2020.05.20.txt

==========New Papers==========
1, TITLE:       An Information-Theoretic Approach for Path Planning in Agents with Computational Constraints
http://arxiv.org/abs/2005.09611
AUTHORS:        Daniel T. Larsson ; Dipankar Maity ; Panagiotis Tsiotras
HIGHLIGHT:      In this paper, we develop a framework for path-planning on abstractions that are not provided to the system a-priori but instead emerge as a function of the agent's available computational resources.

2, TITLE:       A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks
http://arxiv.org/abs/2005.09606
AUTHORS:        Angela S. Lin ; Sudha Rao ; Asli Celikyilmaz ; Elnaz Nouri ; Chris Brockett ; Debadeepta Dey ; Bill Dolan
COMMENTS:       This paper has been accepted to be published at ACL 2020
HIGHLIGHT:      We release the Microsoft Research Multimodal Aligned Recipe Corpus containing 150K pairwise alignments between recipes across 4,262 dishes with rich commonsense information.

3, TITLE:       Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization
http://arxiv.org/abs/2005.09624
AUTHORS:        Yueh-Hua Wu ; I-Hau Yeh ; David Hu ; Hong-Yuan Mark Liao
HIGHLIGHT:      The goal of this work is to provide a viable solution based on reinforcement learning for traffic signal control problems.

4, TITLE:       Focus on defocus: bridging the synthetic to real domain gap for depth estimation
http://arxiv.org/abs/2005.09623
AUTHORS:        Maxim Maximov ; Kevin Galim ; Laura Leal-Taixé
HIGHLIGHT:      In this paper, we tackle this issue by using domain invariant defocus blur as direct supervision.

5, TITLE:       Unlocking New York City Crime Insights using Relational Database Embeddings
http://arxiv.org/abs/2005.09617
AUTHORS:        Apoorva Nitsure ; Rajesh Bordawekar ; Jose Neves
HIGHLIGHT:      This paper demonstrates the use of the AI-Powered Database (AI-DB) in identifying non-obvious patterns in crime data that could serve as an aid to predictive policing measures.

6, TITLE:       Identifying Statistical Bias in Dataset Replication
http://arxiv.org/abs/2005.09619
AUTHORS:        Logan Engstrom ; Andrew Ilyas ; Shibani Santurkar ; Dimitris Tsipras ; Jacob Steinhardt ; Aleksander Madry
HIGHLIGHT:      In this work, we present unintuitive yet significant ways in which standard approaches to dataset replication introduce statistical bias, skewing the resulting observations.

7, TITLE:       Embeddings as representation for symbolic music
http://arxiv.org/abs/2005.09406
AUTHORS:        Sebastian Garcia-Valencia
HIGHLIGHT:      In this paper, we experiment with embeddings to represent musical notes from 3 different variations of a dataset and analyze if the model can capture useful musical patterns.

8, TITLE:       MaskFace: multi-task face and landmark detector
http://arxiv.org/abs/2005.09412
AUTHORS:        Dmitry Yashunin ; Tamir Baydasov ; Roman Vlasov
HIGHLIGHT:      In this paper we draw attention to multi-task models solving both tasks simultaneously.

9, TITLE:       Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge
http://arxiv.org/abs/2005.09409
AUTHORS:        Benjamin van Niekerk ; Leanne Nortje ; Herman Kamper
COMMENTS:       5 pages, 3 figures, 2 tables
HIGHLIGHT:      In this paper, we explore vector quantization for acoustic unit discovery.

10, TITLE:      A cognitive based Intrusion detection system
http://arxiv.org/abs/2005.09436
AUTHORS:        Siamak Parhizkari ; Mohammad Bagher Menhaj
COMMENTS:       18 pages, 6 figures
HIGHLIGHT:      To overcome these issues, this paper proposes a new approach based on Deep Neural Network (DNN.

11, TITLE:      Functorial Language Games for Question Answering
http://arxiv.org/abs/2005.09439
AUTHORS:        Giovanni de Felice ; Elena Di Lavore ; Mario Román ; Alexis Toumi
COMMENTS:       14 pages
HIGHLIGHT:      We present some categorical investigations into Wittgenstein's language-games, with applications to game-theoretic pragmatics and question-answering in natural language processing.

12, TITLE:      The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report
http://arxiv.org/abs/2005.09448
AUTHORS:        Daniel Sonntag ; Fabrizio Nunnari ; Hans-Jürgen Profitlich
COMMENTS:       20 pages, 15 figures
HIGHLIGHT:      In this article, we describe its functionalities and the user interfaces to facilitate machine learning from human input.

13, TITLE:      Table Search Using a Deep Contextualized Language Model
http://arxiv.org/abs/2005.09207
AUTHORS:        Zhiyu Chen ; Mohamed Trabelsi ; Jeff Heflin ; Yinan Xu ; Brian D. Davison
COMMENTS:       Accepted at SIGIR 2020 (long)
HIGHLIGHT:      In this paper, we use the deep contextualized language model BERT for the task of ad hoc table retrieval.

14, TITLE:      Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning
http://arxiv.org/abs/2005.09453
AUTHORS:        Zhenhui Ye ; Yining Chen ; Guanghua Song ; Bowei Yang
COMMENTS:       10 pages, 4 figures, submitted to NIPS2020
HIGHLIGHT:      We present a novel technique called Experience Augmentation, which enables a time-efficient and boosted learning based on a fast, fair and thorough exploration to the environment.

15, TITLE:      A Self-ensembling Framework for Semi-supervised Knee Osteoarthritis Localization and Classification with Dual-Consistency
http://arxiv.org/abs/2005.09212
AUTHORS:        Jiayu Huo ; Liping Si ; Xi Ouyang ; Kai Xuan ; Weiwu Yao ; Zhong Xue ; Lichi Zhang ; Qian Wang
HIGHLIGHT:      In this paper, we propose a novel approach for knee OA diagnosis, including severity classification and lesion localization.

16, TITLE:      VigiFlood: evaluating the impact of a change of perspective on flood vigilance
http://arxiv.org/abs/2005.09460
AUTHORS:        Carole Adam
HIGHLIGHT:      We have designed a serious game called Vigiflood, based on a real case study of flash floods hitting the South West of France in October 2018.

17, TITLE:      Privileged Information Dropout in Reinforcement Learning
http://arxiv.org/abs/2005.09220
AUTHORS:        Pierre-Alexandre Kamienny ; Kai Arulkumaran ; Feryal Behbahani ; Wendelin Boehmer ; Shimon Whiteson
HIGHLIGHT:      In this work, we investigate Privileged Information Dropout (\pid) for achieving the latter which can be applied equally to value-based and policy-based RL algorithms.

18, TITLE:      Deep Learning Guided Building Reconstruction from Satellite Imagery-derived Point Clouds
http://arxiv.org/abs/2005.09223
AUTHORS:        Bo Xu ; Xu Zhang ; Zhixin Li ; Matt Leotta ; Shih-Fu Chang ; Jie Shan
HIGHLIGHT:      To address these major challenges, we present a reliable and effective approach for building model reconstruction from the point clouds generated from multi-view satellite images.

19, TITLE:      Holistic Parameteric Reconstruction of Building Models from Point Clouds
http://arxiv.org/abs/2005.09226
AUTHORS:        Zhixin Li ; Wenyuan Zhang ; Jie Shan
HIGHLIGHT:      Fundamentally different from this traditional approach, we propose a holistic parametric reconstruction method which means taking into consideration the entire point clouds of one building simultaneously.

20, TITLE:      The Effect of Moderation on Online Mental Health Conversations
http://arxiv.org/abs/2005.09225
AUTHORS:        David Wadden ; Tal August ; Qisheng Li ; Tim Althoff
COMMENTS:       13 pages, 12 figures. 3 tables
HIGHLIGHT:      In this work, we leveraged a natural experiment, occurring across 200,000 messages from 7,000 conversations hosted on a mental health mobile application, to evaluate the effects of moderation on online mental health discussions.

21, TITLE:      Structural Residual Learning for Single Image Rain Removal
http://arxiv.org/abs/2005.09228
AUTHORS:        Hong Wang ; Yichen Wu ; Qi Xie ; Qian Zhao ; Yong Liang ; Deyu Meng
HIGHLIGHT:      To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed.

22, TITLE:      Two-View Fine-grained Classification of Plant Species
http://arxiv.org/abs/2005.09110
AUTHORS:        Voncarlos M. Araujo ; Alceu S. Britto Jr. ; Luiz E. S. Oliveira ; Alessandro L. Koerich
COMMENTS:       Submitted to Ecological Informatics
HIGHLIGHT:      In this paper, we propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species.

23, TITLE:      Continuous LWE
http://arxiv.org/abs/2005.09595
AUTHORS:        Joan Bruna ; Oded Regev ; Min Jae Song ; Yi Tang
COMMENTS:       28 pages
HIGHLIGHT:      We introduce a continuous analogue of the Learning with Errors (LWE) problem, which we name CLWE.

24, TITLE:      Contextual Embeddings: When Are They Worth It?
http://arxiv.org/abs/2005.09117
AUTHORS:        Simran Arora ; Avner May ; Jian Zhang ; Christopher Ré
COMMENTS:       ACL 2020
HIGHLIGHT:      We study the settings for which deep contextual embeddings (e.g., BERT) give large improvements in performance relative to classic pretrained embeddings (e.g., GloVe), and an even simpler baseline---random word embeddings---focusing on the impact of the training set size and the linguistic properties of the task.

25, TITLE:      Domain Adaptive Relational Reasoning for 3D Multi-Organ Segmentation
http://arxiv.org/abs/2005.09120
AUTHORS:        Shuhao Fu ; Yongyi Lu ; Yan Wang ; Yuyin Zhou ; Wei Shen ; Elliot Fishman ; Alan Yuille
HIGHLIGHT:      In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).

26, TITLE:      GPT-too: A language-model-first approach for AMR-to-text generation
http://arxiv.org/abs/2005.09123
AUTHORS:        Manuel Mager ; Ramon Fernandez Astudillo ; Tahira Naseem ; Md Arafat Sultan ; Young-Suk Lee ; Radu Florian ; Salim Roukos
COMMENTS:       Paper accepted to the Annual Meeting of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring.

27, TITLE:      On the Power of Unambiguity in Büchi Complementation
http://arxiv.org/abs/2005.09125
AUTHORS:        Yong Li ; Moshe Y. Vardi ; Lijun Zhang
HIGHLIGHT:      In this work, we exploit the power of unambiguity for the complementation problem of B\"uchi automata by utilizing reduced run directed acyclic graphs (DAGs) over infinite words, in which each vertex has at most one predecessor.

28, TITLE:      Neural Generation of Dialogue Response Timings
http://arxiv.org/abs/2005.09128
AUTHORS:        Matthew Roddy ; Naomi Harte
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We propose neural models that simulate the distributions of these response offsets, taking into account the response turn as well as the preceding turn.

29, TITLE:      Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection
http://arxiv.org/abs/2005.09372
AUTHORS:        Rituparna Sarkar ; Suvadip Mukherjee ; Elisabeth Labruyère ; Jean-Christophe Olivo-Marin
HIGHLIGHT:      In this regard, we introduce a novel supervised technique for cell segmentation in a multi-task learning paradigm.

30, TITLE:      Improve robustness of DNN for ECG signal classification:a noise-to-signal ratio perspective
http://arxiv.org/abs/2005.09134
AUTHORS:        Linhai Ma ; Liang Liang
COMMENTS:       14 pages, 7 figures
HIGHLIGHT:      In this work, we proposed to improve DNN robustness from the perspective of noise-to-signal ratio (NSR) and developed two methods to minimize NSR during training process.

31, TITLE:      NEJM-enzh: A Parallel Corpus for English-Chinese Translation in the Biomedical Domain
http://arxiv.org/abs/2005.09133
AUTHORS:        Boxiang Liu ; Liang Huang
COMMENTS:       11 pages, 11 figures, and 2 tables
HIGHLIGHT:      In this study, we develop an effective pipeline to acquire and process an English-Chinese parallel corpus, consisting of about 100,000 sentence pairs and 3,000,000 tokens on each side, from the New England Journal of Medicine (NEJM).

32, TITLE:      hidden markov random fields and cuckoo search method for medical image segmentation
http://arxiv.org/abs/2005.09377
AUTHORS:        EL-Hachemi Guerrout ; Ramdane Mahiou ; Dominique Michelucci ; Boukabene Randa ; Ouali Assia
COMMENTS:       5 pages, 2 figures, 8 tables
HIGHLIGHT:      In this paper, we use three cuckoo search algorithm to achieve medical image segmentation.

33, TITLE:      Staying True to Your Word: (How) Can Attention Become Explanation?
http://arxiv.org/abs/2005.09379
AUTHORS:        Martin Tutek ; Jan Šnajder
HIGHLIGHT:      In this paper, we provide an explanation as to why attention has seen rightful critique when used with recurrent networks in sequence classification tasks.

34, TITLE:      Weak-Attention Suppression For Transformer Based Speech Recognition
http://arxiv.org/abs/2005.09137
AUTHORS:        Yangyang Shi ; Yongqiang Wang ; Chunyang Wu ; Christian Fuegen ; Frank Zhang ; Duc Le ; Ching-Feng Yeh ; Michael L. Seltzer
COMMENTS:       submitted to interspeech 2020
HIGHLIGHT:      In this paper, we propose Weak-Attention Suppression (WAS), a method that dynamically induces sparsity in attention probabilities.

35, TITLE:      On Restricting Real-Valued Genotypes in Evolutionary Algorithms
http://arxiv.org/abs/2005.09380
AUTHORS:        Jørgen Nordmoen ; Tønnes Frostad Nygaard ; Eivind Samuelsen ; Kyrre Glette
HIGHLIGHT:      In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values.

36, TITLE:      Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text
http://arxiv.org/abs/2005.09382
AUTHORS:        Felix Hill ; Sona Mokra ; Nathaniel Wong ; Tim Harley
HIGHLIGHT:      Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions.

37, TITLE:      On the Choice of Auxiliary Languages for Improved Sequence Tagging
http://arxiv.org/abs/2005.09389
AUTHORS:        Lukas Lange ; Heike Adel ; Jannik Strötgen
COMMENTS:       RepL4NLP at ACL 2020
HIGHLIGHT:      In this analysis paper, we investigate whether the best auxiliary language can be predicted based on language distances and show that the most related language is not always the best auxiliary language.

38, TITLE:      Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks
http://arxiv.org/abs/2005.09147
AUTHORS:        Linhai Ma ; Liang Liang
COMMENTS:       8 pages, 13 figures
HIGHLIGHT:      In this study, we propose a novel training method, named IMA, to improve the robust-ness of CNN against adversarial noises.

39, TITLE:      Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text
http://arxiv.org/abs/2005.09392
AUTHORS:        Lukas Lange ; Anastasiia Iurshina ; Heike Adel ; Jannik Strötgen
COMMENTS:       RepL4NLP at ACL 2020
HIGHLIGHT:      In this paper, we explore multilingual methods for the extraction of temporal expressions from text and investigate adversarial training for aligning embedding spaces to one common space.

40, TITLE:      Fast, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces
http://arxiv.org/abs/2005.09150
AUTHORS:        Frank Zhang ; Yongqiang Wang ; Xiaohui Zhang ; Chunxi Liu ; Yatharth Saraf ; Geoffrey Zweig
COMMENTS:       submitted to interspeech 2020
HIGHLIGHT:      Fast, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces

41, TITLE:      Enhancing Monotonic Multihead Attention for Streaming ASR
http://arxiv.org/abs/2005.09394
AUTHORS:        Hirofumi Inaguma ; Masato Mimura ; Tatsuya Kawahara
HIGHLIGHT:      To encourage every head to learn alignments properly, we propose HeadDrop regularization by masking out a part of heads stochastically during training.

42, TITLE:      Associating Multi-Scale Receptive Fields for Fine-grained Recognition
http://arxiv.org/abs/2005.09153
AUTHORS:        Zihan Ye ; Fuyuan Hu ; Yin Liu ; Zhenping Xia ; Fan Lyu ; Pengqing Liu
COMMENTS:       Accepted by ICIP2020
HIGHLIGHT:      In this paper, we propose a novel cross-layer non-local (CNL) module to associate multi-scale receptive fields by two operations.

43, TITLE:      Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain
http://arxiv.org/abs/2005.09397
AUTHORS:        Lukas Lange ; Heike Adel ; Jannik Strötgen
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we close this gap by reporting concept extraction performance on automatically anonymized data and investigating joint models for de-identification and concept extraction.

44, TITLE:      Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
http://arxiv.org/abs/2005.09159
AUTHORS:        Hangyu Lin ; Yanwei Fu ; Yu-Gang Jiang ; Xiangyang Xue
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      Particularly, towards the pre-training task, we present a novel Sketch Gestalt Model (SGM) to help train the Sketch-BERT.

45, TITLE:      Adversarial Attacks for Embodied Agents
http://arxiv.org/abs/2005.09161
AUTHORS:        Aishan Liu ; Tairan Huang ; Xianglong Liu ; Yitao Xu ; Yuqing Ma ; Xinyun Chen ; Stephen J. Maybank ; Dacheng Tao
COMMENTS:       17 pages, 9 figures
HIGHLIGHT:      In this work, we take the first step to study adversarial attacks for embodied agents.

46, TITLE:      Learning from a Lightweight Teacher for Efficient Knowledge Distillation
http://arxiv.org/abs/2005.09163
AUTHORS:        Yuang Liu ; Wei Zhang ; Jun Wang
COMMENTS:       11 pages, 3 figures, 9 tables
HIGHLIGHT:      To address the above issues, this paper proposes en efficient knowledge distillation learning framework LW-KD, short for lightweight knowledge distillation.

47, TITLE:      A New Validity Index for Fuzzy-Possibilistic C-Means Clustering
http://arxiv.org/abs/2005.09162
AUTHORS:        Mohammad Hossein Fazel Zarandi ; Shahabeddin Sotudian ; Oscar Castillo
COMMENTS:       The following article has been accepted by Scientia Iranica
HIGHLIGHT:      This paper presents a new validity index for fuzzy-possibilistic c-means clustering called Fuzzy-Possibilistic (FP) index, which works well in the presence of clusters that vary in shape and density.

48, TITLE:      Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies
http://arxiv.org/abs/2005.09165
AUTHORS:        Minhyeok Lee ; Junhee Seok
HIGHLIGHT:      Consequently, in order to make the training of GAN stable, numerous regularization methods have been proposed in recent years.

49, TITLE:      MOTS: Multiple Object Tracking for General Categories Based On Few-Shot Method
http://arxiv.org/abs/2005.09167
AUTHORS:        Xixi Xu ; Chao Lu ; Liang Zhu ; Xiangyang Xue ; Guanxian Chen ; Qi Guo ; Yining Lin ; Zhijian Zhao
COMMENTS:       6 pages
HIGHLIGHT:      It contains two stages in series: In the first stage, we design the self-Adaptive-matching module to perform simple targets matching, which can complete 88.76% assignments without sacrificing performance on MOT16 training set.

50, TITLE:      Retrieving and Highlighting Action with Spatiotemporal Reference
http://arxiv.org/abs/2005.09183
AUTHORS:        Seito Kasai ; Yuchi Ishikawa ; Masaki Hayashi ; Yoshimitsu Aoki ; Kensho Hara ; Hirokatsu Kataoka
COMMENTS:       Accepted to ICIP 2020
HIGHLIGHT:      In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods.

51, TITLE:      Quantifying the Uncertainty of Precision Estimates for Rule based Text Classifiers
http://arxiv.org/abs/2005.09198
AUTHORS:        James Nutaro ; Ozgur Ozmen
HIGHLIGHT:      Quantifying the Uncertainty of Precision Estimates for Rule based Text Classifiers

52, TITLE:      RIOT-POLICE: An implementation of spatial memory safety for the RIOT operating system
http://arxiv.org/abs/2005.09516
AUTHORS:        Sören Tempel ; Tristan Bruns
HIGHLIGHT:      We present an integration of a safe C dialect, Checked C, for the Internet of Things operating system RIOT.

53, TITLE:      Decidability and k-Regular Sequences
http://arxiv.org/abs/2005.09507
AUTHORS:        Daniel Krenn ; Jeffrey Shallit
HIGHLIGHT:      In this paper we consider a number of natural decision problems involving k-regular sequences.

54, TITLE:      Choreographies as Objects
http://arxiv.org/abs/2005.09520
AUTHORS:        Saverio Giallorenzo ; Fabrizio Montesi ; Marco Peressotti
HIGHLIGHT:      We present Choral, the first framework for programming choreographies (multiparty protocols) that builds on top of mainstream programming abstractions: in Choral, choreographies are objects.

55, TITLE:      Toward Automated Classroom Observation: Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate
http://arxiv.org/abs/2005.09525
AUTHORS:        Anand Ramakrishnan ; Brian Zylich ; Erin Ottmar ; Jennifer LoCasale-Crouch ; Jacob Whitehill
HIGHLIGHT:      In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS observation protocol that is widely used in educational research.

56, TITLE:      In-memory Implementation of On-chip Trainable and Scalable ANN for AI/ML Applications
http://arxiv.org/abs/2005.09526
AUTHORS:        Abhash Kumar ; Jawar Singh ; Sai Manohar Beeraka ; Bharat Gupta
HIGHLIGHT:      This paper presents an in-memory computing architecture for ANN enabling artificial intelligence (AI) and machine learning (ML) applications.

57, TITLE:      Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization
http://arxiv.org/abs/2005.09530
AUTHORS:        Peter Karkus ; Anelia Angelova ; Vincent Vanhoucke ; Rico Jonschkowski
COMMENTS:       ICRA 2020
HIGHLIGHT:      We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN).

58, TITLE:      Ultrasound Video Summarization using Deep Reinforcement Learning
http://arxiv.org/abs/2005.09531
AUTHORS:        Tianrui Liu ; Qingjie Meng ; Athanasios Vlontzos ; Jeremy Tan ; Daniel Rueckert ; Bernhard Kainz
COMMENTS:       Accepted by MICCAI'20
HIGHLIGHT:      In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video data.

59, TITLE:      CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
http://arxiv.org/abs/2005.09544
AUTHORS:        Maxim Maximov ; Ismail Elezi ; Laura Leal-Taixé
HIGHLIGHT:      We propose and develop CIAGAN, a model for image and video anonymization based on conditional generative adversarial networks.

60, TITLE:      AdaptiveWeighted Attention Network with Camera Spectral Sensitivity Prior for Spectral Reconstruction from RGB Images
http://arxiv.org/abs/2005.09305
AUTHORS:        Jiaojiao Li ; Chaoxiong Wu ; Rui Song ; Yunsong Li ; Fei Liu
COMMENTS:       The 1st ranking on the Clean track and the 3rd place only 1.59106e-4 more than the 1st on the Real World track of the NTIRE 2020 Spectral Reconstruction Challenge
HIGHLIGHT:      To conquer these issues, we propose a novel adaptive weighted attention network (AWAN) for SR, whose backbone is stacked with multiple dual residual attention blocks (DRAB) decorating with long and short skip connections to form the dual residual learning.

61, TITLE:      A Diverse Clustering Particle Swarm Optimizer for Dynamic Environment: To Locate and Track Multiple Optima
http://arxiv.org/abs/2005.09551
AUTHORS:        Zahid Iqbal ; Waseem Shahzad
COMMENTS:       2018 IEEE Conference on Industrial Electronics and Applications
HIGHLIGHT:      To overcome these limitations/drawbacks, we have proposed a new efficient algorithm to handle the dynamic environment effectively by tracking and locating multiple optima and by improving the diversity and convergence speed of algorithm.

62, TITLE:      Localizing Firearm Carriers by Identifying Human-Object Pairs
http://arxiv.org/abs/2005.09329
AUTHORS:        Abdul Basit ; Muhammad Akhtar Munir ; Mohsen Ali ; Arif Mahmood
COMMENTS:       5 pages, accepted in ICIP2020
HIGHLIGHT:      We present a novel approach to address this problem, by defining human-object interaction (and non-interaction) bounding boxes.

63, TITLE:      A New Training Pipeline for an Improved Neural Transducer
http://arxiv.org/abs/2005.09319
AUTHORS:        Albert Zeyer ; André Merboldt ; Ralf Schlüter ; Hermann Ney
COMMENTS:       submission to Interspeech 2020
HIGHLIGHT:      We compare the original training criterion with the full marginalization over all alignments, to the commonly used maximum approximation, which simplifies, improves and speeds up our training.

64, TITLE:      Dynamic Partial Removal: A Neural Network Heuristic for Large Neighborhood Search
http://arxiv.org/abs/2005.09330
AUTHORS:        Mingxiang Chen ; Lei Gao ; Qichang Chen ; Zhixin Liu
HIGHLIGHT:      This paper presents a novel neural network design that learns the heuristic for Large Neighborhood Search (LNS).

65, TITLE:      TAIP: an anytime algorithm for allocating student teams to internship programs
http://arxiv.org/abs/2005.09331
AUTHORS:        Athina Georgara ; Carles Sierra ; Juan A. Rodríguez-Aguilar
COMMENTS:       10 pages, 7 figures
HIGHLIGHT:      Thereafter, we propose TAIP, a heuristic algorithm that generates an initial team allocation which later on attempts to improve in an iterative process.

66, TITLE:      Investigations on Phoneme-Based End-To-End Speech Recognition
http://arxiv.org/abs/2005.09336
AUTHORS:        Albert Zeyer ; Wei Zhou ; Thomas Ng ; Ralf Schlüter ; Hermann Ney
COMMENTS:       submission to Interspeech 2020
HIGHLIGHT:      For this purpose, we introduce phoneme-based BPE labels.

67, TITLE:      Bridging the Gap Between Training and Inference for Spatio-Temporal Forecasting
http://arxiv.org/abs/2005.09343
AUTHORS:        Hong-Bin Liu ; Ickjai Lee
COMMENTS:       ECAI 2020 Accepted, preprint
HIGHLIGHT:      In this work, we propose a novel curriculum learning based strategy named Temporal Progressive Growing Sampling to effectively bridge the gap between training and inference for spatio-temporal sequence forecasting, by transforming the training process from a fully-supervised manner which utilises all available previous ground-truth values to a less-supervised manner which replaces some of the ground-truth context with generated predictions.

68, TITLE:      Uncertainty Estimation in Deep 2D Echocardiography Segmentation
http://arxiv.org/abs/2005.09349
AUTHORS:        Lavsen Dahal ; Aayush Kafle ; Bishesh Khanal
HIGHLIGHT:      Hence, to gain insight of uncertainty modelling for left ventricular segmentation from US images, we compare three ensembling based uncertainty models quantified using four different metrics (one newly proposed) on state-of-the-art baseline networks using two publicly available echocardiogram datasets.

69, TITLE:      Pegasus: Sound Continuous Invariant Generation
http://arxiv.org/abs/2005.09348
AUTHORS:        Andrew Sogokon ; Stefan Mitsch ; Yong Kiam Tan ; Katherine Cordwell ; André Platzer
COMMENTS:       Extended version of FM'19 conference paper (https://doi.org/10.1007/978-3-030-30942-8_10)
HIGHLIGHT:      We describe some of the architectural aspects of this integration, comment on its methods and challenges, and present an experimental evaluation on a suite of benchmarks.

70, TITLE:      Dynamic Knowledge embedding and tracing
http://arxiv.org/abs/2005.09109
AUTHORS:        Liangbei Xu ; Mark A. Davenport
HIGHLIGHT:      In this paper we propose a novel approach to knowledge tracing that combines techniques from matrix factorization with recent progress in recurrent neural networks (RNNs) to effectively track the state of a student's knowledge.

71, TITLE:      Comparing Transformers and RNNs on predicting human sentence processing data
http://arxiv.org/abs/2005.09471
AUTHORS:        Danny Merkx ; Stefan L. Frank
HIGHLIGHT:      In this paper we train both Transformer and RNN based language models and compare their performance as a model of human sentence processing.

72, TITLE:      An Auto-Context Deformable Registration Network for Infant Brain MRI
http://arxiv.org/abs/2005.09230
AUTHORS:        Dongming Wei ; Sahar Ahmad ; Yunzhi Huang ; Lei Ma ; Qian Wang ; Pew-Thian Yap ; Dinggang Shen
HIGHLIGHT:      In this paper, we propose an infant-dedicated deep registration network that uses the auto-context strategy to gradually refine the deformation fields to obtain highly accurate correspondences.

73, TITLE:      Self-supervised Transfer Learning for Instance Segmentation through Physical Interaction
http://arxiv.org/abs/2005.09484
AUTHORS:        Andreas Eitel ; Nico Hauff ; Wolfram Burgard
COMMENTS:       Extended version and code release of accepted IROS 2019 paper
HIGHLIGHT:      To overcome the time-consuming process of manually labeling data for new environments, we present a transfer learning approach for robots that learn to segment objects by interacting with their environment in a self-supervised manner.

74, TITLE:      On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
http://arxiv.org/abs/2005.09241
AUTHORS:        Damien Teney ; Kushal Kafle ; Robik Shrestha ; Ehsan Abbasnejad ; Christopher Kanan ; Anton van den Hengel
HIGHLIGHT:      We provide short- and long-term solutions to avoid these pitfalls and realize the benefits of OOD evaluation.

75, TITLE:      Built Infrastructure Monitoring and Inspection Using UAVs and Vision-based Algorithms
http://arxiv.org/abs/2005.09486
AUTHORS:        Khai Ky Ly ; Manh Duong Phung
HIGHLIGHT:      This study presents an inspecting system using real-time control unmanned aerial vehicles (UAVs) to investigate structural surfaces.

76, TITLE:      Assertion Detection in Multi-Label Clinical Text using Scope Localization
http://arxiv.org/abs/2005.09246
AUTHORS:        Rajeev Bhatt Ambati ; Ahmed Ada Hanifi ; Ramya Vunikili ; Puneet Sharma ; Oladimeji Farri
HIGHLIGHT:      The state-of-theart methods for assertion detection mostly address this task in the setting of a single assertion label per sentence (text).

77, TITLE:      U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
http://arxiv.org/abs/2005.09007
AUTHORS:        Xuebin Qin ; Zichen Zhang ; Chenyang Huang ; Masood Dehghan ; Osmar R. Zaiane ; Martin Jagersand
COMMENTS:       Accepted in Pattern Recognition 2020
HIGHLIGHT:      In this paper, we design a simple yet powerful deep network architecture, U$^2$-Net, for salient object detection (SOD).

78, TITLE:      Safe Learning for Near Optimal Scheduling
http://arxiv.org/abs/2005.09253
AUTHORS:        Gilles Geeraerts ; Shibashis Guha ; Guillermo A. Pérez ; Jean-François Raskin
HIGHLIGHT:      In this paper, we investigate the combination of synthesis techniques and learning techniques to obtain safe and near optimal schedulers for a preemptible task scheduling problem.

79, TITLE:      Weakest Preexpectation Semantics for Bayesian Inference
http://arxiv.org/abs/2005.09013
AUTHORS:        Marcin Szymczak ; Joost-Pieter Katoen
HIGHLIGHT:      We present a semantics of a probabilistic while-language with soft conditioning and continuous distributions which handles programs diverging with positive probability.

80, TITLE:      RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
http://arxiv.org/abs/2005.09496
AUTHORS:        Sangeeth Reddy ; Minesh Mathew ; Lluis Gomez ; Marcal Rusinol ; Dimosthenis Karatzas. ; C. V. Jawahar
COMMENTS:       to be published in ICRA 2020
HIGHLIGHT:      This paper introduces a new "RoadText-1K" dataset for text in driving videos.

81, TITLE:      Patch Attack for Automatic Check-out
http://arxiv.org/abs/2005.09257
AUTHORS:        Aishan Liu ; Jiakai Wang ; Xianglong Liu ; Chongzhi Zhang ; Bowen Cao ; Hang Yu
HIGHLIGHT:      To address the problem, this paper proposes a bias-based framework to generate class-agnostic universal adversarial patches with strong generalization ability, which exploits both the perceptual and semantic bias of models.

82, TITLE:      Patch based Colour Transfer using SIFT Flow
http://arxiv.org/abs/2005.09015
AUTHORS:        Hana Alghamdi ; Rozenn Dahyot
COMMENTS:       8 pages, 7 figures, 4 tables
HIGHLIGHT:      We propose a new colour transfer method with Optimal Transport (OT) to transfer the colour of a sourceimage to match the colour of a target image of the same scene that may exhibit large motion changes betweenimages.

83, TITLE:      Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition
http://arxiv.org/abs/2005.09256
AUTHORS:        Kohei Matsuura ; Masato Mimura ; Shinsuke Sakai ; Tatsuya Kawahara
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this work, for mitigating this speaker sparsity problem, we propose to convert the whole training speech data and make it sound like the test speaker in order to develop a highly accurate ASR system for this speaker.

84, TITLE:      Cross-lingual Transfer Learning for Dialogue Act Recognition
http://arxiv.org/abs/2005.09260
AUTHORS:        Jiří Martínek ; Christophe Cerisara ; Pavel Král ; Ladislav Lenc
COMMENTS:       Submitted for Interspeech 2020
HIGHLIGHT:      Besides generic contextual information gathered from pre-trained BERT embeddings, our objective is to transfer models trained on a standard English DA corpus to two other languages, German and French, and to potentially very different types of dialogue with different dialogue acts than the standard well-known DA corpora.

85, TITLE:      Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data
http://arxiv.org/abs/2005.09020
AUTHORS:        Anthony C. Constantinou ; Yang Liu ; Kiattikun Chobtham ; Zhigao Guo ; Neville K. Kitson
HIGHLIGHT:      Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades.

86, TITLE:      A Novel Technique Combining Image Processing, Plant Development Properties, and the Hungarian Algorithm, to Improve Leaf Detection in Maize
http://arxiv.org/abs/2005.09022
AUTHORS:        Nazifa Khan ; Oliver A. S. Lyon ; Mark Eramian ; Ian McQuillan
COMMENTS:       to be published in the IEEE CVPR 2020 Workshop Proceedings, and accepted by The 1st International Workshop and Prize Challenge on Agriculture-Vision: Challenges & Opportunities for Computer Vision in Agriculture in conjunction with IEEE/CVF CVPR 2020
HIGHLIGHT:      This research aims to identify the position (and number) of leaves from a temporal sequence of high-quality indoor images consisting of multiple views, focussing in particular of images of maize.

87, TITLE:      On the effectiveness of GAN generated cardiac MRIs for segmentation
http://arxiv.org/abs/2005.09026
AUTHORS:        Youssef Skandarani ; Nathan Painchaud ; Pierre-Marc Jodoin ; Alain Lalande
COMMENTS:       4 pages, Accepted for MIDL 2020
HIGHLIGHT:      In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation.

88, TITLE:      Iterative Pseudo-Labeling for Speech Recognition
http://arxiv.org/abs/2005.09267
AUTHORS:        Qiantong Xu ; Tatiana Likhomanenko ; Jacob Kahn ; Awni Hannun ; Gabriel Synnaeve ; Ronan Collobert
HIGHLIGHT:      We study Iterative Pseudo-Labeling (IPL), a semi-supervised algorithm which efficiently performs multiple iterations of pseudo-labeling on unlabeled data as the acoustic model evolves. Finally, we release a new large in-domain text corpus which does not overlap with the Librispeech training transcriptions to foster research in low-resource, semi-supervised ASR

89, TITLE:      Sham: A DSL for Fast DSLs
http://arxiv.org/abs/2005.09028
AUTHORS:        Rajan Walia ; Chung-chieh Shan ; Sam Tobin-Hochstadt
HIGHLIGHT:      We present Sham, a toolkit designed to enable internal DSLs with high performance.

90, TITLE:      Efficient Image Gallery Representations at Scale Through Multi-Task Learning
http://arxiv.org/abs/2005.09027
AUTHORS:        Benjamin Gutelman ; Pavel Levin
COMMENTS:       Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
HIGHLIGHT:      We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks.

91, TITLE:      Improving Accent Conversion with Reference Encoder and End-To-End Text-To-Speech
http://arxiv.org/abs/2005.09271
AUTHORS:        Wenjie Li ; Benlai Tang ; Xiang Yin ; Yushi Zhao ; Wei Li ; Kang Wang ; Hao Huang ; Yuxuan Wang ; Zejun Ma
HIGHLIGHT:      In this paper, we propose approaches to improving accent conversion applicability, as well as quality.

92, TITLE:      Matching Questions and Answers in Dialogues from Online Forums
http://arxiv.org/abs/2005.09276
AUTHORS:        Qi Jia ; Mengxue Zhang ; Shengyao Zhang ; Kenny Q. Zhu
COMMENTS:       Accepted at ECAI2020
HIGHLIGHT:      This paper presents a QA matching model considering both distance information and dialogue history by two simultaneous attention mechanisms called mutual attention. Because existing dialogue datasets such as the Ubuntu dataset are not suitable for the QA matching task, we further create a dataset with 1,000 labeled dialogues and demonstrate that our proposed model outperforms the state-of-the-art and other strong baselines, particularly for matching long-distance QA pairs.

93, TITLE:      Cross-filter compression for CNN inference acceleration
http://arxiv.org/abs/2005.09034
AUTHORS:        Fuyuan Lyu ; Shien Zhu ; Weichen Liu
HIGHLIGHT:      To address this issue, we propose a new cross-filter compression method that can provide $\sim32\times$ memory savings and $122\times$ speed up in convolution operations.

94, TITLE:      Bayesian Subspace HMM for the Zerospeech 2020 Challenge
http://arxiv.org/abs/2005.09282
AUTHORS:        Bolaji Yusuf ; Lucas Ondel
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper we describe our submission to the Zerospeech 2020 challenge, where the participants are required to discover latent representations from unannotated speech, and to use those representations to perform speech synthesis, with synthesis quality used as a proxy metric for the unit quality.

95, TITLE:      ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes
http://arxiv.org/abs/2005.09284
AUTHORS:        William Caicedo-Torres ; Jairo Gutierrez
HIGHLIGHT:      In this work we show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance.

96, TITLE:      Improving the Effectiveness of Traceability Link Recovery using Hierarchical Bayesian Networks
http://arxiv.org/abs/2005.09046
AUTHORS:        Kevin Moran ; David N. Palacio ; Carlos Bernal-Cárdenas ; Daniel McCrystal ; Denys Poshyvanyk ; Chris Shenefiel ; Jeff Johnson
COMMENTS:       Accepted in the Proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 pages
HIGHLIGHT:      In this paper, we illustrate how these limitations can be overcome through the use of a tailored probabilistic model.

97, TITLE:      Controlled Language and Baby Turing Test for General Conversational Intelligence
http://arxiv.org/abs/2005.09280
AUTHORS:        Anton Kolonin
COMMENTS:       10 pages, 4 figures
HIGHLIGHT:      We describe how the two can be used together to build a general-purpose conversational system such as an intelligent assistant for online media and social network data processing.

98, TITLE:      Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models
http://arxiv.org/abs/2005.09294
AUTHORS:        Martin Kotuliak ; Sandro E. Schoenborn ; Andrei Dan
HIGHLIGHT:      In this paper, we introduce a new category of attacks that create unrestricted adversarial examples for object detection.

99, TITLE:      Translating Video Recordings of Mobile App Usages into Replayable Scenarios
http://arxiv.org/abs/2005.09057
AUTHORS:        Carlos Bernal-Cárdenas ; Nathan Cooper ; Kevin Moran ; Oscar Chaparro ; Andrian Marcus ; Denys Poshyvanyk
COMMENTS:       In proceedings of the 42nd International Conference on Software Engineering (ICSE'20), 13 pages
HIGHLIGHT:      To address these challenges, this paper introduces V2S, a lightweight, automated approach for translating video recordings of Android app usages into replayable scenarios.

100, TITLE:     Question-Driven Summarization of Answers to Consumer Health Questions
http://arxiv.org/abs/2005.09067
AUTHORS:        Max Savery ; Asma Ben Abacha ; Soumya Gayen ; Dina Demner-Fushman
HIGHLIGHT:      Using answers provided by the National Library of Medicine's consumer health question answering system, we present the MEDIQA Answer Summarization dataset, the first summarization collection containing question-driven summaries of answers to consumer health questions.

101, TITLE:     P-SIF: Document Embeddings Using Partition Averaging
http://arxiv.org/abs/2005.09069
AUTHORS:        Vivek Gupta ; Ankit Saw ; Pegah Nokhiz ; Praneeth Netrapalli ; Piyush Rai ; Partha Talukdar
COMMENTS:       15 Pages, 3 Figures, 13 Tables, AAAI 2020, Blog : http://vivgupt.blogspot.com/2019/06/document-vector-estimation-using.html
HIGHLIGHT:      To alleviate this problem, we present P-SIF, a partitioned word averaging model to represent long documents.

102, TITLE:     Dice: Compiling Discrete Probabilistic Programs for Scalable Inference
http://arxiv.org/abs/2005.09089
AUTHORS:        Steven Holtzen ; Guy Van den Broeck ; Todd Millstein
HIGHLIGHT:      In this work, we target scalable inference for an important class of probabilistic programs: those whose probability distributions are discrete.

103, TITLE:     (Re)construing Meaning in NLP
http://arxiv.org/abs/2005.09099
AUTHORS:        Sean Trott ; Tiago Timponi Torrent ; Nancy Chang ; Nathan Schneider
COMMENTS:       ACL 2020 camera-ready
HIGHLIGHT:      In this paper, we engage with an idea largely absent from discussions of meaning in natural language understanding--namely, that the way something is expressed reflects different ways of conceptualizing or construing the information being conveyed.

104, TITLE:     Are All Languages Created Equal in Multilingual BERT?
http://arxiv.org/abs/2005.09093
AUTHORS:        Shijie Wu ; Mark Dredze
COMMENTS:       Repl4NLP Workshop 2020
HIGHLIGHT:      We explore how mBERT performs on a much wider set of languages, focusing on the quality of representation for low-resource languages, measured by within-language performance.



==========Updates to Previous Papers==========
1, TITLE:       Average-Case Lower Bounds for Learning Sparse Mixtures, Robust Estimation and Semirandom Adversaries
http://arxiv.org/abs/1908.06130
AUTHORS:        Matthew Brennan ; Guy Bresler
COMMENTS:       Preliminary version (subsumed by expanded version at arXiv:2005.08099), 65 pages
HIGHLIGHT:      For our universality result, we introduce a new method to perform an algorithmic change of measure tailored to sparse mixtures.

2, TITLE:       Neural Entity Linking on Technical Service Tickets
http://arxiv.org/abs/2005.07604
AUTHORS:        Nadja Kurz ; Felix Hamann ; Adrian Ulges
HIGHLIGHT:      Using an entity linking model based on BERT, a popular transformer network in natural language processing, we show that a neural approach outperforms and complements hand-coded heuristics, with improvements of about 20% top-1 accuracy.

3, TITLE:       Predicting Strategic Behavior from Free Text
http://arxiv.org/abs/2004.02973
AUTHORS:        Omer Ben-Porat ; Sharon Hirsch ; Lital Kuchy ; Guy Elad ; Roi Reichart ; Moshe Tennenholtz
COMMENTS:       Accepted to Journal of Artificial Intelligence Research (JAIR), 2020
HIGHLIGHT:      This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web.

4, TITLE:       Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning
http://arxiv.org/abs/1910.04987
AUTHORS:        Yue Gao ; Yuan Guo ; Zhouhui Lian ; Yingmin Tang ; Jianguo Xiao
COMMENTS:       Accepted by SIGGRAPH Asia 2019, code and datasets: https://hologerry.github.io/AGIS-Net/
HIGHLIGHT:      In this paper, we propose a novel model, AGIS-Net, to transfer both shape and texture styles in one-stage with only a few stylized samples. We also propose a large-scale dataset with Chinese glyph images in various shape and texture styles, rendered from 35 professional-designed artistic fonts with 7,326 characters and 2,460 synthetic artistic fonts with 639 characters, to validate the effectiveness and extendability of our method.

5, TITLE:       Concept Learning in Deep Reinforcement Learning
http://arxiv.org/abs/2005.07870
AUTHORS:        Diego Gomez ; Nicanor Quijano ; Luis Felipe Giraldo
HIGHLIGHT:      Deep reinforcement learning techniques have shown to be a promising path to solve very complex tasks that once were thought to be out of the realm of machines.

6, TITLE:       An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
http://arxiv.org/abs/2004.11001
AUTHORS:        Michael Gadermayr ; Maximilian Tschuchnig ; Laxmi Gupta ; Dorit Merhof ; Nils Krämer ; Daniel Truhn ; Burkhard Gess
COMMENTS:       Submitted to MICCAI'20
HIGHLIGHT:      In this work, we offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss.

7, TITLE:       K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
http://arxiv.org/abs/2005.04120
AUTHORS:        Cheul Young Park ; Narae Cha ; Soowon Kang ; Auk Kim ; Ahsan Habib Khandoker ; Leontios Hadjileontiadis ; Alice Oh ; Yong Jeong ; Uichin Lee
COMMENTS:       20 pages, 4 figures, for associated dataset, see https://doi.org/10.5281/zenodo.3814370
HIGHLIGHT:      K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations

8, TITLE:       Zero-Resource Cross-Domain Named Entity Recognition
http://arxiv.org/abs/2002.05923
AUTHORS:        Zihan Liu ; Genta Indra Winata ; Pascale Fung
COMMENTS:       RepL4NLP 2020
HIGHLIGHT:      Hence, we propose a cross-domain NER model that does not use any external resources.

9, TITLE:       Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Security Videos
http://arxiv.org/abs/2004.11475
AUTHORS:        Mamshad Nayeem Rizve ; Ugur Demir ; Praveen Tirupattur ; Aayush Jung Rana ; Kevin Duarte ; Ishan Dave ; Yogesh Singh Rawat ; Mubarak Shah
COMMENTS:       9 pages
HIGHLIGHT:      In this work we propose Gabriella, a real-time online system to perform activity detection on untrimmed security videos.

10, TITLE:      EfficientPS: Efficient Panoptic Segmentation
http://arxiv.org/abs/2004.02307
AUTHORS:        Rohit Mohan ; Abhinav Valada
COMMENTS:       Ranked # 1 on Cityscapes panoptic segmentation benchmark, ranked # 2 among the published methods on Cityscapes semantic segmentation benchmark, and ranked # 2 among the published methods on Cityscapes instance segmentation benchmark. Demo, code and models are available at https://rl.uni-freiburg.de/research/panoptic
HIGHLIGHT:      In this paper, we introduce the Efficient Panoptic Segmentation (EfficientPS) architecture that consists of a shared backbone which efficiently encodes and fuses semantically rich multi-scale features. Additionally, we introduce the KITTI panoptic segmentation dataset that contains panoptic annotations for the popularly challenging KITTI benchmark.

11, TITLE:      Compositional Transfer in Hierarchical Reinforcement Learning
http://arxiv.org/abs/1906.11228
AUTHORS:        Markus Wulfmeier ; Abbas Abdolmaleki ; Roland Hafner ; Jost Tobias Springenberg ; Michael Neunert ; Tim Hertweck ; Thomas Lampe ; Noah Siegel ; Nicolas Heess ; Martin Riedmiller
COMMENTS:       Robotics Science and Systems 2020
HIGHLIGHT:      We introduce Regularized Hierarchical Policy Optimization (RHPO) to improve data-efficiency for domains with multiple dominant tasks and ultimately reduce required platform time.

12, TITLE:      Active Learning for Coreference Resolution using Discrete Annotation
http://arxiv.org/abs/2004.13671
AUTHORS:        Belinda Z. Li ; Gabriel Stanovsky ; Luke Zettlemoyer
COMMENTS:       12 pages, 7 figures, ACL 2020
HIGHLIGHT:      We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent.

13, TITLE:      Enhancing LGMD's Looming Selectivity for UAVs with Spatial-temporal Distributed Presynaptic Connection
http://arxiv.org/abs/2005.04397
AUTHORS:        Jiannan Zhao ; Hongxin Wang ; Shigang Yue
COMMENTS:       14 pages, 18 figures, 4 tables
HIGHLIGHT:      In this paper, we proposed a new LGMD model for flying robots considering distributed spatial-temporal computing for both excitation and inhibition to enhance the looming selectivity in flying scenes.

14, TITLE:      Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement
http://arxiv.org/abs/2005.05021
AUTHORS:        Youngnam Lee ; Byungsoo Kim ; Dongmin Shin ; JungHoon Kim ; Jineon Baek ; Jinhwan Lee ; Youngduck Choi
HIGHLIGHT:      In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence.

15, TITLE:      Grounding Conversations with Improvised Dialogues
http://arxiv.org/abs/2004.09544
AUTHORS:        Hyundong Cho ; Jonathan May
COMMENTS:       ACL2020 Camera Ready; 9 pages + 5 page appendix
HIGHLIGHT:      We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier.

16, TITLE:      Constant Curvature Graph Convolutional Networks
http://arxiv.org/abs/1911.05076
AUTHORS:        Gregor Bachmann ; Gary Bécigneul ; Octavian-Eugen Ganea
HIGHLIGHT:      Here, we bridge this gap by proposing mathematically grounded generalizations of graph convolutional networks (GCN) to (products of) constant curvature spaces.

17, TITLE:      Derivation of a Constant Velocity Motion Model for Visual Tracking
http://arxiv.org/abs/2005.00844
AUTHORS:        Nathanael L. Baisa
HIGHLIGHT:      In this document, we derive the constant velocity motion model that incorporates sizes of objects that, we think, can help the new researchers to adapt to it very quickly.

18, TITLE:      Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated Driving
http://arxiv.org/abs/2002.00434
AUTHORS:        Ekim Yurtsever ; Linda Capito ; Keith Redmill ; Umit Ozguner
COMMENTS:       6 pages, 5 figures. Accepted for IEEE Intelligent Vehicles Symposium 2020
HIGHLIGHT:      Here we propose a hybrid approach for integrating a path planning pipe into a vision based DRL framework to alleviate the shortcomings of both worlds.

19, TITLE:      Effectiveness of self-supervised pre-training for speech recognition
http://arxiv.org/abs/1911.03912
AUTHORS:        Alexei Baevski ; Michael Auli ; Abdelrahman Mohamed
HIGHLIGHT:      We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization.

20, TITLE:      KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
http://arxiv.org/abs/1912.02805
AUTHORS:        Xingyu Liu ; Rico Jonschkowski ; Anelia Angelova ; Kurt Konolige
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper we forgo using a depth sensor in favor of raw stereo input. To evaluate the performance of our method, we create a dataset of 15 clear objects in five classes, with 48K 3D-keypoint labeled images.

21, TITLE:      Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer
http://arxiv.org/abs/1811.07594
AUTHORS:        J. Olivares-Sánchez ; J. Casanova ; E. Solano ; L. Lamata
HIGHLIGHT:      We present an experimental realization of a measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti cloud quantum computer.

22, TITLE:      Classical linear logic, cobordisms and categorial grammars
http://arxiv.org/abs/1911.03962
AUTHORS:        Sergey Slavnov
COMMENTS:       This is an improved version of the previously posted paper "Classical linear logic, cobordisms and categorical semantics of categorial grammars" with reduced amonunt of category theory, much simplified definitions and a number of examples. Also, a treatment of tree languages is added
HIGHLIGHT:      We propose a categorial grammar based on classical multiplicative linear logic.

23, TITLE:      Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model
http://arxiv.org/abs/2001.05609
AUTHORS:        Silei Xu ; Giovanni Campagna ; Jian Li ; Monica S. Lam
HIGHLIGHT:      This paper proposes Schema2QA, an open-source toolkit that can build a Q&A skill from a database schema, requiring just a few manual annotations on each field.

24, TITLE:      Time-Delay Feedback Neural Network for Fast-Moving Small Target Discrimination Against Complex Dynamic Environments
http://arxiv.org/abs/2001.05846
AUTHORS:        Hongxin Wang ; Huatian Wang ; Jiannan Zhao ; Cheng Hu ; Jigen Peng ; Shigang Yue
COMMENTS:       13 pages, 16 figures
HIGHLIGHT:      In this paper, we propose a STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to lower layers to mediate neural responses.

25, TITLE:      Optimal least-squares solution to the hand-eye calibration problem
http://arxiv.org/abs/2002.10838
AUTHORS:        Amit Dekel ; Linus Härenstam-Nielsen ; Sergio Caccamo
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We propose a least-squares formulation to the noisy hand-eye calibration problem using dual-quaternions, and introduce efficient algorithms to find the exact optimal solution, based on analytic properties of the problem, avoiding non-linear optimization.

26, TITLE:      Learning light field synthesis with Multi-Plane Images: scene encoding as a recurrent segmentation task
http://arxiv.org/abs/2002.05028
AUTHORS:        Tomás Völker ; Guillaume Boisson ; Bertrand Chupeau
COMMENTS:       Accepted to ICIP 2020
HIGHLIGHT:      In this paper we address the problem of view synthesis from large baseline light fields, by turning a sparse set of input views into a Multi-plane Image (MPI).

27, TITLE:      Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
http://arxiv.org/abs/2005.08056
AUTHORS:        Hongyu Gong ; Yelong Shen ; Dian Yu ; Jianshu Chen ; Dong Yu
HIGHLIGHT:      In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer.

28, TITLE:      Personalization of Health Interventions using Cluster-Based Reinforcement Learning
http://arxiv.org/abs/1804.03592
AUTHORS:        Ali el Hassouni ; Mark Hoogendoorn ; Martijn van Otterlo ; Eduardo Barbaro
HIGHLIGHT:      In this paper, we present a cluster-based reinforcement learning approach which learns across groups of users.

29, TITLE:      Root Identification in Minirhizotron Imagery with Multiple Instance Learning
http://arxiv.org/abs/1903.03207
AUTHORS:        Guohao Yu ; Alina Zare ; Hudanyun Sheng ; Roser Matamala ; Joel Reyes-Cabrera ; Felix B. Fritschi ; Thomas E. Juenger
HIGHLIGHT:      In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed.

30, TITLE:      What is Learned in Visually Grounded Neural Syntax Acquisition
http://arxiv.org/abs/2005.01678
AUTHORS:        Noriyuki Kojima ; Hadar Averbuch-Elor ; Alexander M. Rush ; Yoav Artzi
COMMENTS:       In ACL 2020
HIGHLIGHT:      In this analysis, we consider the case study of the Visually Grounded Neural Syntax Learner (Shi et al., 2019), a recent approach for learning syntax from a visual training signal.

31, TITLE:      Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
http://arxiv.org/abs/2005.08094
AUTHORS:        Sharif Amit Kamran ; Alireza Tavakkoli ; Stewart Lee Zuckerbrod
COMMENTS:       \c{opyright} 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      In this paper we propose the use of disease-specific feature representation as a novel architecture comprised of two joint networks -- one for supervised encoding of disease model and the other for producing attention maps in an unsupervised manner to retain disease specific spatial information.

32, TITLE:      Sum-Product Network Decompilation
http://arxiv.org/abs/1912.10092
AUTHORS:        Cory J. Butz ; Jhonatan S. Oliveira ; Robert Peharz
HIGHLIGHT:      In this paper, we fill this gap by proposing SPN2BN, an algorithm that decompiles an SPN into a BN.

33, TITLE:      Local WL Invariance and Hidden Shades of Regularity
http://arxiv.org/abs/2002.04590
AUTHORS:        Frank Fuhlbrück ; Johannes Köbler ; Oleg Verbitsky
COMMENTS:       12 pages, 2 figures, 1 table. Section 5 of the preceding version is moved to arxiv:2005.08887
HIGHLIGHT:      For an input graph $G$, the algorithm determines a canonical coloring of $s$-tuples of vertices of $G$ for each $s$ between 1 and $k$.

34, TITLE:      Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension
http://arxiv.org/abs/2001.05687
AUTHORS:        Kiet Van Nguyen ; Khiem Vinh Tran ; Son T. Luu ; Anh Gia-Tuan Nguyen ; Ngan Luu-Thuy Nguyen
HIGHLIGHT:      In this work, we construct a dataset which consists of 417 Vietnamese texts and 2,783 pairs of multiple-choice questions and answers.

35, TITLE:      Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
http://arxiv.org/abs/2003.14111
AUTHORS:        Ziyu Liu ; Hongwen Zhang ; Zhenghao Chen ; Zhiyong Wang ; Wanli Ouyang
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we present (1) a simple method to disentangle multi-scale graph convolutions and (2) a unified spatial-temporal graph convolutional operator named G3D.

36, TITLE:      Analyzing Information Leakage of Updates to Natural Language Models
http://arxiv.org/abs/1912.07942
AUTHORS:        Marc Brockschmidt ; Boris Köpf ; Olga Ohrimenko ; Andrew Paverd ; Victor Rühle ; Shruti Tople ; Lukas Wutschitz ; Santiago Zanella-Béguelin
HIGHLIGHT:      We propose two new metrics---differential score and differential rank---for analyzing the leakage due to updates of natural language models.

37, TITLE:      Video Logo Retrieval based on local Features
http://arxiv.org/abs/1808.03735
AUTHORS:        Bochen Guan ; Hanrong Ye ; Hong Liu ; William A. Sethares
COMMENTS:       Accepted by ICIP 20. Contact author: Bochen Guan (gbochen@wisc.edu)
HIGHLIGHT:      This paper develops an algorithm called Video Logo Retrieval (VLR), which is an image-to-video retrieval algorithm based on the spatial distribution of local image descriptors that measure the distance between the query image (the logo) and a collection of video images.

38, TITLE:      Visual Memorability for Robotic Interestingness via Unsupervised Online Learning
http://arxiv.org/abs/2005.08829
AUTHORS:        Chen Wang ; Wenshan Wang ; Yuheng Qiu ; Yafei Hu ; Sebastian Scherer
HIGHLIGHT:      In this paper, we aim to solve the problem of interesting scene prediction for mobile robots.

39, TITLE:      MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
http://arxiv.org/abs/2005.08847
AUTHORS:        Xin Liu ; Jiancheng Li ; Jiaqi Wang ; Ziwei Liu
COMMENTS:       Codes and models are available at: https://github.com/open-mmlab/mmfashion
HIGHLIGHT:      We welcome all contributions to this still-growing efforts towards open science: https://github.com/open-mmlab/mmfashion.

40, TITLE:      Human in Events: A Large-Scale Benchmark for Human-centric Video Analysis in Complex Events
http://arxiv.org/abs/2005.04490
AUTHORS:        Weiyao Lin ; Huabin Liu ; Shizhan Liu ; Yuxi Li ; Guo-Jun Qi ; Rui Qian ; Tao Wang ; Nicu Sebe ; Ning Xu ; Hongkai Xiong ; Mubarak Shah
COMMENTS:       Dataset for ACM MM'20 Grand Challenge on Large-scale Human-centric Video Analysis in Complex Events (http://humaninevents.org)
HIGHLIGHT:      To this end, we present a new large-scale dataset, named Human-in-Events or HiEve (human-centric video analysis in complex events), for understanding human motions, poses, and actions in a variety of realistic events, especially crowd & complex events.

41, TITLE:      Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
http://arxiv.org/abs/2005.08874
AUTHORS:        Tobias Huber ; Katharina Weitz ; Elisabeth André ; Ofra Amir
HIGHLIGHT:      In this paper, we combine global and local explanation methods, and evaluate their joint and separate contributions, providing (to the best of our knowledge) the first user study of combined local and global explanations for RL agents.

42, TITLE:      Dynamic Block Matching to assess the longitudinal component of the dense motion field of the carotid artery wall in B-mode ultrasound sequences -- Association with coronary artery disease
http://arxiv.org/abs/1809.01924
AUTHORS:        Guillaume Zahnd ; Kozue Saito ; Kazuyuki Nagatsuka ; Yoshito Otake ; Yoshinobu Sato
HIGHLIGHT:      The aim of this work is to introduce a motion tracking framework able to simultaneously extract the trajectory of a large collection of points spanning the entire exploitable width of the image.

43, TITLE:      The Weisfeiler-Leman Algorithm and Recognition of Graph Properties
http://arxiv.org/abs/2005.08887
AUTHORS:        Frank Fuhlbrück ; Johannes Köbler ; Ilia Ponomarenko ; Oleg Verbitsky
COMMENTS:       24 pages, 2 figures. This paper supersedes Section 5 in the first version of arXiv:2002.04590. This version: a corrected typo in the title
HIGHLIGHT:      We address the applicability of $k$-WL to recognition of graph properties.

44, TITLE:      Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research
http://arxiv.org/abs/2005.08637
AUTHORS:        Xiangjun Peng ; Zhentao Huang ; Xu Sun
COMMENTS:       Conference: ACM CHI Conference on Human Factors in Computing Systems Workshops (CHI'20 Workshops)At: Honolulu, Hawaii, USA URL:https://emergentdatatrails.com
HIGHLIGHT:      In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterize drivers' affective states and driving styles.

45, TITLE:      A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
http://arxiv.org/abs/2004.12823
AUTHORS:        Gianluca Maguolo ; Loris Nanni
HIGHLIGHT:      In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.

46, TITLE:      SpeechBERT: An Audio-and-text Jointly Learned Language Model for End-to-end Spoken Question Answering
http://arxiv.org/abs/1910.11559
AUTHORS:        Yung-Sung Chuang ; Chi-Liang Liu ; Hung-Yi Lee ; Lin-shan Lee
HIGHLIGHT:      Learning from the very successful BERT model for various text processing tasks, here we proposed an audio-and-text jointly learned SpeechBERT model.

47, TITLE:      Good-Enough Compositional Data Augmentation
http://arxiv.org/abs/1904.09545
AUTHORS:        Jacob Andreas
HIGHLIGHT:      We propose a simple data augmentation protocol aimed at providing a compositional inductive bias in conditional and unconditional sequence models.

48, TITLE:      Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
http://arxiv.org/abs/2005.08463
AUTHORS:        Bingyu Liu ; Zhen Zhao ; Zhenpeng Li ; Jianan Jiang ; Yuhong Guo ; Haifeng Shen ; Jieping Ye
HIGHLIGHT:      In this paper, we propose a feature transformation ensemble model with batch spectral regularization and label propagation for the CD-FSL challenge.

49, TITLE:      SPFCN: Select and Prune the Fully Convolutional Networks for Real-time Parking Slot Detection
http://arxiv.org/abs/2003.11337
AUTHORS:        Zhuoping Yu ; Zhong Gao ; Hansheng Chen ; Yuyao Huang
HIGHLIGHT:      In this paper, we proposed a detector using CNN(convolutional neural networks) for faster speed and smaller model size while keeps accuracy.

50, TITLE:      Modeling relation paths for knowledge base completion via joint adversarial training
http://arxiv.org/abs/1810.06033
AUTHORS:        Chen Li ; Xutan Peng ; Shanghang Zhang ; Hao Peng ; Philip S. Yu ; Min He ; Linfeng Du ; Lihong Wang
COMMENTS:       Accepted by Knowledge-Based Systems
HIGHLIGHT:      In this paper, we propose a novel method to explore the intrinsic relationship between the single relation (i.e. 1-hop path) and multi-hop paths between paired entities.

51, TITLE:      Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
http://arxiv.org/abs/1904.07399
AUTHORS:        Xinyao Wang ; Liefeng Bo ; Li Fuxin
COMMENTS:       [v2] Camera-ready version for ICCV 2019. [v3] Corrected AUC(fr10%) on table 2
HIGHLIGHT:      In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems.

52, TITLE:      Reinforced Coloring for End-to-End Instance Segmentation
http://arxiv.org/abs/2005.07058
AUTHORS:        Tuan Tran Anh ; Khoa Nguyen-Tuan ; Tran Minh Quan ; Won-Ki Jeong
HIGHLIGHT:      To exploit the advantages of conventional single-object-per-step segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel.

53, TITLE:      Robust Medical Instrument Segmentation Challenge 2019
http://arxiv.org/abs/2003.10299
AUTHORS:        Tobias Ross ; Annika Reinke ; Peter M. Full ; Martin Wagner ; Hannes Kenngott ; Martin Apitz ; Hellena Hempe ; Diana Mindroc Filimon ; Patrick Scholz ; Thuy Nuong Tran ; Pierangela Bruno ; Pablo Arbeláez ; Gui-Bin Bian ; Sebastian Bodenstedt ; Jon Lindström Bolmgren ; Laura Bravo-Sánchez ; Hua-Bin Chen ; Cristina González ; Dong Guo ; Pål Halvorsen ; Pheng-Ann Heng ; Enes Hosgor ; Zeng-Guang Hou ; Fabian Isensee ; Debesh Jha ; Tingting Jiang ; Yueming Jin ; Kadir Kirtac ; Sabrina Kletz ; Stefan Leger ; Zhixuan Li ; Klaus H. Maier-Hein ; Zhen-Liang Ni ; Michael A. Riegler ; Klaus Schoeffmann ; Ruohua Shi ; Stefanie Speidel ; Michael Stenzel ; Isabell Twick ; Gutai Wang ; Jiacheng Wang ; Liansheng Wang ; Lu Wang ; Yujie Zhang ; Yan-Jie Zhou ; Lei Zhu ; Manuel Wiesenfarth ; Annette Kopp-Schneider ; Beat P. Müller-Stich ; Lena Maier-Hein
COMMENTS:       A pre-print
HIGHLIGHT:      Robust Medical Instrument Segmentation Challenge 2019

54, TITLE:      Shortcut Learning in Deep Neural Networks
http://arxiv.org/abs/2004.07780
AUTHORS:        Robert Geirhos ; Jörn-Henrik Jacobsen ; Claudio Michaelis ; Richard Zemel ; Wieland Brendel ; Matthias Bethge ; Felix A. Wichmann
COMMENTS:       perspective article
HIGHLIGHT:      In this perspective we seek to distil how many of deep learning's problem can be seen as different symptoms of the same underlying problem: shortcut learning. Based on these observations, we develop a set of recommendations for model interpretation and benchmarking, highlighting recent advances in machine learning to improve robustness and transferability from the lab to real-world applications.

55, TITLE:      ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients
http://arxiv.org/abs/1909.02068
AUTHORS:        Ran Xu ; Rakesh Kumar ; Pengcheng Wang ; Peter Bai ; Ganga Meghanath ; Somali Chaterji ; Subrata Mitra ; Saurabh Bagchi
HIGHLIGHT:      In this paper we introduce ApproxNet, a video analytics system for embedded or mobile clients (which we collectively refer to as ``sensor devices'').

56, TITLE:      A Fixation-based 360° Benchmark Dataset for Salient Object Detection
http://arxiv.org/abs/2001.07960
AUTHORS:        Yi Zhang ; Lu Zhang ; Wassim Hamidouche ; Olivier Deforges
COMMENTS:       5 pages, 5 figures, accepted by ICIP2020
HIGHLIGHT:      Toward this end, we collect 107 equirectangular panoramas with challenging scenes and multiple object classes.

57, TITLE:      A Survey of Adversarial Learning on Graphs
http://arxiv.org/abs/2003.05730
AUTHORS:        Liang Chen ; Jintang Li ; Jiaying Peng ; Tao Xie ; Zengxu Cao ; Kun Xu ; Xiangnan He ; Zibin Zheng
COMMENTS:       TKDD under review
HIGHLIGHT:      Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction and graph clustering.

58, TITLE:      Reinforcement Learning in Non-Stationary Environments
http://arxiv.org/abs/1905.03970
AUTHORS:        Sindhu Padakandla ; Prabuchandran K. J ; Shalabh Bhatnagar
HIGHLIGHT:      In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment.

59, TITLE:      Gems of Corrado Böhm
http://arxiv.org/abs/1812.02243
AUTHORS:        Henk P. Barendregt
COMMENTS:       30 pages. Paper submitted to the special issue of Logical Methods in Computer Science dedicated to the memory of Corrado B\"ohm
HIGHLIGHT:      Gems of Corrado Böhm

60, TITLE:      Fictitious Play Outperforms Counterfactual Regret Minimization
http://arxiv.org/abs/2001.11165
AUTHORS:        Sam Ganzfried
HIGHLIGHT:      We compare the performance of two popular iterative algorithms, fictitious play and counterfactual regret minimization, in approximating Nash equilibrium in multiplayer games.

61, TITLE:      Multi-task Learning for Low-resource Second Language Acquisition Modeling
http://arxiv.org/abs/1908.09283
AUTHORS:        Yong Hu ; Heyan Huang ; Tian Lan ; Xiaochi Wei ; Yuxiang Nie ; Jiarui Qi ; Liner Yang ; Xian-Ling Mao
HIGHLIGHT:      Inspired by this idea, in this paper, we propose a novel SLA modeling method, which learns the latent common patterns among different language-learning datasets by multi-task learning and are further applied to improving the prediction performance in low-resource scenarios.

62, TITLE:      Category Trees
http://arxiv.org/abs/1811.02617
AUTHORS:        Kieran Greer
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:1711.07042
HIGHLIGHT:      This paper presents a batch classifier that has been improved from the earlier version and fixed a mistake in the earlier paper.

63, TITLE:      A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
http://arxiv.org/abs/1904.05730
AUTHORS:        Lichao Mou ; Yuansheng Hua ; Xiao Xiang Zhu
COMMENTS:       CVPR2019
HIGHLIGHT:      In this work, we introduce two simple yet effective network units, the spatial relation module and the channel relation module, to learn and reason about global relationships between any two spatial positions or feature maps, and then produce relation-augmented feature representations.

64, TITLE:      Generalized Label Enhancement with Sample Correlations
http://arxiv.org/abs/2004.03104
AUTHORS:        Qinghai Zheng ; Jihua Zhu ; Haoyu Tang ; Xinyuan Liu ; Zhongyu Li ; Huimin Lu
HIGHLIGHT:      To handle this problem, we propose two novel label enhancement methods, i.e., Label Enhancement with Sample Correlations (LESC) and generalized Label Enhancement with Sample Correlations (gLESC).

65, TITLE:      ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis
http://arxiv.org/abs/1907.05562
AUTHORS:        Yazhou Zhang ; Lingling Song ; Dawei Song ; Peng Guo ; Junwei Zhang ; Peng Zhang
COMMENTS:       Withdrawn by arXiv administration due to policy violation
HIGHLIGHT:      In this paper, we present a new conversational emotion database that we have created and made publically available, namely ScenarioSA.

66, TITLE:      Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction
http://arxiv.org/abs/1911.05845
AUTHORS:        Christopher M. Sandino ; Peng Lai ; Shreyas S. Vasanawala ; Joseph Y. Cheng
COMMENTS:       29 pages, 9 figures, 1 table, 7 supplementary videos, Submitted to Magnetic Resonance in Medicine
HIGHLIGHT:      Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction

67, TITLE:      Noisy Differentiable Architecture Search
http://arxiv.org/abs/2005.03566
AUTHORS:        Xiangxiang Chu ; Bo Zhang ; Xudong Li
COMMENTS:       Make use of noise to address collapse from excessive skip connections in DARTS
HIGHLIGHT:      Noisy Differentiable Architecture Search

68, TITLE:      Towards Fair and Privacy-Preserving Federated Deep Models
http://arxiv.org/abs/1906.01167
AUTHORS:        Lingjuan Lyu ; Jiangshan Yu ; Karthik Nandakumar ; Yitong Li ; Xingjun Ma ; Jiong Jin ; Han Yu ; Kee Siong Ng
COMMENTS:       Accepted for publication in TPDS
HIGHLIGHT:      To address these issues, we propose a decentralized Fair and Privacy-Preserving Deep Learning (FPPDL) framework to incorporate fairness into federated deep learning models.