
	abstract = {While a wide range of interpretable generative procedures for graphs exist, matching observed graph topologies with such procedures and choices for its parameters remains an open problem. Devising generative models that closely reproduce realworld graphs requires domain knowledge and time-consuming simulation. While existing deep learning approaches rely on less manual modelling, they offer little interpretability. This work approaches graph generation (decoding) as the inverse of graph compression (encoding). We show that in a disentanglement-focused deep autoencoding framework, specifically β-Variational Autoencoders (β-VAE), choices of generative procedures and their parameters arise naturally in the latent space. Our model is capable of learning disentangled, interpretable latent variables that represent the generative parameters of procedurally generated random graphs and real-world graphs. The degree of disentanglement is quantitatively measured using the Mutual Information Gap (MIG). When training our β-VAE model on ER random graphs, its latent variables have a near one-to-one mapping to the ER random graph parameters n and p. We deploy the model to analyse the correlation between graph topology and node attributes measuring their mutual dependence without handpicking topological properties. To allow experimenting with the code, we provide an interactive notebook1.},
	language = {en},
	author = {Stoehr, Niklas and Yilmaz, Emine and Brockschmidt, Marc and Stuehmer, Jan},
	pages = {8},
	file = {Stoehr et al. - Disentangling Interpretable Generative Parameters .pdf:/home/wolf/Zotero/storage/7GN5VNV2/Stoehr et al. - Disentangling Interpretable Generative Parameters .pdf:application/pdf},
}

@article{andreoli_convolution_nodate,
	title = {Convolution, attention and structure embedding},
	abstract = {Deep neural networks are composed of layers of parametrised linear operations intertwined with non linear activations. In basic models, such as the multi-layer perceptron, a linear layer operates on a simple input vector embedding of the instance being processed, and produces an output vector embedding by straight multiplication by a matrix parameter. In more complex models, the input and output are structured and their embeddings are higher order tensors. The parameter of each linear operation must then be controlled so as not to explode with the complexity of the structures involved. This is essentially the role of convolution models, which exist in many flavours dependent on the type of structure they deal with (grids, networks, time series etc.). We present here a unified framework which aims at capturing the essence of these diverse models, allowing a systematic analysis of their properties and their mutual enrichment. We also show that attention models naturally fit in the same framework: attention is convolution in which the structure itself is adaptive, and learnt, instead of being given a priori.},
	language = {en},
	author = {Andreoli, Jean-Marc},
	pages = {8},
	file = {Andreoli - Convolution, attention and structure embedding.pdf:/home/wolf/Zotero/storage/2UMFVGX8/Andreoli - Convolution, attention and structure embedding.pdf:application/pdf},
}

@article{birchfield_grid_2017,
	title = {Grid {Structural} {Characteristics} as {Validation} {Criteria} for {Synthetic} {Networks}},
	volume = {32},
	issn = {0885-8950, 1558-0679},
	url = {http://ieeexplore.ieee.org/document/7725528/},
	doi = {10.1109/TPWRS.2016.2616385},
	abstract = {While there have been many works on Graph Neural Networks (GNNs), most of these approaches only consider homogeneous-node graphs without edge features. However, many interesting problems feature different types of nodes and edge features. Based on the example of a power grid, we show that GNNs can learn to model heterogeneous nodes with little additional information given, but that special-purpose network architectures can improve upon that. We compare five different approaches which form a base for any practitioner to start experiments for their specific problem.},
	language = {en},
	number = {4},
	urldate = {2020-06-12},
	journal = {IEEE Transactions on Power Systems},
	author = {Birchfield, Adam B. and Xu, Ti and Gegner, Kathleen M. and Shetye, Komal S. and Overbye, Thomas J.},
	month = jul,
	year = {2017},
	pages = {3258--3265},
	file = {Birchfield et al. - 2017 - Grid Structural Characteristics as Validation Crit.pdf:/home/wolf/Zotero/storage/PBFDQ8WF/Birchfield et al. - 2017 - Grid Structural Characteristics as Validation Crit.pdf:application/pdf},
}

@article{sanborn_node2motif_nodate,
	title = {{NODE2MOTIF}: {Hierarchical} {Invariant} {Embeddings} of {Structured} {Graphs} {Using} the {Bispectrum}},
	abstract = {We present an unsupervised algorithm for embedding graphs into a hierarchy of continuous features that separate local isomorphism classes. The approach incorporates group representation theory and the bispectrum, a third-order polyspectrum that is a complete invariant for small graphs, although any graph invariant can be used in the method. As a direct application, we compute a structural distance metric between organic molecules from the QM9 dataset.},
	language = {en},
	author = {Sanborn, Sophia and Mehta, Ram and Shutty, Noah and Hillar, Christopher},
	pages = {5},
	file = {Sanborn et al. - NODE2MOTIF Hierarchical Invariant Embeddings of S.pdf:/home/wolf/Zotero/storage/2JL4CELR/Sanborn et al. - NODE2MOTIF Hierarchical Invariant Embeddings of S.pdf:application/pdf},
}

@article{tiao_variational_nodate,
	title = {Variational {Graph} {Convolutional} {Networks}},
	abstract = {We propose a Bayesian approach to graph convolutional networks (GCNs) where the graph parameters are considered as random variables. We develop an inference algorithm to estimate the posterior over these parameters and use it to incorporate prior information that is not naturally considered by standard GCNs. The key to our approach is to define a smooth posterior parameterization over the adjacency matrix characterizing the graph, which we estimate via stochastic variational inference. Our experiments show that we can outperform standard GCN methods in the task of semi-supervised classification in noisy-graph regimes.},
	language = {en},
	author = {Tiao, Louis and Elinas, Pantelis and Nguyen, Harrison and Bonilla, Edwin V},
	pages = {11},
	file = {Tiao et al. - Variational Graph Convolutional Networks.pdf:/home/wolf/Zotero/storage/RRQTSTYG/Tiao et al. - Variational Graph Convolutional Networks.pdf:application/pdf},
}

@article{su_f-vaes_2018,
	title = {f-{VAEs}: {Improve} {VAEs} with {Conditional} {Flows}},
	shorttitle = {f-{VAEs}},
	url = {http://arxiv.org/abs/1809.05861},
	abstract = {In this paper, we integrate VAEs and flow-based generative models successfully and get f-VAEs. Compared with VAEs, f-VAEs generate more vivid images, solved the blurred-image problem of VAEs. Compared with flow-based models such as Glow, f-VAE is more lightweight and converges faster, achieving the same performance under smaller-size architecture.},
	urldate = {2020-06-12},
	journal = {arXiv:1809.05861 [cs, stat]},
	author = {Su, Jianlin and Wu, Guang},
	month = sep,
	year = {2018},
	note = {arXiv: 1809.05861},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/NDG8TGJZ/1809.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/UC2Q6CVN/Su and Wu - 2018 - f-VAEs Improve VAEs with Conditional Flows.pdf:application/pdf},
}

@article{shin_incremental_nodate,
	title = {Incremental {Knowledge} {Base} {Construction} {Using} {DeepDive}},
	abstract = {Populating a database with unstructured information is a long-standing problem in industry and research that encompasses problems of extraction, cleaning, and integration. Recent names used for this problem include dealing with dark data and knowledge base construction (KBC). In this work, we describe DeepDive, a system that combines database and machine learning ideas to help develop KBC systems, and we present techniques to make the KBC process more efficient. We observe that the KBC process is iterative, and we develop techniques to incrementally produce inference results for KBC systems. We propose two methods for incremental inference, based respectively on sampling and variational techniques. We also study the tradeoff space of these methods and develop a simple rule-based optimizer. DeepDive includes all of these contributions, and we evaluate DeepDive on five KBC systems, showing that it can speed up KBC inference tasks by up to two orders of magnitude with negligible impact on quality.},
	language = {en},
	author = {Shin, Jaeho and Wu, Sen and Wang, Feiran and Sa, Christopher De and Zhang, Ce and Ré, Christopher},
	pages = {12},
	file = {Shin et al. - Incremental Knowledge Base Construction Using Deep.pdf:/home/wolf/Zotero/storage/26AWXM7T/Shin et al. - Incremental Knowledge Base Construction Using Deep.pdf:application/pdf},
}

@article{niu_elementary_2012,
	title = {Elementary: {Large}-{Scale} {Knowledge}-{Base} {Construction} via {Machine} {Learning} and {Statistical} {Inference}},
	volume = {8},
	issn = {1552-6283, 1552-6291},
	shorttitle = {Elementary},
	url = {http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jswis.2012070103},
	doi = {10.4018/jswis.2012070103},
	abstract = {Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. The authors present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, they have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. The authors describe several challenges and their solutions in designing, implementing, and deploying Elementary. In particular, the authors first describe the conceptual framework and architecture of Elementary to integrate different data resources and KBC techniques in a principled manner. They then discuss how they address scalability challenges to enable Web-scale deployment. The authors empirically show that this decomposition-based inference approach achieves higher performance than prior inference approaches. To validate the effectiveness of Elementary’s approach to KBC, they experimentally show that its ability to incorporate diverse signals has positive impacts on KBC quality.},
	language = {en},
	number = {3},
	urldate = {2020-06-12},
	journal = {International Journal on Semantic Web and Information Systems},
	author = {Niu, Feng and Zhang, Ce and Ré, Christopher and Shavlik, Jude},
	month = jul,
	year = {2012},
	pages = {42--73},
	file = {Niu et al. - 2012 - Elementary Large-Scale Knowledge-Base Constructio.pdf:/home/wolf/Zotero/storage/UDCRH2LW/Niu et al. - 2012 - Elementary Large-Scale Knowledge-Base Constructio.pdf:application/pdf},
}

@misc{noauthor_facebookresearchnle_2020,
	title = {facebookresearch/nle},
	url = {https://github.com/facebookresearch/nle},
	abstract = {The NetHack Learning Environment. Contribute to facebookresearch/nle development by creating an account on GitHub.},
	urldate = {2020-06-12},
	publisher = {Facebook Research},
	month = jun,
	year = {2020},
	note = {original-date: 2020-03-06T00:43:11Z},
}

@article{ribeiro_beyond_2020,
	title = {Beyond {Accuracy}: {Behavioral} {Testing} of {NLP} models with {CheckList}},
	shorttitle = {Beyond {Accuracy}},
	url = {http://arxiv.org/abs/2005.04118},
	abstract = {Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches for evaluating models either focus on individual tasks or on specific behaviors. Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation, as well as a software tool to generate a large and diverse number of test cases quickly. We illustrate the utility of CheckList with tests for three tasks, identifying critical failures in both commercial and state-of-art models. In a user study, a team responsible for a commercial sentiment analysis model found new and actionable bugs in an extensively tested model. In another user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.},
	urldate = {2020-06-12},
	journal = {arXiv:2005.04118 [cs]},
	author = {Ribeiro, Marco Tulio and Wu, Tongshuang and Guestrin, Carlos and Singh, Sameer},
	month = may,
	year = {2020},
	note = {arXiv: 2005.04118},
	keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/KXLLIHJY/2005.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/9N6QDTIG/Ribeiro et al. - 2020 - Beyond Accuracy Behavioral Testing of NLP models .pdf:application/pdf},
}

@article{kingma_auto-encoding_2014,
	title = {Auto-{Encoding} {Variational} {Bayes}},
	url = {http://arxiv.org/abs/1312.6114},
	abstract = {How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions is two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.},
	urldate = {2020-06-11},
	journal = {arXiv:1312.6114 [cs, stat]},
	author = {Kingma, Diederik P. and Welling, Max},
	month = may,
	year = {2014},
	note = {arXiv: 1312.6114},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/AFXN9E5M/1312.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/UEUZ952T/Kingma and Welling - 2014 - Auto-Encoding Variational Bayes.pdf:application/pdf},
}

@article{ha_world_2018,
	title = {World {Models}},
	url = {http://arxiv.org/abs/1803.10122},
	doi = {10.5281/zenodo.1207631},
	abstract = {We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io/},
	urldate = {2020-06-03},
	journal = {arXiv:1803.10122 [cs, stat]},
	author = {Ha, David and Schmidhuber, Jürgen},
	month = mar,
	year = {2018},
	note = {arXiv: 1803.10122},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/VDYQV4VK/1803.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/FJ8YWP9Q/Ha and Schmidhuber - 2018 - World Models.pdf:application/pdf},
}

@article{gangemi_modeling_2018,
	title = {Modeling {Relational} {Data} with {Graph} {Convolutional} {Networks}},
	urldate = {2020-06-03},
	booktitle = {The {Semantic} {Web}},
	publisher = {Springer International Publishing},
	author = {Schlichtkrull, Michael and Kipf, Thomas N. and Bloem, Peter and van den Berg, Rianne and Titov, Ivan and Welling, Max},
	year = {2018},
	doi = {10.1007/978-3-319-93417-4_38},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {593--607},
}

@misc{diestel2016graph,
  title={Graph Theory. 5th. Vol. 173. GTM},
  author={Diestel, Reinhard},
  year={2016},
  publisher={Springer}
}

@online{pwcWN,
author = {paperswithcode},
title  = {Link Prediction on WN18RR},
urldate   = {2021-01},
month = jan,
url    = {https://paperswithcode.com/sota/link-prediction-on-wn18rr}
}

@online{pwcFB,
author = {paperswithcode},
title  = {Link Prediction on FB15k-237},
urldate   = {2021-01},
month = jan,
url    = {https://paperswithcode.com/sota/link-prediction-on-fb15k-237}
}


@incollection{bordes_translating_2013-1,
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	url = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf},
	urldate = {2020-06-03},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 26},
	publisher = {Curran Associates, Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
	editor = {Burges, C. J. C. and Bottou, L. and Welling, M. and Ghahramani, Z. and Weinberger, K. Q.},
	year = {2013},
	pages = {2787--2795},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/LZ6ZCULM/5071-translating-embeddings-for-modeling-multi-relational-data.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/IN3R2PVL/Bordes et al. - 2013 - Translating Embeddings for Modeling Multi-relation.pdf:application/pdf},
}

@inproceedings{mintz_distant_2009,
	address = {Suntec, Singapore},
	title = {Distant supervision for relation extraction without labeled data},
	volume = {2},
	isbn = {978-1-932432-46-6},
	url = {http://portal.acm.org/citation.cfm?doid=1690219.1690287},
	doi = {10.3115/1690219.1690287},
	abstract = {Modern models of relation extraction for tasks like ACE are based on supervised learning of relations from small hand-labeled corpora. We investigate an alternative paradigm that does not require labeled corpora, avoiding the domain dependence of ACEstyle algorithms, and allowing the use of corpora of any size. Our experiments use Freebase, a large semantic database of several thousand relations, to provide distant supervision. For each pair of entities that appears in some Freebase relation, we find all sentences containing those entities in a large unlabeled corpus and extract textual features to train a relation classifier. Our algorithm combines the advantages of supervised IE (combining 400,000 noisy pattern features in a probabilistic classifier) and unsupervised IE (extracting large numbers of relations from large corpora of any domain). Our model is able to extract 10,000 instances of 102 relations at a precision of 67.6\%. We also analyze feature performance, showing that syntactic parse features are particularly helpful for relations that are ambiguous or lexically distant in their expression.},
	language = {en},
	urldate = {2020-06-03},
	booktitle = {Proceedings of the {Joint} {Conference} of the 47th {Annual} {Meeting} of the {ACL} and the 4th {International} {Joint} {Conference} on {Natural} {Language} {Processing} of the {AFNLP}: {Volume} 2 - {ACL}-{IJCNLP} '09},
	publisher = {Association for Computational Linguistics},
	author = {Mintz, Mike and Bills, Steven and Snow, Rion and Jurafsky, Dan},
	year = {2009},
	pages = {1003},
	file = {Mintz et al. - 2009 - Distant supervision for relation extraction withou.pdf:/home/wolf/Zotero/storage/H3PG4PMH/Mintz et al. - 2009 - Distant supervision for relation extraction withou.pdf:application/pdf},
}

@article{zhang_semantics-aware_2020,
	title = {Semantics-aware {BERT} for {Language} {Understanding}},
	url = {http://arxiv.org/abs/1909.02209},
	abstract = {The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. SemBERT keeps the convenient usability of its BERT precursor in a light fine-tuning way without substantial task-specific modifications. Compared with BERT, semantics-aware BERT is as simple in concept but more powerful. It obtains new state-of-the-art or substantially improves results on ten reading comprehension and language inference tasks.},
	urldate = {2020-06-02},
	journal = {arXiv:1909.02209 [cs]},
	author = {Zhang, Zhuosheng and Wu, Yuwei and Zhao, Hai and Li, Zuchao and Zhang, Shuailiang and Zhou, Xi and Zhou, Xiang},
	month = feb,
	year = {2020},
	note = {arXiv: 1909.02209},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020)},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/F4EHJ8LP/1909.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/IQXQTJSF/Zhang et al. - 2020 - Semantics-aware BERT for Language Understanding.pdf:application/pdf},
}

@article{parikh_decomposable_2016,
	title = {A {Decomposable} {Attention} {Model} for {Natural} {Language} {Inference}},
	url = {http://arxiv.org/abs/1606.01933},
	abstract = {We propose a simple neural architecture for natural language inference. Our approach uses attention to decompose the problem into subproblems that can be solved separately, thus making it trivially parallelizable. On the Stanford Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results with almost an order of magnitude fewer parameters than previous work and without relying on any word-order information. Adding intra-sentence attention that takes a minimum amount of order into account yields further improvements.},
	urldate = {2020-06-02},
	journal = {arXiv:1606.01933 [cs]},
	author = {Parikh, Ankur P. and Täckström, Oscar and Das, Dipanjan and Uszkoreit, Jakob},
	month = sep,
	year = {2016},
	note = {arXiv: 1606.01933},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: 7 pages, 1 figure, Proceeedings of EMNLP 2016},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/FA3KESHR/1606.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/YV9XM365/Parikh et al. - 2016 - A Decomposable Attention Model for Natural Languag.pdf:application/pdf},
}

@inproceedings{chen_meta_2019,
	address = {Hong Kong, China},
	title = {Meta {Relational} {Learning} for {Few}-{Shot} {Link} {Prediction} in {Knowledge} {Graphs}},
	url = {https://www.aclweb.org/anthology/D19-1431},
	doi = {10.18653/v1/D19-1431},
	abstract = {Link prediction is an important way to complete knowledge graphs (KGs), while embedding-based methods, effective for link prediction in KGs, perform poorly on relations that only have a few associative triples. In this work, we propose a Meta Relational Learning (MetaR) framework to do the common but challenging few-shot link prediction in KGs, namely predicting new triples about a relation by only observing a few associative triples. We solve few-shot link prediction by focusing on transferring relation-specific meta information to make model learn the most important knowledge and learn faster, corresponding to relation meta and gradient meta respectively in MetaR. Empirically, our model achieves state-of-the-art results on few-shot link prediction KG benchmarks.},
	urldate = {2020-06-02},
	booktitle = {Proceedings of the 2019 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} and the 9th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({EMNLP}-{IJCNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Chen, Mingyang and Zhang, Wen and Zhang, Wei and Chen, Qiang and Chen, Huajun},
	month = nov,
	year = {2019},
	pages = {4217--4226},
	file = {Full Text PDF:/home/wolf/Zotero/storage/HFWB38UN/Chen et al. - 2019 - Meta Relational Learning for Few-Shot Link Predict.pdf:application/pdf},
}

@article{papamakarios_masked_2018,
	title = {Masked {Autoregressive} {Flow} for {Density} {Estimation}},
	url = {http://arxiv.org/abs/1705.07057},
	abstract = {Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data. By constructing a stack of autoregressive models, each modelling the random numbers of the next model in the stack, we obtain a type of normalizing flow suitable for density estimation, which we call Masked Autoregressive Flow. This type of flow is closely related to Inverse Autoregressive Flow and is a generalization of Real NVP. Masked Autoregressive Flow achieves state-of-the-art performance in a range of general-purpose density estimation tasks.},
	urldate = {2020-06-02},
	journal = {arXiv:1705.07057 [cs, stat]},
	author = {Papamakarios, George and Pavlakou, Theo and Murray, Iain},
	month = jun,
	year = {2018},
	note = {arXiv: 1705.07057},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: section 4.3 is corrected since the previous version},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/L4GU3VPP/1705.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/DJQVF7KG/Papamakarios et al. - 2018 - Masked Autoregressive Flow for Density Estimation.pdf:application/pdf},
}

@article{ha_world_2018-1,
	title = {World {Models}},
	url = {http://arxiv.org/abs/1803.10122},
	doi = {10.5281/zenodo.1207631},
	abstract = {We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment. An interactive version of this paper is available at https://worldmodels.github.io/},
	urldate = {2020-05-29},
	journal = {arXiv:1803.10122 [cs, stat]},
	author = {Ha, David and Schmidhuber, Jürgen},
	month = mar,
	year = {2018},
	note = {arXiv: 1803.10122},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/8NFQRCTL/1803.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/B7CZPHR8/Ha and Schmidhuber - 2018 - World Models.pdf:application/pdf},
}

@article{li_deep_2017,
	title = {Deep {Recurrent} {Generative} {Decoder} for {Abstractive} {Text} {Summarization}},
	url = {http://arxiv.org/abs/1708.00625},
	abstract = {We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.},
	urldate = {2020-05-29},
	journal = {arXiv:1708.00625 [cs]},
	author = {Li, Piji and Lam, Wai and Bing, Lidong and Wang, Zihao},
	month = aug,
	year = {2017},
	note = {arXiv: 1708.00625},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
	annote = {Comment: 10 pages, EMNLP 2017},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/2WD4PP8J/1708.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/HQQKHRPQ/Li et al. - 2017 - Deep Recurrent Generative Decoder for Abstractive .pdf:application/pdf},
}


@InProceedings{simonovsky_graphvae_2018,
	author="Simonovsky, Martin
	and Komodakis, Nikos",
	title="GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders",
	booktitle="Artificial Neural Networks and Machine Learning -- ICANN 2018",
	year="2018",
	publisher="Springer International Publishing",
	address="Cham",
	pages="412--422",
	isbn="978-3-030-01418-6"
}

@inproceedings{cudre2013nosql,
  title={NoSQL databases for RDF: an empirical evaluation},
  author={Cudr{\'e}-Mauroux, Philippe and Enchev, Iliya and Fundatureanu, Sever and Groth, Paul and Haque, Albert and Harth, Andreas and Keppmann, Felix Leif and Miranker, Daniel and Sequeda, Juan F and Wylot, Marcin},
  booktitle={International Semantic Web Conference},
  pages={310--325},
  year={2013},
  organization={Springer}
}

@article{miller1998introduction,
  title={An introduction to the resource description framework},
  author={Miller, Eric},
  journal={Bulletin of the American Society for Information Science and Technology},
  volume={25},
  number={1},
  pages={15--19},
  year={1998},
  publisher={Wiley Online Library}
}

@article{ehrlinger2016towards,
  title={Towards a Definition of Knowledge Graphs.},
  author={Ehrlinger, Lisa and W{\"o}{\ss}, Wolfram},
  journal={SEMANTiCS (Posters, Demos, SuCCESS)},
  volume={48},
  pages={1--4},
  year={2016}
}

@inproceedings{simonovsky2017dynamic,
  title={Dynamic edge-conditioned filters in convolutional neural networks on graphs},
  author={Simonovsky, Martin and Komodakis, Nikos},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3693--3702},
  year={2017}
}

@article{khosla_supervised_2020,
	title = {Supervised {Contrastive} {Learning}},
	url = {http://arxiv.org/abs/2004.11362},
	abstract = {Cross entropy is the most widely used loss function for supervised training of image classification models. In this paper, we propose a novel training methodology that consistently outperforms cross entropy on supervised learning tasks across different architectures and data augmentations. We modify the batch contrastive loss, which has recently been shown to be very effective at learning powerful representations in the self-supervised setting. We are thus able to leverage label information more effectively than cross entropy. Clusters of points belonging to the same class are pulled together in embedding space, while simultaneously pushing apart clusters of samples from different classes. In addition to this, we leverage key ingredients such as large batch sizes and normalized embeddings, which have been shown to benefit self-supervised learning. On both ResNet-50 and ResNet-200, we outperform cross entropy by over 1\%, setting a new state of the art number of 78.8\% among methods that use AutoAugment data augmentation. The loss also shows clear benefits for robustness to natural corruptions on standard benchmarks on both calibration and accuracy. Compared to cross entropy, our supervised contrastive loss is more stable to hyperparameter settings such as optimizers or data augmentations.},
	urldate = {2020-05-20},
	journal = {arXiv:2004.11362 [cs, stat]},
	author = {Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
	month = apr,
	year = {2020},
	note = {arXiv: 2004.11362},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Vision and Pattern Recognition},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/F2YP3NXV/2004.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/E47R2CHS/Khosla et al. - 2020 - Supervised Contrastive Learning.pdf:application/pdf},
}

@article{mikolov_efficient_2013,
	title = {Efficient {Estimation} of {Word} {Representations} in {Vector} {Space}},
	url = {http://arxiv.org/abs/1301.3781},
	abstract = {We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.},
	urldate = {2020-05-20},
	journal = {arXiv:1301.3781 [cs]},
	author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
	month = sep,
	year = {2013},
	note = {arXiv: 1301.3781},
	keywords = {Computer Science - Computation and Language},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/LRDRQ5RN/1301.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/QWTV5MNU/Mikolov et al. - 2013 - Efficient Estimation of Word Representations in Ve.pdf:application/pdf},
}

@article{ren_cotype_2017,
	title = {{CoType}: {Joint} {Extraction} of {Typed} {Entities} and {Relations} with {Knowledge} {Bases}},
	shorttitle = {{CoType}},
	url = {http://arxiv.org/abs/1610.08763},
	abstract = {Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25\% improvement in F1 score compared to the next best method.},
	urldate = {2020-05-19},
	journal = {arXiv:1610.08763 [cs]},
	author = {Ren, Xiang and Wu, Zeqiu and He, Wenqi and Qu, Meng and Voss, Clare R. and Ji, Heng and Abdelzaher, Tarek F. and Han, Jiawei},
	month = jun,
	year = {2017},
	note = {arXiv: 1610.08763},
	keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language},
	annote = {Comment: WWW 2017},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/PDIGLJAP/1610.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/WKAU2QSD/Ren et al. - 2017 - CoType Joint Extraction of Typed Entities and Rel.pdf:application/pdf},
}

@article{hoffmann_knowledge-based_nodate,
	title = {Knowledge-{Based} {Weak} {Supervision} for {Information} {Extraction} of {Overlapping} {Relations}},
	abstract = {Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web’s natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors. Recently, researchers have developed multiinstance learning algorithms to combat the noisy training data that can come from heuristic labeling, but their models assume relations are disjoint — for example they cannot extract the pair Founded(Jobs, Apple) and CEO-of(Jobs, Apple).},
	language = {en},
	author = {Hoffmann, Raphael and Zhang, Congle and Ling, Xiao and Zettlemoyer, Luke and Weld, Daniel S},
	pages = {10},
	file = {Hoffmann et al. - Knowledge-Based Weak Supervision for Information E.pdf:/home/wolf/Zotero/storage/42J4XVVK/Hoffmann et al. - Knowledge-Based Weak Supervision for Information E.pdf:application/pdf},
}

@article{hoffmann_knowledge-based_nodate-1,
	title = {Knowledge-{Based} {Weak} {Supervision} for {Information} {Extraction} of {Overlapping} {Relations}},
	abstract = {Information extraction (IE) holds the promise of generating a large-scale knowledge base from the Web’s natural language text. Knowledge-based weak supervision, using structured data to heuristically label a training corpus, works towards this goal by enabling the automated learning of a potentially unbounded number of relation extractors. Recently, researchers have developed multiinstance learning algorithms to combat the noisy training data that can come from heuristic labeling, but their models assume relations are disjoint — for example they cannot extract the pair Founded(Jobs, Apple) and CEO-of(Jobs, Apple).},
	language = {en},
	author = {Hoffmann, Raphael and Zhang, Congle and Ling, Xiao and Zettlemoyer, Luke and Weld, Daniel S},
	pages = {10},
	file = {Hoffmann et al. - Knowledge-Based Weak Supervision for Information E.pdf:/home/wolf/Zotero/storage/ALLA8SGR/Hoffmann et al. - Knowledge-Based Weak Supervision for Information E.pdf:application/pdf},
}

@article{liu_heterogeneous_2017,
	title = {Heterogeneous {Supervision} for {Relation} {Extraction}: {A} {Representation} {Learning} {Approach}},
	shorttitle = {Heterogeneous {Supervision} for {Relation} {Extraction}},
	url = {http://arxiv.org/abs/1707.00166},
	abstract = {Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous information source, e.g., knowledge base and domain heuristics. These annotations, referred as heterogeneous supervision, often conflict with each other, which brings a new challenge to the original relation extraction task: how to infer the true label from noisy labels for a given instance. Identifying context information as the backbone of both relation extraction and true label discovery, we adopt embedding techniques to learn the distributed representations of context, which bridges all components with mutual enhancement in an iterative fashion. Extensive experimental results demonstrate the superiority of REHession over the state-of-the-art.},
	urldate = {2020-05-19},
	journal = {arXiv:1707.00166 [cs]},
	author = {Liu, Liyuan and Ren, Xiang and Zhu, Qi and Zhi, Shi and Gui, Huan and Ji, Heng and Han, Jiawei},
	month = aug,
	year = {2017},
	note = {arXiv: 1707.00166},
	keywords = {Computer Science - Computation and Language},
	annote = {Comment: EMNLP 2017},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/5NDXCBSY/1707.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/5TTKSZBF/Liu et al. - 2017 - Heterogeneous Supervision for Relation Extraction.pdf:application/pdf},
}

@article{de_cao_molgan_2018,
	title = {{MolGAN}: {An} implicit generative model for small molecular graphs},
	shorttitle = {{MolGAN}},
	url = {http://arxiv.org/abs/1805.11973},
	abstract = {Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an implicit, likelihood-free generative model for small molecular graphs that circumvents the need for expensive graph matching procedures or node ordering heuristics of previous likelihood-based methods. Our method adapts generative adversarial networks (GANs) to operate directly on graph-structured data. We combine our approach with a reinforcement learning objective to encourage the generation of molecules with specific desired chemical properties. In experiments on the QM9 chemical database, we demonstrate that our model is capable of generating close to 100\% valid compounds. MolGAN compares favorably both to recent proposals that use string-based (SMILES) representations of molecules and to a likelihood-based method that directly generates graphs, albeit being susceptible to mode collapse.},
	urldate = {2020-05-19},
	journal = {arXiv:1805.11973 [cs, stat]},
	author = {De Cao, Nicola and Kipf, Thomas},
	month = may,
	year = {2018},
	note = {arXiv: 1805.11973},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: 11 pages, 3 figures, 3 tables},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/T6A8WBAL/1805.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/5RHQFWC9/De Cao and Kipf - 2018 - MolGAN An implicit generative model for small mol.pdf:application/pdf},
}

@article{devlin_bert_2019,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {http://arxiv.org/abs/1810.04805},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5\% (7.7\% point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
	urldate = {2020-05-19},
	journal = {arXiv:1810.04805 [cs]},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = may,
	year = {2019},
	note = {arXiv: 1810.04805},
	keywords = {Computer Science - Computation and Language},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/ADZAWADK/1810.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/NMZXGRF9/Devlin et al. - 2019 - BERT Pre-training of Deep Bidirectional Transform.pdf:application/pdf},
}

@article{liu_graph_2019,
	title = {Graph {Normalizing} {Flows}},
	url = {http://arxiv.org/abs/1905.13177},
	abstract = {We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervised case, we combine graph normalizing flows with a novel graph auto-encoder to create a generative model of graph structures. Our model is permutation-invariant, generating entire graphs with a single feed-forward pass, and achieves competitive results with the state-of-the art auto-regressive models, while being better suited to parallel computing architectures.},
	urldate = {2020-05-19},
	journal = {arXiv:1905.13177 [cs, stat]},
	author = {Liu, Jenny and Kumar, Aviral and Ba, Jimmy and Kiros, Jamie and Swersky, Kevin},
	month = may,
	year = {2019},
	note = {arXiv: 1905.13177},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/93RKDT75/1905.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/NMCULX3N/Liu et al. - 2019 - Graph Normalizing Flows.pdf:application/pdf},
}

@article{poon_unsupervised_nodate,
	title = {Unsupervised {Ontology} {Induction} from {Text}},
	abstract = {Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induction, information extraction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scalability. We present OntoUSP, a system that induces and populates a probabilistic ontology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierarchy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate OntoUSP by using it to extract a knowledge base from biomedical abstracts and answer questions. OntoUSP improves on the recall of USP by 47\% and greatly outperforms previous state-of-the-art approaches.},
	language = {en},
	author = {Poon, Hoifung and Domingos, Pedro},
	pages = {10},
	file = {Poon and Domingos - Unsupervised Ontology Induction from Text.pdf:/home/wolf/Zotero/storage/A6FJWQCN/Poon and Domingos - Unsupervised Ontology Induction from Text.pdf:application/pdf},
}

@article{poon_unsupervised_nodate-1,
	title = {Unsupervised {Ontology} {Induction} from {Text}},
	abstract = {Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induction, information extraction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scalability. We present OntoUSP, a system that induces and populates a probabilistic ontology using only dependency-parsed text as input. OntoUSP builds on the USP unsupervised semantic parser by jointly forming ISA and IS-PART hierarchies of lambda-form clusters. The ISA hierarchy allows more general knowledge to be learned, and the use of smoothing for parameter estimation. We evaluate OntoUSP by using it to extract a knowledge base from biomedical abstracts and answer questions. OntoUSP improves on the recall of USP by 47\% and greatly outperforms previous state-of-the-art approaches.},
	language = {en},
	author = {Poon, Hoifung and Domingos, Pedro},
	pages = {10},
	file = {Poon and Domingos - Unsupervised Ontology Induction from Text.pdf:/home/wolf/Zotero/storage/3QU956C3/Poon and Domingos - Unsupervised Ontology Induction from Text.pdf:application/pdf},
}

@incollection{lowe_putting_2019,
	title = {Putting {An} {End} to {End}-to-{End}: {Gradient}-{Isolated} {Learning} of {Representations}},
	shorttitle = {Putting {An} {End} to {End}-to-{End}},
	url = {http://papers.nips.cc/paper/8568-putting-an-end-to-end-to-end-gradient-isolated-learning-of-representations.pdf},
	urldate = {2020-05-19},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 32},
	publisher = {Curran Associates, Inc.},
	author = {Löwe, Sindy and O{\textbackslash}textquotesingle Connor, Peter and Veeling, Bastiaan},
	editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and Alché-Buc, F. d{\textbackslash}textquotesingle and Fox, E. and Garnett, R.},
	year = {2019},
	pages = {3039--3051},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/2N5LG2XC/8568-putting-an-end-to-end-to-end-gradient-isolated-learning-of-representations.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/53ZH8XRL/Löwe et al. - 2019 - Putting An End to End-to-End Gradient-Isolated Le.pdf:application/pdf},
}

@article{li_learning_2018,
	title = {Learning {Deep} {Generative} {Models} of {Graphs}},
	url = {http://arxiv.org/abs/1803.03324},
	abstract = {Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both their structure and attributes. Our approach uses graph neural networks to express probabilistic dependencies among a graph's nodes and edges, and can, in principle, learn distributions over any arbitrary graph. In a series of experiments our results show that once trained, our models can generate good quality samples of both synthetic graphs as well as real molecular graphs, both unconditionally and conditioned on data. Compared to baselines that do not use graph-structured representations, our models often perform far better. We also explore key challenges of learning generative models of graphs, such as how to handle symmetries and ordering of elements during the graph generation process, and offer possible solutions. Our work is the first and most general approach for learning generative models over arbitrary graphs, and opens new directions for moving away from restrictions of vector- and sequence-like knowledge representations, toward more expressive and flexible relational data structures.},
	urldate = {2020-05-15},
	journal = {arXiv:1803.03324 [cs, stat]},
	author = {Li, Yujia and Vinyals, Oriol and Dyer, Chris and Pascanu, Razvan and Battaglia, Peter},
	month = mar,
	year = {2018},
	note = {arXiv: 1803.03324},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: 21 pages},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/33WJ4V6J/1803.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/XMMLTCHX/Li et al. - 2018 - Learning Deep Generative Models of Graphs.pdf:application/pdf},
}

@article{oliehoek_beyond_2019,
	title = {Beyond {Local} {Nash} {Equilibria} for {Adversarial} {Networks}},
	volume = {1021},
	url = {http://arxiv.org/abs/1806.07268},
	doi = {10.1007/978-3-030-31978-6_7},
	abstract = {Save for some special cases, current training methods for Generative Adversarial Networks (GANs) are at best guaranteed to converge to a `local Nash equilibrium` (LNE). Such LNEs, however, can be arbitrarily far from an actual Nash equilibrium (NE), which implies that there are no guarantees on the quality of the found generator or classifier. This paper proposes to model GANs explicitly as finite games in mixed strategies, thereby ensuring that every LNE is an NE. With this formulation, we propose a solution method that is proven to monotonically converge to a resource-bounded Nash equilibrium (RB-NE): by increasing computational resources we can find better solutions. We empirically demonstrate that our method is less prone to typical GAN problems such as mode collapse, and produces solutions that are less exploitable than those produced by GANs and MGANs, and closely resemble theoretical predictions about NEs.},
	urldate = {2020-05-15},
	journal = {arXiv:1806.07268 [cs, stat]},
	author = {Oliehoek, Frans A. and Savani, Rahul and Gallego, Jose and van der Pol, Elise and Groß, Roderich},
	year = {2019},
	note = {arXiv: 1806.07268},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Science and Game Theory},
	pages = {73--89},
	annote = {Comment: Supersedes arXiv:1712.00679; v2 includes Fictitious GAN in the related work and refers to Danskin (1981)},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/LJ5QTUNG/1806.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/MABMIDYL/Oliehoek et al. - 2019 - Beyond Local Nash Equilibria for Adversarial Netwo.pdf:application/pdf},
}

@article{hamilton_embedding_2019,
	title = {Embedding {Logical} {Queries} on {Knowledge} {Graphs}},
	url = {http://arxiv.org/abs/1806.01445},
	abstract = {Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that \{{\textbackslash}em might\} interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.},
	urldate = {2020-05-15},
	journal = {arXiv:1806.01445 [cs, stat]},
	author = {Hamilton, William L. and Bajaj, Payal and Zitnik, Marinka and Jurafsky, Dan and Leskovec, Jure},
	month = oct,
	year = {2019},
	note = {arXiv: 1806.01445},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Social and Information Networks},
	annote = {Comment: Published in NeurIPS 2018},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/B2MFL572/1806.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/C5SVDFI8/Hamilton et al. - 2019 - Embedding Logical Queries on Knowledge Graphs.pdf:application/pdf},
}

@incollection{santoro_simple_2017,
	title = {A simple neural network module for relational reasoning},
	url = {http://papers.nips.cc/paper/7082-a-simple-neural-network-module-for-relational-reasoning.pdf},
	urldate = {2020-05-15},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 30},
	publisher = {Curran Associates, Inc.},
	author = {Santoro, Adam and Raposo, David and Barrett, David G and Malinowski, Mateusz and Pascanu, Razvan and Battaglia, Peter and Lillicrap, Timothy},
	editor = {Guyon, I. and Luxburg, U. V. and Bengio, S. and Wallach, H. and Fergus, R. and Vishwanathan, S. and Garnett, R.},
	year = {2017},
	pages = {4967--4976},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/FX29XKQ6/7082-a-simple-neural-network-module-for-relational-reasoning.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/EPBX9R65/Santoro et al. - 2017 - A simple neural network module for relational reas.pdf:application/pdf},
}

@article{vaswani_attention_2017,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2020-05-14},
	journal = {arXiv:1706.03762 [cs]},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = dec,
	year = {2017},
	note = {arXiv: 1706.03762},
	keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language},
	annote = {Comment: 15 pages, 5 figures},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/5D42JIKF/1706.html:text/html;Full Text:/home/wolf/Zotero/storage/6RWA3BAT/Vaswani et al. - 2017 - Attention Is All You Need.pdf:application/pdf},
}

@inproceedings{ruffinelli_you_2019,
	title = {You {CAN} {Teach} an {Old} {Dog} {New} {Tricks}! {On} {Training} {Knowledge} {Graph} {Embeddings}},
	url = {https://openreview.net/forum?id=BkxSmlBFvr},
	abstract = {Knowledge graph embedding (KGE) models learn algebraic representations of the entities and relations in a knowledge graph. A vast number of KGE techniques for multi-relational link prediction have...},
	urldate = {2020-05-14},
	author = {Ruffinelli, Daniel and Broscheit, Samuel and Gemulla, Rainer},
	month = sep,
	year = {2019},
	file = {Snapshot:/home/wolf/Zotero/storage/D8NQRFDJ/forum.html:text/html;Full Text PDF:/home/wolf/Zotero/storage/3WHRBAZ5/Ruffinelli et al. - 2019 - You CAN Teach an Old Dog New Tricks! On Training K.pdf:application/pdf},
}

@inproceedings{ren_afet_2016,
	address = {Austin, Texas},
	title = {{AFET}: {Automatic} {Fine}-{Grained} {Entity} {Typing} by {Hierarchical} {Partial}-{Label} {Embedding}},
	shorttitle = {{AFET}},
	url = {https://www.aclweb.org/anthology/D16-1144},
	doi = {10.18653/v1/D16-1144},
	urldate = {2020-05-13},
	booktitle = {Proceedings of the 2016 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing}},
	publisher = {Association for Computational Linguistics},
	author = {Ren, Xiang and He, Wenqi and Qu, Meng and Huang, Lifu and Ji, Heng and Han, Jiawei},
	month = nov,
	year = {2016},
	pages = {1369--1378},
	file = {Full Text PDF:/home/wolf/Zotero/storage/KVN8XTEB/Ren et al. - 2016 - AFET Automatic Fine-Grained Entity Typing by Hier.pdf:application/pdf},
}

@article{ren_cotype_2017-1,
	title = {{CoType}: {Joint} {Extraction} of {Typed} {Entities} and {Relations} with {Knowledge} {Bases}},
	shorttitle = {{CoType}},
	url = {http://arxiv.org/abs/1610.08763},
	abstract = {Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25\% improvement in F1 score compared to the next best method.},
	urldate = {2020-05-13},
	journal = {arXiv:1610.08763 [cs]},
	author = {Ren, Xiang and Wu, Zeqiu and He, Wenqi and Qu, Meng and Voss, Clare R. and Ji, Heng and Abdelzaher, Tarek F. and Han, Jiawei},
	month = jun,
	year = {2017},
	note = {arXiv: 1610.08763},
	keywords = {Computer Science - Machine Learning, Computer Science - Computation and Language},
	annote = {Comment: WWW 2017},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/ZPPNIPMZ/1610.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/I3XPIJM5/Ren et al. - 2017 - CoType Joint Extraction of Typed Entities and Rel.pdf:application/pdf},
}

@misc{noauthor_development_nodate,
	title = {Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports {\textbar} {Elsevier} {Enhanced} {Reader}},
	url = {https://reader.elsevier.com/reader/sd/pii/S1532046412000615?token=6A75D5479FA426C1BEC8349744108671443493D14BD185EB4C839978CE1BAF3ABAE5D54AA2B86199D7B846094C6EB759},
	language = {en},
	urldate = {2020-05-13},
	doi = {10.1016/j.jbi.2012.04.008},
	note = {Library Catalog: reader.elsevier.com},
	file = {Full Text:/home/wolf/Zotero/storage/CW8EEC97/Development of a benchmark corpus to support the a.pdf:application/pdf;Snapshot:/home/wolf/Zotero/storage/KYUZ474C/S1532046412000615.html:text/html},
}

@article{liu_seq2rdf_2018,
	title = {{Seq2RDF}: {An} end-to-end application for deriving {Triples} from {Natural} {Language} {Text}},
	shorttitle = {{Seq2RDF}},
	url = {http://arxiv.org/abs/1807.01763},
	abstract = {We present an end-to-end approach that takes unstructured textual input and generates structured output compliant with a given vocabulary. Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings. Our model learns the mapping from natural language text to triple representation in the form of subject-predicate-object using the selected knowledge graph vocabulary. Experiments on three different data sets show that we achieve competitive F1-Measures over the baselines using our simple yet effective approach. A demo video is included.},
	urldate = {2020-05-13},
	journal = {arXiv:1807.01763 [cs]},
	author = {Liu, Yue and Zhang, Tongtao and Liang, Zhicheng and Ji, Heng and McGuinness, Deborah L.},
	month = aug,
	year = {2018},
	note = {arXiv: 1807.01763
version: 3},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
	annote = {Comment: Proceedings of the 17th International Semantic Web Conference P\&D},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/ZNCH29E5/1807.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/4TKEQWEE/Liu et al. - 2018 - Seq2RDF An end-to-end application for deriving Tr.pdf:application/pdf},
}

@incollection{bordes_translating_2013-2,
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	url = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf},
	urldate = {2020-05-12},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 26},
	publisher = {Curran Associates, Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
	editor = {Burges, C. J. C. and Bottou, L. and Welling, M. and Ghahramani, Z. and Weinberger, K. Q.},
	year = {2013},
	pages = {2787--2795},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/ZSIARUH9/5071-translating-embeddings-for-modeling-multi-relational-data.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/GBDFAVPW/Bordes et al. - 2013 - Translating Embeddings for Modeling Multi-relation.pdf:application/pdf},
}

@incollection{bordes_translating_2013-3,
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	url = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf},
	urldate = {2020-05-12},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 26},
	publisher = {Curran Associates, Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
	editor = {Burges, C. J. C. and Bottou, L. and Welling, M. and Ghahramani, Z. and Weinberger, K. Q.},
	year = {2013},
	pages = {2787--2795},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/ATMVE5SK/5071-translating-embeddings-for-modeling-multi-rela.html:text/html},
}

@incollection{bordes_translating_2013-4,
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	url = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf},
	urldate = {2020-05-12},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 26},
	publisher = {Curran Associates, Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
	editor = {Burges, C. J. C. and Bottou, L. and Welling, M. and Ghahramani, Z. and Weinberger, K. Q.},
	year = {2013},
	pages = {2787--2795},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/LDL9FB3L/5071-translating-embeddings-for-modeling-multi-rela.html:text/html},
}

@incollection{de_haan_causal_2019,
	title = {Causal {Confusion} in {Imitation} {Learning}},
	url = {http://papers.nips.cc/paper/9343-causal-confusion-in-imitation-learning.pdf},
	urldate = {2020-05-12},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 32},
	publisher = {Curran Associates, Inc.},
	author = {de Haan, Pim and Jayaraman, Dinesh and Levine, Sergey},
	editor = {Wallach, H. and Larochelle, H. and Beygelzimer, A. and Alché-Buc, F. d{\textbackslash}textquotesingle and Fox, E. and Garnett, R.},
	year = {2019},
	pages = {11698--11709},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/LAIHXSJ4/9343-causal-confusion-in-imitation-learning.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/5KMEJJXT/de Haan et al. - 2019 - Causal Confusion in Imitation Learning.pdf:application/pdf},
}

@article{zeng_graphsaint_2020,
	title = {{GraphSAINT}: {Graph} {Sampling} {Based} {Inductive} {Learning} {Method}},
	shorttitle = {{GraphSAINT}},
	url = {http://arxiv.org/abs/1907.04931},
	abstract = {Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).},
	urldate = {2020-05-12},
	journal = {arXiv:1907.04931 [cs, stat]},
	author = {Zeng, Hanqing and Zhou, Hongkuan and Srivastava, Ajitesh and Kannan, Rajgopal and Prasanna, Viktor},
	month = feb,
	year = {2020},
	note = {arXiv: 1907.04931},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Published at ICLR 2020; Code release: github.com/GraphSAINT/GraphSAINT},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/EEQ92H4T/1907.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/L57CNF3H/Zeng et al. - 2020 - GraphSAINT Graph Sampling Based Inductive Learnin.pdf:application/pdf},
}

@inproceedings{xie2016representation,
  title={Representation learning of knowledge graphs with entity descriptions},
  author={Xie, Ruobing and Liu, Zhiyuan and Jia, Jia and Luan, Huanbo and Sun, Maosong},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={30},
  number={1},
  year={2016}
}

@article{kipf_contrastive_2020,
	title = {Contrastive {Learning} of {Structured} {World} {Models}},
	url = {http://arxiv.org/abs/1911.12247},
	abstract = {A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition. Learning such a structured world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network. This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process. We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation. Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.},
	urldate = {2020-05-12},
	journal = {ICLR 2020},
	author = {Kipf, Thomas and van der Pol, Elise and Welling, Max},
	month = jan,
	year = {2020},
	note = {arXiv: 1911.12247},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Artificial Intelligence},
	annote = {Comment: ICLR 2020},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/FC4KPU6T/1911.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/BHFDDRAP/Kipf et al. - 2020 - Contrastive Learning of Structured World Models.pdf:application/pdf},
}

@misc{noauthor_scripties_nodate,
	title = {Scripties - {Bibliotheek} - {Universiteit} van {Amsterdam}},
	url = {https://scripties.uba.uva.nl/},
	abstract = {Scripties van studenten van de Universiteit van Amsterdam.},
	language = {en},
	urldate = {2020-05-10},
	note = {Library Catalog: scripties.uba.uva.nl},
	file = {Snapshot:/home/wolf/Zotero/storage/AVJKKVWN/search.html:text/html},
}

@incollection{kingma_semi-supervised_2014,
	title = {Semi-supervised {Learning} with {Deep} {Generative} {Models}},
	url = {http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf},
	urldate = {2020-05-08},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 27},
	publisher = {Curran Associates, Inc.},
	author = {Kingma, Durk P and Mohamed, Shakir and Jimenez Rezende, Danilo and Welling, Max},
	editor = {Ghahramani, Z. and Welling, M. and Cortes, C. and Lawrence, N. D. and Weinberger, K. Q.},
	year = {2014},
	pages = {3581--3589},
	file = {NIPS Snapshot:/home/wolf/Zotero/storage/5E9M3HFL/5352-semi-supervised-learning-with-deep-generative-models.html:text/html;NIPS Full Text PDF:/home/wolf/Zotero/storage/76WXX2ME/Kingma et al. - 2014 - Semi-supervised Learning with Deep Generative Mode.pdf:application/pdf},
}

@article{brockschmidt_gnn-film_2019,
	title = {{GNN}-{FiLM}: {Graph} {Neural} {Networks} with {Feature}-wise {Linear} {Modulation}},
	shorttitle = {{GNN}-{FiLM}},
	url = {http://arxiv.org/abs/1906.12192},
	abstract = {This paper presents a new Graph Neural Network (GNN) type using feature-wise linear modulation (FiLM). Many standard GNN variants propagate information along the edges of a graph by computing "messages" based only on the representation of the source of each edge. In GNN-FiLM, the representation of the target node of an edge is additionally used to compute a transformation that can be applied to all incoming messages, allowing feature-wise modulation of the passed information. Results of experiments comparing different GNN architectures on three tasks from the literature are presented, based on re-implementations of baseline methods. Hyperparameters for all methods were found using extensive search, yielding somewhat surprising results: differences between baseline models are smaller than reported in the literature. Nonetheless, GNN-FiLM outperforms baseline methods on a regression task on molecular graphs and performs competitively on other tasks.},
	urldate = {2020-05-08},
	journal = {arXiv:1906.12192 [cs, stat]},
	author = {Brockschmidt, Marc},
	month = nov,
	year = {2019},
	note = {arXiv: 1906.12192},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv.org Snapshot:/home/wolf/Zotero/storage/9LMU5ZCW/1906.html:text/html;arXiv Fulltext PDF:/home/wolf/Zotero/storage/T8WD7IT2/Brockschmidt - 2019 - GNN-FiLM Graph Neural Networks with Feature-wise .pdf:application/pdf},
}

@inproceedings{mehta_scalable_2019-1,
	address = {San Francisco, USA},
	series = {{WWW} '19},
	title = {Scalable {Knowledge} {Graph} {Construction} over {Text} using {Deep} {Learning} based {Predicate} {Mapping}},
	isbn = {978-1-4503-6675-5},
	url = {https://doi.org/10.1145/3308560.3317708},
	doi = {10.1145/3308560.3317708},
	abstract = {Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work.},
	urldate = {2020-05-08},
	booktitle = {Companion {Proceedings} of {The} 2019 {World} {Wide} {Web} {Conference}},
	publisher = {Association for Computing Machinery},
	author = {Mehta, Aman and Singhal, Aashay and Karlapalem, Kamalakar},
	month = may,
	year = {2019},
	keywords = {Knowledge Graph, Deep Learning, Predicate Mapping, Scalability, Sentence Simplification},
	pages = {705--713},
	file = {Full Text PDF:/home/wolf/Zotero/storage/9SGG27RC/Mehta et al. - 2019 - Scalable Knowledge Graph Construction over Text us.pdf:application/pdf},
}

@inproceedings{mehta_scalable_2019-2,
	address = {San Francisco, USA},
	title = {Scalable {Knowledge} {Graph} {Construction} over {Text} using {Deep} {Learning} based {Predicate} {Mapping}},
	isbn = {978-1-4503-6675-5},
	url = {http://dl.acm.org/citation.cfm?doid=3308560.3317708},
	doi = {10.1145/3308560.3317708},
	abstract = {Automatic extraction of information from text and its transformation into a structured format is an important goal in both Semantic Web Research and computational linguistics. Knowledge Graphs (KG) serve as an intuitive way to provide structure to unstructured text. A fact in a KG is expressed in the form of a triple which captures entities and their interrelationships (predicates). Multiple triples extracted from text can be semantically identical but they may have a vocabulary gap which could lead to an explosion in the number of redundant triples. Hence, to get rid of this vocabulary gap, there is a need to map triples to a homogeneous namespace. In this work, we present an end-to-end KG construction system, which identifies and extracts entities and relationships from text and maps them to the homogenous DBpedia namespace. For Predicate Mapping, we propose a Deep Learning architecture to model semantic similarity. This mapping step is computation heavy, owing to the large number of triples in DBpedia. We identify and prune unnecessary comparisons to make this step scalable. Our experiments show that the proposed approach is able to construct a richer KG at a significantly lower computation cost with respect to previous work.},
	language = {en},
	urldate = {2020-05-08},
	booktitle = {Companion {Proceedings} of {The} 2019 {World} {Wide} {Web} {Conference} on   - {WWW} '19},
	publisher = {ACM Press},
	author = {Mehta, Aman and Singhal, Aashay and Karlapalem, Kamalakar},
	year = {2019},
	pages = {705--713},
	file = {Mehta et al. - 2019 - Scalable Knowledge Graph Construction over Text us.pdf:/home/wolf/Zotero/storage/2C2BMH3S/Mehta et al. - 2019 - Scalable Knowledge Graph Construction over Text us.pdf:application/pdf},
}

@article{mueller_siamese_nodate,
	title = {Siamese {Recurrent} {Architectures} for {Learning} {Sentence} {Similarity}},
	abstract = {We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide wordembedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding.},
	language = {en},
	author = {Mueller, Jonas and Thyagarajan, Aditya},
	pages = {7},
	file = {Mueller and Thyagarajan - Siamese Recurrent Architectures for Learning Sente.pdf:/home/wolf/Zotero/storage/YWWKGZET/Mueller and Thyagarajan - Siamese Recurrent Architectures for Learning Sente.pdf:application/pdf},
}

@article{shu_amortized_2019,
	title = {Amortized {Inference} {Regularization}},
	url = {http://arxiv.org/abs/1805.08913},
	abstract = {The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. However, it is often overlooked that an overly-expressive inference model can be detrimental to the test set performance of both the amortized posterior approximator and, more importantly, the generative density estimator. In this paper, we leverage the fact that VAEs rely on amortized inference and propose techniques for amortized inference regularization (AIR) that control the smoothness of the inference model. We demonstrate that, by applying AIR, it is possible to improve VAE generalization on both inference and generative performance. Our paper challenges the belief that amortized inference is simply a mechanism for approximating maximum likelihood training and illustrates that regularization of the amortization family provides a new direction for understanding and improving generalization in VAEs.},
	urldate = {2021-01-09},
	journal = {arXiv:1805.08913 [cs, stat]},
	author = {Shu, Rui and Bui, Hung H. and Zhao, Shengjia and Kochenderfer, Mykel J. and Ermon, Stefano},
	month = jan,
	year = {2019},
	note = {arXiv: 1805.08913},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: NeurIPS 2018},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/UESW9MNX/Shu et al. - 2019 - Amortized Inference Regularization.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/UMQXWAF3/1805.html:text/html},
}

@article{chen_variational_2017,
	title = {Variational {Lossy} {Autoencoder}},
	url = {http://arxiv.org/abs/1611.02731},
	abstract = {Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture. In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN. Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only "autoencodes" data in a lossy fashion. In addition, by leveraging autoregressive models as both prior distribution \$p(z)\$ and decoding distribution \$p(x{\textbar}z)\$, we can greatly improve generative modeling performance of VAEs, achieving new state-of-the-art results on MNIST, OMNIGLOT and Caltech-101 Silhouettes density estimation tasks.},
	urldate = {2021-01-09},
	journal = {arXiv:1611.02731 [cs, stat]},
	author = {Chen, Xi and Kingma, Diederik P. and Salimans, Tim and Duan, Yan and Dhariwal, Prafulla and Schulman, John and Sutskever, Ilya and Abbeel, Pieter},
	month = mar,
	year = {2017},
	note = {arXiv: 1611.02731},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Added CIFAR10 experiments; ICLR 2017},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/3IKYQ8V4/Chen et al. - 2017 - Variational Lossy Autoencoder.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/WFFRGZCD/1611.html:text/html},
}

@article{tolstikhin_wasserstein_2019,
	title = {Wasserstein {Auto}-{Encoders}},
	url = {http://arxiv.org/abs/1711.01558},
	abstract = {We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder (VAE). This regularizer encourages the encoded training distribution to match the prior. We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders (AAE). Our experiments show that WAE shares many of the properties of VAEs (stable training, encoder-decoder architecture, nice latent manifold structure) while generating samples of better quality, as measured by the FID score.},
	urldate = {2021-01-09},
	journal = {arXiv:1711.01558 [cs, stat]},
	author = {Tolstikhin, Ilya and Bousquet, Olivier and Gelly, Sylvain and Schoelkopf, Bernhard},
	month = dec,
	year = {2019},
	note = {arXiv: 1711.01558},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: Published at ICLR 2018.. Included much wider hyperparameter sweep: in significant improvements in FIDs on CelebA},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/FZTLIPGL/Tolstikhin et al. - 2019 - Wasserstein Auto-Encoders.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/JR75RGEP/1711.html:text/html},
}

@article{makhzani_adversarial_2016,
	title = {Adversarial {Autoencoders}},
	url = {http://arxiv.org/abs/1511.05644},
	abstract = {In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution. Matching the aggregated posterior to the prior ensures that generating from any part of prior space results in meaningful samples. As a result, the decoder of the adversarial autoencoder learns a deep generative model that maps the imposed prior to the data distribution. We show how the adversarial autoencoder can be used in applications such as semi-supervised classification, disentangling style and content of images, unsupervised clustering, dimensionality reduction and data visualization. We performed experiments on MNIST, Street View House Numbers and Toronto Face datasets and show that adversarial autoencoders achieve competitive results in generative modeling and semi-supervised classification tasks.},
	urldate = {2021-01-09},
	journal = {arXiv:1511.05644 [cs]},
	author = {Makhzani, Alireza and Shlens, Jonathon and Jaitly, Navdeep and Goodfellow, Ian and Frey, Brendan},
	month = may,
	year = {2016},
	note = {arXiv: 1511.05644},
	keywords = {Computer Science - Machine Learning},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/54ER548I/Makhzani et al. - 2016 - Adversarial Autoencoders.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/QPXIYN3E/1511.html:text/html},
}

@article{zhao_infovae_2018,
	title = {{InfoVAE}: {Information} {Maximizing} {Variational} {Autoencoders}},
	shorttitle = {{InfoVAE}},
	url = {http://arxiv.org/abs/1706.02262},
	abstract = {A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for variational autoencoders can lead to inaccurate amortized inference distributions and, in some cases, improving the objective provably degrades the inference quality. In addition, it has been observed that variational autoencoders tend to ignore the latent variables when combined with a decoding distribution that is too flexible. We again identify the cause in existing training criteria and propose a new class of objectives (InfoVAE) that mitigate these problems. We show that our model can significantly improve the quality of the variational posterior and can make effective use of the latent features regardless of the flexibility of the decoding distribution. Through extensive qualitative and quantitative analyses, we demonstrate that our models outperform competing approaches on multiple performance metrics.},
	urldate = {2021-01-09},
	journal = {arXiv:1706.02262 [cs, stat]},
	author = {Zhao, Shengjia and Song, Jiaming and Ermon, Stefano},
	month = may,
	year = {2018},
	note = {arXiv: 1706.02262},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/DSR8SAXT/Zhao et al. - 2018 - InfoVAE Information Maximizing Variational Autoen.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/GQ228WW3/1706.html:text/html},
}

@article{zhao_infovae_2018-1,
	title = {{InfoVAE}: {Information} {Maximizing} {Variational} {Autoencoders}},
	shorttitle = {{InfoVAE}},
	url = {http://arxiv.org/abs/1706.02262},
	abstract = {A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for variational autoencoders can lead to inaccurate amortized inference distributions and, in some cases, improving the objective provably degrades the inference quality. In addition, it has been observed that variational autoencoders tend to ignore the latent variables when combined with a decoding distribution that is too flexible. We again identify the cause in existing training criteria and propose a new class of objectives (InfoVAE) that mitigate these problems. We show that our model can significantly improve the quality of the variational posterior and can make effective use of the latent features regardless of the flexibility of the decoding distribution. Through extensive qualitative and quantitative analyses, we demonstrate that our models outperform competing approaches on multiple performance metrics.},
	urldate = {2021-01-09},
	journal = {arXiv:1706.02262 [cs, stat]},
	author = {Zhao, Shengjia and Song, Jiaming and Ermon, Stefano},
	month = may,
	year = {2018},
	note = {arXiv: 1706.02262},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/FYAAMZVJ/Zhao et al. - 2018 - InfoVAE Information Maximizing Variational Autoen.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/UZY87A5H/1706.html:text/html},
}

@article{dieng_avoiding_2019,
	title = {Avoiding {Latent} {Variable} {Collapse} {With} {Generative} {Skip} {Models}},
	url = {http://arxiv.org/abs/1807.04863},
	abstract = {Variational autoencoders learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the log marginal likelihood. VAEs can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful. Specifically, the lower bound involves an approximate posterior of the latent variables; this posterior "collapses" when it is set equal to the prior, i.e., when the approximate posterior is independent of the data. While VAEs learn good generative models, latent variable collapse prevents them from learning useful representations. In this paper, we propose a simple new way to avoid latent variable collapse by including skip connections in our generative model; these connections enforce strong links between the latent variables and the likelihood function. We study generative skip models both theoretically and empirically. Theoretically, we prove that skip models increase the mutual information between the observations and the inferred latent variables. Empirically, we study images (MNIST and Omniglot) and text (Yahoo). Compared to existing VAE architectures, we show that generative skip models maintain similar predictive performance but lead to less collapse and provide more meaningful representations of the data.},
	urldate = {2021-01-09},
	journal = {arXiv:1807.04863 [cs, stat]},
	author = {Dieng, Adji B. and Kim, Yoon and Rush, Alexander M. and Blei, David M.},
	month = jan,
	year = {2019},
	note = {arXiv: 1807.04863},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
	annote = {Comment: In the Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan. PMLR: Volume 89. An earlier version of this paper was presented at the Workshop on Theoretical Foundations and Applications of Deep Generative Models, ICML, 2018},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/JSZBPLFR/Dieng et al. - 2019 - Avoiding Latent Variable Collapse With Generative .pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/DV35GYLR/1807.html:text/html},
}

@article{park_hierarchical_2018,
	title = {A {Hierarchical} {Latent} {Structure} for {Variational} {Conversation} {Modeling}},
	url = {http://arxiv.org/abs/1804.03424},
	abstract = {Variational autoencoders (VAE) combined with hierarchical RNNs have emerged as a powerful framework for conversation modeling. However, they suffer from the notorious degeneration problem, where the decoders learn to ignore latent variables and reduce to vanilla RNNs. We empirically show that this degeneracy occurs mostly due to two reasons. First, the expressive power of hierarchical RNN decoders is often high enough to model the data using only its decoding distributions without relying on the latent variables. Second, the conditional VAE structure whose generation process is conditioned on a context, makes the range of training targets very sparse; that is, the RNN decoders can easily overfit to the training data ignoring the latent variables. To solve the degeneration problem, we propose a novel model named Variational Hierarchical Conversation RNNs (VHCR), involving two key ideas of (1) using a hierarchical structure of latent variables, and (2) exploiting an utterance drop regularization. With evaluations on two datasets of Cornell Movie Dialog and Ubuntu Dialog Corpus, we show that our VHCR successfully utilizes latent variables and outperforms state-of-the-art models for conversation generation. Moreover, it can perform several new utterance control tasks, thanks to its hierarchical latent structure.},
	urldate = {2021-01-09},
	journal = {arXiv:1804.03424 [cs]},
	author = {Park, Yookoon and Cho, Jaemin and Kim, Gunhee},
	month = apr,
	year = {2018},
	note = {arXiv: 1804.03424},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
	annote = {Comment: Published in NAACL 2018 (Oral)},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/ZZUIA2TT/Park et al. - 2018 - A Hierarchical Latent Structure for Variational Co.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/GSHD4SGI/1804.html:text/html},
}

@article{hoffman_elbo_nodate,
	title = {{ELBO} surgery: yet another way to carve up the variational evidence lower bound},
	abstract = {We rewrite the variational evidence lower bound objective (ELBO) of variational autoencoders in a way that highlights the role of the encoded data distribution. This perspective suggests that to improve our variational bounds we should improve our priors and not just the encoder and decoder.},
	language = {en},
	author = {Hoffman, Matthew D and Johnson, Matthew J},
	pages = {4},
	file = {Hoffman and Johnson - ELBO surgery yet another way to carve up the vari.pdf:/home/wolf/Zotero/storage/24KJX3Q5/Hoffman and Johnson - ELBO surgery yet another way to carve up the vari.pdf:application/pdf},
}

@inproceedings{razavi_preventing_2018,
	title = {Preventing {Posterior} {Collapse} with delta-{VAEs}},
	url = {https://openreview.net/forum?id=BJe0Gn0cY7},
	abstract = {Avoid posterior collapse by lower bounding the rate.},
	language = {en},
	urldate = {2021-01-10},
	author = {Razavi, Ali and Oord, Aaron van den and Poole, Ben and Vinyals, Oriol},
	month = sep,
	year = {2018},
	file = {Full Text PDF:/home/wolf/Zotero/storage/UTDV6CDI/Razavi et al. - 2018 - Preventing Posterior Collapse with delta-VAEs.pdf:application/pdf;Snapshot:/home/wolf/Zotero/storage/CBRABCAC/forum.html:text/html},
}

@article{silver_mastering_2017,
	title = {Mastering the game of {Go} without human knowledge},
	volume = {550},
	copyright = {2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/nature24270},
	doi = {10.1038/nature24270},
	abstract = {A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100–0 against the previously published, champion-defeating AlphaGo.},
	language = {en},
	number = {7676},
	urldate = {2021-01-11},
	journal = {Nature},
	author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and Chen, Yutian and Lillicrap, Timothy and Hui, Fan and Sifre, Laurent and van den Driessche, George and Graepel, Thore and Hassabis, Demis},
	month = oct,
	year = {2017},
	note = {Number: 7676
Publisher: Nature Publishing Group},
	pages = {354--359},
	file = {Submitted Version:/home/wolf/Zotero/storage/EJLKLX2Q/Silver et al. - 2017 - Mastering the game of Go without human knowledge.pdf:application/pdf;Snapshot:/home/wolf/Zotero/storage/ICBP3QC9/nature24270.html:text/html},
}

@online{noauthor_elon_nodate,
  title = {Elon Musk on Twitter},
  author = {Musk, Elon},
  year = {2017},
  month = {Aug},
  url = {https://twitter.com/elonmusk/status/896166762361704450},
  urldate = {2021-01-11}
}

@inproceedings{bollacker_freebase_2008,
	address = {New York, NY, USA},
	series = {{SIGMOD} '08},
	title = {Freebase: a collaboratively created graph database for structuring human knowledge},
	isbn = {978-1-60558-102-6},
	shorttitle = {Freebase},
	url = {https://doi.org/10.1145/1376616.1376746},
	doi = {10.1145/1376616.1376746},
	abstract = {Freebase is a practical, scalable tuple database used to structure general human knowledge. The data in Freebase is collaboratively created, structured, and maintained. Freebase currently contains more than 125,000,000 tuples, more than 4000 types, and more than 7000 properties. Public read/write access to Freebase is allowed through an HTTP-based graph-query API using the Metaweb Query Language (MQL) as a data query and manipulation language. MQL provides an easy-to-use object-oriented interface to the tuple data in Freebase and is designed to facilitate the creation of collaborative, Web-based data-oriented applications.},
	urldate = {2021-01-12},
	booktitle = {Proceedings of the 2008 {ACM} {SIGMOD} international conference on {Management} of data},
	publisher = {Association for Computing Machinery},
	author = {Bollacker, Kurt and Evans, Colin and Paritosh, Praveen and Sturge, Tim and Taylor, Jamie},
	month = jun,
	year = {2008},
	keywords = {collaborative systems, semantic network, tuple store},
	pages = {1247--1250},
}

@article{dettmers_convolutional_2018,
	title = {Convolutional {2D} {Knowledge} {Graph} {Embeddings}},
	url = {http://arxiv.org/abs/1707.01476},
	abstract = {Link prediction for knowledge graphs is the task of predicting missing relationships between entities. Previous work on link prediction has focused on shallow, fast models which can scale to large knowledge graphs. However, these models learn less expressive features than deep, multi-layer models -- which potentially limits performance. In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets. We also show that the model is highly parameter efficient, yielding the same performance as DistMult and R-GCN with 8x and 17x fewer parameters. Analysis of our model suggests that it is particularly effective at modelling nodes with high indegree -- which are common in highly-connected, complex knowledge graphs such as Freebase and YAGO3. In addition, it has been noted that the WN18 and FB15k datasets suffer from test set leakage, due to inverse relations from the training set being present in the test set -- however, the extent of this issue has so far not been quantified. We find this problem to be severe: a simple rule-based model can achieve state-of-the-art results on both WN18 and FB15k. To ensure that models are evaluated on datasets where simply exploiting inverse relations cannot yield competitive results, we investigate and validate several commonly used datasets -- deriving robust variants where necessary. We then perform experiments on these robust datasets for our own and several previously proposed models and find that ConvE achieves state-of-the-art Mean Reciprocal Rank across most datasets.},
	urldate = {2021-01-12},
	journal = {arXiv:1707.01476 [cs]},
	author = {Dettmers, Tim and Minervini, Pasquale and Stenetorp, Pontus and Riedel, Sebastian},
	month = jul,
	year = {2018},
	note = {arXiv: 1707.01476},
	keywords = {Computer Science - Machine Learning},
	annote = {Comment: Extended AAAI2018 paper},
	file = {arXiv Fulltext PDF:/home/wolf/Zotero/storage/EPP4EID9/Dettmers et al. - 2018 - Convolutional 2D Knowledge Graph Embeddings.pdf:application/pdf;arXiv.org Snapshot:/home/wolf/Zotero/storage/93RKI2HU/1707.html:text/html},
}