2020.07.08.txt

==========New Papers==========
1, TITLE:       DAM: Deliberation, Abandon and Memory Networks for Generating Detailed and Non-repetitive Responses in Visual Dialogue
http://arxiv.org/abs/2007.03310
AUTHORS:        Xiaoze Jiang ; Jing Yu ; Yajing Sun ; Zengchang Qin ; Zihao Zhu ; Yue Hu ; Qi Wu
COMMENTS:       Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence)
HIGHLIGHT:      In this paper, we propose a novel generative decoding architecture to generate high-quality responses, which moves away from decoding the whole encoded semantics towards the design that advocates both transparency and flexibility.

2, TITLE:       Spatial Semantic Embedding Network: Fast 3D Instance Segmentation with Deep Metric Learning
http://arxiv.org/abs/2007.03169
AUTHORS:        Dongsu Zhang ; Junha Chun ; Sang Kyun Cha ; Young Min Kim
HIGHLIGHT:      We propose spatial semantic embedding network (SSEN), a simple, yet efficient algorithm for 3D instance segmentation using deep metric learning.

3, TITLE:       Exploring Heterogeneous Information Networks via Pre-Training
http://arxiv.org/abs/2007.03184
AUTHORS:        Yang Fang ; Xiang Zhao ; Weidong Xiao
HIGHLIGHT:      In this paper, we propose a pre-training and fine-tuning framework PF-HIN to capture the features of a HIN.

4, TITLE:       Do Transformers Need Deep Long-Range Memory
http://arxiv.org/abs/2007.03356
AUTHORS:        Jack W. Rae ; Ali Razavi
COMMENTS:       published at 58th Annual Meeting of the Association for Computational Linguistics. 6 pages, 4 figures, 1 table
HIGHLIGHT:      Deep attention models have advanced the modelling of sequential data across many domains.

5, TITLE:       Learning and Reasoning with the Graph Structure Representation in Robotic Surgery
http://arxiv.org/abs/2007.03357
AUTHORS:        Mobarakol Islam ; Lalithkumar Seenivasan ; Lim Chwee Ming ; Hongliang Ren
COMMENTS:       Accepted in MICCAI 2020
HIGHLIGHT:      For this purpose, we develop an approach to generate the scene graph and predict surgical interactions between instruments and surgical region of interest (ROI) during robot-assisted surgery.

6, TITLE:       GOLD-NAS: Gradual, One-Level, Differentiable
http://arxiv.org/abs/2007.03331
AUTHORS:        Kaifeng Bi ; Lingxi Xie ; Xin Chen ; Longhui Wei ; Qi Tian
COMMENTS:       14 pages, 5 figures
HIGHLIGHT:      In this paper, we first relax these manually designed constraints and enlarge the search space to contain more than $10^{160}$ candidates.

7, TITLE:       Provably Safe PAC-MDP Exploration Using Analogies
http://arxiv.org/abs/2007.03574
AUTHORS:        Melrose Roderick ; Vaishnavh Nagarajan ; J. Zico Kolter
COMMENTS:       9 pages, 3 figures
HIGHLIGHT:      Addressing this gap, we propose Analogous Safe-state Exploration (ASE), an algorithm for provably safe exploration in MDPs with unknown, stochastic dynamics.

8, TITLE:       Structured (De)composable Representations Trained with Neural Networks
http://arxiv.org/abs/2007.03325
AUTHORS:        Graham Spinks ; Marie-Francine Moens
HIGHLIGHT:      The paper proposes a novel technique for representing templates and instances of concept classes.

9, TITLE:       Are spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors?
http://arxiv.org/abs/2007.03397
AUTHORS:        Roberto Casula ; Giulia Orrù ; Daniele Angioni ; Xiaoyi Feng ; Gian Luca Marcialis ; Fabio Roli
COMMENTS:       Accepted for the 25th International Conference on Pattern Recognition (ICPR 2020)
HIGHLIGHT:      In this paper, we focus on using snapshot pictures of latent fingerprints. To this aim, we collected a novel data set of live and spoof images fabricated with snapshot pictures of latent fingerprints.

10, TITLE:      Can GAN Generated Morphs Threaten Face Recognition Systems Equally as Landmark Based Morphs? -- Vulnerability and Detection
http://arxiv.org/abs/2007.03621
AUTHORS:        Sushma Venkatesh ; Haoyu Zhang ; Raghavendra Ramachandra ; Kiran Raja ; Naser Damer ; Christoph Busch
COMMENTS:       Accepted in IWBF 2020
HIGHLIGHT:      In this paper, we propose a new framework for generating face morphs using a newer Generative Adversarial Network (GAN) - StyleGAN.

11, TITLE:      Discretization-Aware Architecture Search
http://arxiv.org/abs/2007.03154
AUTHORS:        Yunjie Tian ; Chang Liu ; Lingxi Xie ; Jianbin Jiao ; Qixiang Ye
COMMENTS:       14 pages, 7 figures
HIGHLIGHT:      This paper presents discretization-aware architecture search (DA\textsuperscript{2}S), with the core idea being adding a loss term to push the super-network towards the configuration of desired topology, so that the accuracy loss brought by discretization is largely alleviated.

12, TITLE:      What Gives the Answer Away? Question Answering Bias Analysis on Video QA Datasets
http://arxiv.org/abs/2007.03626
AUTHORS:        Jianing Yang ; Yuying Zhu ; Yongxin Wang ; Ruitao Yi ; Amir Zadeh ; Louis-Philippe Morency
HIGHLIGHT:      In this paper, we analyze QA biases in popular video question answering datasets and discover pretrained language models can answer 37-48% questions correctly without using any multimodal context information, far exceeding the 20% random guess baseline for 5-choose-1 multiple-choice questions.

13, TITLE:      An Integer Programming Approach to Deep Neural Networks with Binary Activation Functions
http://arxiv.org/abs/2007.03326
AUTHORS:        Bubacarr Bah ; Jannis Kurtz
HIGHLIGHT:      We implemented our methods on random and real datasets and show that the heuristic version of the BDNN outperforms classical deep neural networks on the Breast Cancer Wisconsin dataset while performing worse on random data.

14, TITLE:      Guided Exploration with Proximal Policy Optimization using a Single Demonstration
http://arxiv.org/abs/2007.03328
AUTHORS:        Gabriele Libardi ; Gianni De Fabritiis
HIGHLIGHT:      Critically, the algorithm proposed in this article uses a single human demonstration to solve hard-exploration problems.

15, TITLE:      Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
http://arxiv.org/abs/2007.03639
AUTHORS:        Parth Kothari ; Sven Kreiss ; Alexandre Alahi
COMMENTS:       27 pages
HIGHLIGHT:      In this work, we cast the problem of human trajectory forecasting as learning a representation of human social interactions.

16, TITLE:      SaADB: A Self-attention Guided ADB Network for Person Re-identification
http://arxiv.org/abs/2007.03584
AUTHORS:        Bo Jiang ; Sheng Wang ; Xiao Wang ; Aihua Zheng
COMMENTS:       Under Review
HIGHLIGHT:      In this paper, we propose a novel Self-attention guided Adaptive DropBlock network (SaADB) for person re-ID which can \textbf{adaptively} erase the most discriminative regions.

17, TITLE:      Self domain adapted network
http://arxiv.org/abs/2007.03162
AUTHORS:        Yufan He ; Aaron Carass ; Lianrui Zuo ; Blake E. Dewey ; Jerry L. Prince
COMMENTS:       early accept in miccai2020
HIGHLIGHT:      In this paper, we propose a novel self domain adapted network (SDA-Net) that can rapidly adapt itself to a single test subject at the testing stage, without using extra data or training a UDA model.

18, TITLE:      Streaming Complexity of SVMs
http://arxiv.org/abs/2007.03633
AUTHORS:        Alexandr Andoni ; Collin Burns ; Yi Li ; Sepideh Mahabadi ; David P. Woodruff
COMMENTS:       APPROX 2020
HIGHLIGHT:      For related problems, better streaming algorithms are only known for smooth functions, unlike the SVM objective that we focus on in this work.

19, TITLE:      Using Semantic Web Services for AI-Based Research in Industry 4.0
http://arxiv.org/abs/2007.03580
AUTHORS:        Lukas Malburg ; Patrick Klein ; Ralph Bergmann
COMMENTS:       Submitted to ISWC 2020
HIGHLIGHT:      In this paper, we present semantic web services for AI-based research in Industry 4.0.

20, TITLE:      Expressiveness of SETAFs and Support-Free ADFs under 3-valued Semantics
http://arxiv.org/abs/2007.03581
AUTHORS:        Wolfgang Dvořák ; Atefeh Keshavarzi Zafarghandi ; Stefan Woltran
HIGHLIGHT:      The aim of the paper is to shed light on the relation between these two different approaches.

21, TITLE:      Scribble-based Domain Adaptation via Co-segmentation
http://arxiv.org/abs/2007.03632
AUTHORS:        Reuben Dorent ; Samuel Joutard ; Jonathan Shapey ; Sotirios Bisdas ; Neil Kitchen ; Robert Bradford ; Shakeel Saeed ; Marc Modat ; Sebastien Ourselin ; Tom Vercauteren
COMMENTS:       Accepted at MICCAI 2020
HIGHLIGHT:      In this work, we propose a novel weakly-supervised method.

22, TITLE:      Lower Bounds for XOR of Forrelations
http://arxiv.org/abs/2007.03631
AUTHORS:        Uma Girish ; Ran Raz ; Wei Zhan
HIGHLIGHT:      To achieve separations when the classical protocol has smaller advantage, we study in this work the XOR of $k$ independent copies of the Forrelation function (where $k\ll N$).

23, TITLE:      A Vision-based Social Distance and Critical Density Detection System for COVID-19
http://arxiv.org/abs/2007.03578
AUTHORS:        Dongfang Yang ; Ekim Yurtsever ; Vishnu Renganathan ; Keith A. Redmill ; Ümit Özgüner
HIGHLIGHT:      Against this backdrop, we propose using a monocular camera and deep learning-based real-time object detectors to measure social distancing.

24, TITLE:      Fast Perturbative Algorithm Configurators
http://arxiv.org/abs/2007.03336
AUTHORS:        George T. Hall ; Pietro Simone Oliveto ; Dirk Sudholt
COMMENTS:       To appear at PPSN 2020
HIGHLIGHT:      In this paper we prove a linear lower bound on the expected time to optimise any parameter tuning problem for ParamRLS, ParamILS as well as for larger classes of algorithm configurators.

25, TITLE:      The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
http://arxiv.org/abs/2007.03158
AUTHORS:        Harm van Seijen ; Hadi Nekoei ; Evan Racah ; Sarath Chandar
HIGHLIGHT:      To address this, we introduce an experimental setup to evaluate model-based behavior of RL methods, inspired by work from neuroscience on detecting model-based behavior in humans and animals.

26, TITLE:      Monitoring Browsing Behavior of Customers in Retail Stores via RFID Imaging
http://arxiv.org/abs/2007.03600
AUTHORS:        Kamran Ali ; Alex X. Liu ; Eugene Chai ; Karthik Sundaresan
HIGHLIGHT:      In this paper, we propose to use commercial off-the-shelf (COTS) monostatic RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags) to monitor browsing activity of customers in front of display items in places such as retail stores.

27, TITLE:      HKR For Handwritten Kazakh & Russian Database
http://arxiv.org/abs/2007.03579
AUTHORS:        Daniyar Nurseitov ; Kairat Bostanbekov ; Daniyar Kurmankhojayev ; Anel Alimova ; Abdelrahman Abdallah
HIGHLIGHT:      In this paper, we present a new Russian and Kazakh database (with about 95% of Russian and 5% of Kazakh words/sentences respectively) for offline handwriting recognition, A few pre-processing and segmentation procedures have been developed together with the database.

28, TITLE:      Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts
http://arxiv.org/abs/2007.03338
AUTHORS:        Marzieh Heidari ; Mehdi Ghatee ; Ahmad Nickabadi ; Arash Pourhasan Nezhad
COMMENTS:       13 pages, 4 figures and 5 tables, extracted from an MSc thesis in the Amirkabir University of Technology, Tehran, Iran
HIGHLIGHT:      To validate this captioning model, we use Microsoft COCO which is a standard factual image caption dataset.

29, TITLE:      Resolving Head-On Conflicts for Multi-Agent Path Finding with Conflict-Based Search
http://arxiv.org/abs/2007.03575
AUTHORS:        Lun Yang
HIGHLIGHT:      This paper introduces a new technique, namely the head-on technique that finds out such conflicts, so they can be processed more efficiently by resolving the conflict with the potential conflict all together in one split.

30, TITLE:      Location Sensitive Image Retrieval and Tagging
http://arxiv.org/abs/2007.03375
AUTHORS:        Raul Gomez ; Jaume Gibert ; Lluis Gomez ; Dimosthenis Karatzas
HIGHLIGHT:      In this work, we address the task of image retrieval related to a given tag conditioned on a certain location on Earth.

31, TITLE:      Learning to learn generative programs with Memoised Wake-Sleep
http://arxiv.org/abs/2007.03132
AUTHORS:        Luke B. Hewitt ; Tuan Anh Le ; Joshua B. Tenenbaum
HIGHLIGHT:      To tackle the challenge of performing program induction as an 'inner-loop' to learning, we propose the Memoised Wake-Sleep (MWS) algorithm, which extends Wake Sleep by explicitly storing and reusing the best programs discovered by the inference network throughout training.

32, TITLE:      Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening
http://arxiv.org/abs/2007.03373
AUTHORS:        Louis Béthune ; Yacouba Kaloga ; Pierre Borgnat ; Aurélien Garivier ; Amaury Habrard
COMMENTS:       17 pages, 15 figures, submitted
HIGHLIGHT:      We propose a novel algorithm for unsupervised graph representation learning with attributed graphs.

33, TITLE:      Calibrated BatchNorm: Improving Robustness Against Noisy Weights in Neural Networks
http://arxiv.org/abs/2007.03230
AUTHORS:        Li-Huang Tsai ; Shih-Chieh Chang ; Yu-Ting Chen ; Jia-Yu Pan ; Wei Wei ; Da-Cheng Juan
COMMENTS:       6 pages, 1 figure
HIGHLIGHT:      In this paper, we propose to recalculate the statistics of the batch normalization layers to calibrate the biased distributions during the inference phase.

34, TITLE:      Deep Learning for Apple Diseases: Classification and Identification
http://arxiv.org/abs/2007.02980
AUTHORS:        Asif Iqbal Khan ; SMK Quadri ; Saba Banday
HIGHLIGHT:      In this study, we propose a deep learning based approach for identification and classification of apple diseases.

35, TITLE:      Extracting the fundamental diagram from aerial footage
http://arxiv.org/abs/2007.03227
AUTHORS:        Rafael Makrigiorgis ; Panayiotis Kolios ; Stelios Timotheou ; Theocharis Theocharides ; Christos G. Panayiotou
COMMENTS:       5 pages, 7 figures, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring)
HIGHLIGHT:      At large, this system behavior is characterized through the fundamental diagram of a road segment, a region or the network.In this paper we devise an innovative way to obtain the fundamental diagram through aerial footage obtained from drone platforms.

36, TITLE:      Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN
http://arxiv.org/abs/2007.03480
AUTHORS:        Junghyun Lee ; Jawook Gu ; Jong Chul Ye
HIGHLIGHT:      To address this, here we propose a much simpler and much effective unsupervised MAR method for CT.

37, TITLE:      Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters
http://arxiv.org/abs/2007.03001
AUTHORS:        Vineel Pratap ; Anuroop Sriram ; Paden Tomasello ; Awni Hannun ; Vitaliy Liptchinsky ; Gabriel Synnaeve ; Ronan Collobert
HIGHLIGHT:      We compare three variants of multilingual training from a single joint model without knowing the input language, to using this information, to multiple heads (one per language cluster).

38, TITLE:      A Term-Rewriting Semantics for Imperative Style Programming
http://arxiv.org/abs/2007.03075
AUTHORS:        David Plaisted ; Lee Barnett
COMMENTS:       This paper was submitted to FSCD 2020 on December 24, 2019
HIGHLIGHT:      We define a term rewriting based abstract programming language with an imperative style and a precise semantics allowing programs to be translatable into efficient imperative languages, to obtain proofs of correctness together with efficient execution.

39, TITLE:      Learning Model-Blind Temporal Denoisers without Ground Truths
http://arxiv.org/abs/2007.03241
AUTHORS:        Bichuan Guo ; Jiangtao Wen ; Zhen Xia ; Shan Liu ; Yuxing Han
COMMENTS:       17 pages, 6 figures
HIGHLIGHT:      In this paper, we propose a general framework for video denoising networks that successfully addresses these challenges.

40, TITLE:      Kernel Stein Generative Modeling
http://arxiv.org/abs/2007.03074
AUTHORS:        Wei-Cheng Chang ; Chun-Liang Li ; Youssef Mroueh ; Yiming Yang
HIGHLIGHT:      The goal of this work is to study high dimensional inference with SVGD.

41, TITLE:      Generative Model-Based Loss to the Rescue: A Method to Overcome Annotation Errors for Depth-Based Hand Pose Estimation
http://arxiv.org/abs/2007.03073
AUTHORS:        Jiayi Wang ; Franziska Mueller ; Florian Bernard ; Christian Theobalt
HIGHLIGHT:      We propose to use a model-based generative loss for training hand pose estimators on depth images based on a volumetric hand model.

42, TITLE:      Wasserstein Distances for Stereo Disparity Estimation
http://arxiv.org/abs/2007.03085
AUTHORS:        Divyansh Garg ; Yan Wang ; Bharath Hariharan ; Mark Campbell ; Kilian Q. Weinberger ; Wei-Lun Chao
HIGHLIGHT:      We address these issues using a new neural network architecture that is capable of outputting arbitrary depth values, and a new loss function that is derived from the Wasserstein distance between the true and the predicted distributions.

43, TITLE:      Learning Branching Heuristics for Propositional Model Counting
http://arxiv.org/abs/2007.03204
AUTHORS:        Pashootan Vaezipoor ; Gil Lederman ; Yuhuai Wu ; Chris J. Maddison ; Roger Grosse ; Edward Lee ; Sanjit A. Seshia ; Fahiem Bacchus
HIGHLIGHT:      In this paper, we present Neuro#, an approach for learning branching heuristics for exact #SAT solvers via evolution strategies (ES) to reduce the number of branching steps the solver takes to solve an instance.

44, TITLE:      Learning Combined Set Covering and Traveling Salesman Problem
http://arxiv.org/abs/2007.03203
AUTHORS:        Yuwen Yang ; Jayant Rajgopal
COMMENTS:       38 pages, 1 figure, 5 tables
HIGHLIGHT:      Motivated by applications where the optimal policy needs to be updated on a regular basis and repetitively solving this via MIP can be computationally expensive, we propose a machine learning approach to effectively deal with this problem by providing an opportunity to learn from historical optimal solutions that are derived from the MIP formulation.

45, TITLE:      Cultural Convergence: Insights into the behavior of misinformation networks on Twitter
http://arxiv.org/abs/2007.03443
AUTHORS:        Liz McQuillan ; Erin McAweeney ; Alicia Bargar ; Alex Ruch
COMMENTS:       15 pages (7 for paper, 3 for reference, 5 for appendix), 3 figures
HIGHLIGHT:      We use a multimodal pipeline, consisting of network mapping, topic modeling, bridging centrality, and divergence to analyze Twitter data surrounding the COVID-19 pandemic.

46, TITLE:      Benefitting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution
http://arxiv.org/abs/2007.03053
AUTHORS:        Mohammad Saeed Rad ; Thomas Yu ; Claudiu Musat ; Hazim Kemal Ekenel ; Behzad Bozorgtabar ; Jean-Philippe Thiran
HIGHLIGHT:      In this work, we propose to handle real-world SR by splitting this ill-posed problem into two comparatively more well-posed steps.

47, TITLE:      Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks
http://arxiv.org/abs/2007.03207
AUTHORS:        Yan Liu ; Lingqiao Liu ; Peng Wang ; Pingping Zhang ; Yinjie Lei
COMMENTS:       To be appeared at ECCV 2020
HIGHLIGHT:      Specifically, we proposed a novel semi-supervised crowd counting method which is built upon two innovative components: (1) a set of inter-related binary segmentation tasks are derived from the original density map regression task as the surrogate prediction target; (2) the surrogate target predictors are learned from both labeled and unlabeled data by utilizing a proposed self-training scheme which fully exploits the underlying constraints of these binary segmentation tasks.

48, TITLE:      Learning to Segment Anatomical Structures Accurately from One Exemplar
http://arxiv.org/abs/2007.03052
AUTHORS:        Yuhang Lu ; Kang Zheng ; Weijian Li ; Yirui Wang ; Adam P. Harrison ; Chihung Lin ; Song Wang ; Jing Xiao ; Le Lu ; Chang-Fu Kuo ; Shun Miao
COMMENTS:       MICCAI2020
HIGHLIGHT:      In this work, we propose a novel contribution of Contour Transformer Network (CTN), a one-shot anatomy segmentor including a naturally built-in human-in-the-loop mechanism.

49, TITLE:      Automatic Ischemic Stroke Lesion Segmentation from Computed Tomography Perfusion Images by Image Synthesis and Attention-Based Deep Neural Networks
http://arxiv.org/abs/2007.03294
AUTHORS:        Guotai Wang ; Tao Song ; Qiang Dong ; Mei Cui ; Ning Huang ; Shaoting Zhang
COMMENTS:       14 pages, 10 figures
HIGHLIGHT:      To deal with this problem, we propose a novel framework based on synthesized pseudo Diffusion-Weighted Imaging (DWI) from perfusion parameter maps to obtain better image quality for more accurate segmentation.

50, TITLE:      Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer
http://arxiv.org/abs/2007.03292
AUTHORS:        Christian Abbet ; Inti Zlobec ; Behzad Bozorgtabar ; Jean-Philippe Thiran
HIGHLIGHT:      In this work, we aim to learn histopathological patterns within cancerous tissue regions that can be used to improve prognostic stratification for colorectal cancer. To this end, we introduce a new well-characterized clinicopathological dataset, including a retrospective collective of 374 patients, with their survival time and treatment information.

51, TITLE:      AnchorFace: An Anchor-based Facial Landmark Detector Across Large Poses
http://arxiv.org/abs/2007.03221
AUTHORS:        Zixuan Xu ; Banghuai Li ; Miao Geng ; Ye Yuan ; Gang Yu
HIGHLIGHT:      In this paper, we target the problem of facial landmark localization across large poses and address this task based on a split-and-aggregate strategy.

52, TITLE:      VPN: Learning Video-Pose Embedding for Activities of Daily Living
http://arxiv.org/abs/2007.03056
AUTHORS:        Srijan Das ; Saurav Sharma ; Rui Dai ; Francois Bremond ; Monique Thonnat
COMMENTS:       Accepted in ECCV 2020
HIGHLIGHT:      In this paper, we focus on the spatio-temporal aspect of recognizing Activities of Daily Living (ADL).

53, TITLE:      Metric-Guided Prototype Learning
http://arxiv.org/abs/2007.03047
AUTHORS:        Vivien Sainte Fare Garnot ; Loic Landrieu
HIGHLIGHT:      When certain conditions are met, this matrix defines a metric, which we use in a new and versatile classification layer to model the disparity of errors.

54, TITLE:      Guided Fine-Tuning for Large-Scale Material Transfer
http://arxiv.org/abs/2007.03059
AUTHORS:        Valentin Deschaintre ; George Drettakis ; Adrien Bousseau
COMMENTS:       Accepted to CGF, proceedings of EGSR 2020
HIGHLIGHT:      We present a method to transfer the appearance of one or a few exemplar SVBRDFs to a target image representing similar materials.

55, TITLE:      Robust Technique for Representative Volume Element Identification in Noisy Microtomography Images of Porous Materials Based on Pores Morphology and Their Spatial Distribution
http://arxiv.org/abs/2007.03035
AUTHORS:        Maxim Grigoriev ; Anvar Khafizov ; Vladislav Kokhan ; Viktor Asadchikov
HIGHLIGHT:      This research sheds light on representative elementary volume identification without consideration of any physical parameters such as porosity, etc.

56, TITLE:      Dual Mixup Regularized Learning for Adversarial Domain Adaptation
http://arxiv.org/abs/2007.03141
AUTHORS:        Yuan Wu ; Diana Inkpen ; Ahmed El-Roby
COMMENTS:       16 pages, 4 figures
HIGHLIGHT:      In order to alleviate the above issues, we propose a dual mixup regularized learning (DMRL) method for UDA, which not only guides the classifier in enhancing consistent predictions in-between samples, but also enriches the intrinsic structures of the latent space.

57, TITLE:      Research on Annotation Rules and Recognition Algorithm Based on Phrase Window
http://arxiv.org/abs/2007.03140
AUTHORS:        Guang Liu ; Gang Tu ; Zheng Li ; Yi-Jian Liu
COMMENTS:       in Chinese
HIGHLIGHT:      In order to solve these two problems, we propose labeling rules based on phrase windows, and designed corresponding phrase recognition algorithms.

58, TITLE:      Re-thinking Co-Salient Object Detection
http://arxiv.org/abs/2007.03380
AUTHORS:        Fan Deng-Ping ; Li Tengpeng ; Lin Zheng ; Ji Ge-Peng ; Zhang Dingwen ; Cheng Ming-Ming ; Fu Huazhu ; Shen Jianbing
COMMENTS:       22pages, 18 figures. CVPR2020-CoSOD3K extension. Code: https://dpfan.net/CoSOD3K
HIGHLIGHT:      In this paper, we conduct a comprehensive study on the co-salient object detection (CoSOD) problem for images. To tackle this issue, we first introduce a new benchmark, called CoSOD3k in the wild, which requires a large amount of semantic context, making it more challenging than existing CoSOD datasets.

59, TITLE:      C2G-Net: Exploiting Morphological Properties for Image Classification
http://arxiv.org/abs/2007.03378
AUTHORS:        Laurin Herbsthofer ; Barbara Prietl ; Martina Tomberger ; Thomas Pieber ; Pablo López-García
COMMENTS:       10 pages, 5 figures (Figure 3 with 4 sub-figures), Appendix A and Appendix B after the references. Originally submitted to ICML2020 but rejected
HIGHLIGHT:      In this paper we propose C2G-Net, a pipeline for image classification that exploits the morphological properties of images containing a large number of similar objects like biological cells.

60, TITLE:      Adaptive Cascade Submodular Maximization
http://arxiv.org/abs/2007.03592
AUTHORS:        Shaojie Tang ; Jing Yuan
HIGHLIGHT:      In this paper, we propose and study the cascade submodular maximization problem under the adaptive setting.

61, TITLE:      An Emergency Medical Services Clinical Audit System driven by Named Entity Recognition from Deep Learning
http://arxiv.org/abs/2007.03596
AUTHORS:        Wang Han ; Wesley Yeung ; Angeline Tung ; Joey Tay Ai Meng ; Davin Ryanputera ; Feng Mengling ; Shalini Arulanadam
HIGHLIGHT:      The dataset used in this study contained 58,898 unlabelled ambulance incidents encountered by the Singapore Civil Defence Force from 1st April 2019 to 30th June 2019.

62, TITLE:      Instance Segmentation for Whole Slide Imaging: End-to-End or Detect-Then-Segment
http://arxiv.org/abs/2007.03593
AUTHORS:        Aadarsh Jha ; Haichun Yang ; Ruining Deng ; Meghan E. Kapp ; Agnes B. Fogo ; Yuankai Huo
HIGHLIGHT:      In this paper, we assess if the end-to-end instance segmentation framework is optimal for high-resolution WSI objects by comparing Mask-RCNN with our proposed detect-then-segment framework.

63, TITLE:      SpinalNet: Deep Neural Network with Gradual Input
http://arxiv.org/abs/2007.03347
AUTHORS:        H M Dipu Kabir ; Moloud Abdar ; Seyed Mohammad Jafar Jalali ; Abbas Khosravi ; Amir F Atiya ; Saeid Nahavandi ; Dipti Srinivasan
HIGHLIGHT:      This paper aims to present the SpinalNet.

64, TITLE:      Natural Emergence of Heterogeneous Strategies in Artificially Intelligent Competitive Teams
http://arxiv.org/abs/2007.03102
AUTHORS:        Ankur Deka ; Katia Sycara
HIGHLIGHT:      In this work, we develop a competitive multi agent environment called FortAttack in which two teams compete against each other.

65, TITLE:      RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayEr
http://arxiv.org/abs/2007.03349
AUTHORS:        Xingjian Li ; Haoyi Xiong ; Haozhe An ; Chengzhong Xu ; Dejing Dou
COMMENTS:       Accepted by ICML'2020
HIGHLIGHT:      In this work, we propose RIFLE - a simple yet effective strategy that deepens backpropagation in transfer learning settings, through periodically Re-Initializing the Fully-connected LayEr with random scratch during the fine-tuning procedure.

66, TITLE:      Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
http://arxiv.org/abs/2007.03107
AUTHORS:        Francesco Salvetti ; Vittorio Mazzia ; Aleem Khaliq ; Marcello Chiaberge
HIGHLIGHT:      At present, satellite based remote sensing platforms offer huge data availability with high temporal resolution and low spatial resolution.In this context, the presented research proposes a novel residual attention model (RAMS) that efficiently tackles the multi-image super-resolution task, simultaneously exploiting spatial and temporal correlations to combine multiple images.

67, TITLE:      Learning Embeddings for Image Clustering: An Empirical Study of Triplet Loss Approaches
http://arxiv.org/abs/2007.03123
AUTHORS:        Kalun Ho ; Janis Keuper ; Franz-Josef Pfreundt ; Margret Keuper
HIGHLIGHT:      In this work, we evaluate two different image clustering objectives, k-means clustering and correlation clustering, in the context of Triplet Loss induced feature space embeddings.

68, TITLE:      Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment
http://arxiv.org/abs/2007.03363
AUTHORS:        Ithan Moreira ; Javier Rivas ; Francisco Cruz ; Richard Dazeley ; Angel Ayala ; Bruno Fernandes
COMMENTS:       19 pages, 7 figures
HIGHLIGHT:      In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a human-robot scenario.

69, TITLE:      Non-image Data Classification with Convolutional Neural Networks
http://arxiv.org/abs/2007.03218
AUTHORS:        Anuraganand Sharma ; Dinesh Kumar
COMMENTS:       20 pages - preprint version
HIGHLIGHT:      We have proposed some novel preprocessing methods of data wrangling that transform a 1-D data vector to a 2-D graphical image with appropriate correlations among the fields to be processed on CNN.

70, TITLE:      Long-term Human Motion Prediction with Scene Context
http://arxiv.org/abs/2007.03672
AUTHORS:        Zhe Cao ; Hang Gao ; Karttikeya Mangalam ; Qi-Zhi Cai ; Minh Vo ; Jitendra Malik
COMMENTS:       ECCV 2020 Oral. Dataset & Code: https://github.com/ZheC/GTA-IM-Dataset. Video: https://people.eecs.berkeley.edu/~zhecao/hmp/index.html
HIGHLIGHT:      In this work, we propose a novel three-stage framework that exploits scene context to tackle this task.

71, TITLE:      Learning to Count in the Crowd from Limited Labeled Data
http://arxiv.org/abs/2007.03195
AUTHORS:        Vishwanath A. Sindagi ; Rajeev Yasarla ; Deepak Sam Babu ; R. Venkatesh Babu ; Vishal M. Patel
COMMENTS:       Accepted at ECCV 2020
HIGHLIGHT:      In this work, we focus on reducing the annotation efforts by learning to count in the crowd from limited number of labeled samples while leveraging a large pool of unlabeled data.

72, TITLE:      See, Hear, Explore: Curiosity via Audio-Visual Association
http://arxiv.org/abs/2007.03669
AUTHORS:        Victoria Dean ; Shubham Tulsiani ; Abhinav Gupta
HIGHLIGHT:      In this paper, we introduce an alternative form of curiosity that rewards novel associations between different senses.

73, TITLE:      Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI
http://arxiv.org/abs/2007.03199
AUTHORS:        Hang Min ; Darryl McClymont ; Shekhar S. Chandra ; Stuart Crozier ; Andrew P. Bradley
HIGHLIGHT:      In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention.

74, TITLE:      ReMOTS: Refining Multi-Object Tracking and Segmentation
http://arxiv.org/abs/2007.03200
AUTHORS:        Fan Yang ; Xin Chang ; Chenyu Dang ; Ziqiang Zheng ; Sakriani Sakti ; Satoshi Nakamura ; Yang Wu
COMMENTS:       4 pages
HIGHLIGHT:      To tackle this issue, we propose a Refining MOTS (i.e., ReMOTS) framework.

75, TITLE:      Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability
http://arxiv.org/abs/2007.03198
AUTHORS:        Utku Ozbulak ; Jonathan Peck ; Wesley De Neve ; Bart Goossens ; Yvan Saeys ; Arnout Van Messem
COMMENTS:       Accepted for the ICML 2020, Workshop on Uncertainty and Robustness in Deep Learning (UDL)
HIGHLIGHT:      In this study, we show that effective regional perturbations can be generated without resorting to complex methods.

76, TITLE:      Kidney Exchange with Inhomogeneous Edge Existence Uncertainty
http://arxiv.org/abs/2007.03191
AUTHORS:        Hoda Bidkhori ; John P Dickerson ; Duncan C McElfresh ; Ke Ren
HIGHLIGHT:      Subsequently, we propose a sample-average-approximation (SAA) based approach to solve this problem.

77, TITLE:      Continual Learning in Human Activity Recognition: an Empirical Analysis of Regularization
http://arxiv.org/abs/2007.03032
AUTHORS:        Saurav Jha ; Martin Schiemer ; Juan Ye
COMMENTS:       7 pages, 5 figures, 3 tables (Appendix included)
HIGHLIGHT:      As recent methods have mostly been composed of loss regularization terms and memory replay, we provide a constituent-wise analysis of some prominent task-incremental learning techniques employing these on HAR datasets.

78, TITLE:      Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
http://arxiv.org/abs/2007.03274
AUTHORS:        Zihan Pan ; Malu Zhang ; Jibin Wu ; Haizhou Li
HIGHLIGHT:      Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array.

79, TITLE:      Labeling of Multilingual Breast MRI Reports
http://arxiv.org/abs/2007.03028
AUTHORS:        Chen-Han Tsai ; Nahum Kiryati ; Eli Konen ; Miri Sklair-Levy ; Arnaldo Mayer
COMMENTS:       Submitted to MICCAI LABELS Workshop 2020
HIGHLIGHT:      In this work, we present a framework for developing a multilingual breast MRI report classifier using a custom-built language representation called LAMBR.

80, TITLE:      Optical Navigation in Unstructured Dynamic Railroad Environments
http://arxiv.org/abs/2007.03409
AUTHORS:        Darius Burschka ; Christian Robl ; Sebastian Ohrendorf-Weiss
HIGHLIGHT:      We present an approach for optical navigation in unstructured, dynamic railroad environments.

81, TITLE:      Single Storage Semi-Global Matching for Real Time Depth Processing
http://arxiv.org/abs/2007.03269
AUTHORS:        Prathmesh Sawant ; Yashwant Temburu ; Mandar Datar ; Imran Ahmed ; Vinayak Shriniwas ; Sachin Patkar
COMMENTS:       10 pages, Published in National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics(NCVPRIPG) 2019
HIGHLIGHT:      In this paper, we show the design and implementation of a stereo-vision system, which is based on FPGA-implementation of More Global Matching(MGM).

82, TITLE:      Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
http://arxiv.org/abs/2007.03643
AUTHORS:        Keegan Lensink ; Issam Laradji ; Marco Law ; Paolo Emilio Barbano ; Savvas Nicolaou. William Parker ; Eldad Haber
COMMENTS:       9 pages, 5 figures
HIGHLIGHT:      In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection.

83, TITLE:      LabelEnc: A New Intermediate Supervision Method for Object Detection
http://arxiv.org/abs/2007.03282
AUTHORS:        Miao Hao ; Yitao Liu ; Xiangyu Zhang ; Jian Sun
COMMENTS:       To appear in ECCV 2020
HIGHLIGHT:      In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems.

84, TITLE:      Spectral Graph-based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals
http://arxiv.org/abs/2007.03281
AUTHORS:        Mohammad Idrees Bhat ; B. Sharada
COMMENTS:       16 pages, 8 figures
HIGHLIGHT:      In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the robust graph representation and spectral graph embedding concept to characterise and effectively represent handwritten characters, taking into account writing styles, cursiveness and relationships.

85, TITLE:      Continual BERT: Continual Learning for Adaptive Extractive Summarization of COVID-19 Literature
http://arxiv.org/abs/2007.03405
AUTHORS:        Jong Won Park
COMMENTS:       6 pages, 3 figures
HIGHLIGHT:      To aid the community in understanding the rapidly flowing array of COVID-19 literature, we propose a novel BERT architecture that provides a brief yet original summarization of lengthy papers.

86, TITLE:      An Entropy Equation for Energy
http://arxiv.org/abs/2007.03286
AUTHORS:        Kieran Greer
HIGHLIGHT:      This paper describes an entropy equation, but one that should be used for measuring energy and not information.

87, TITLE:      srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications
http://arxiv.org/abs/2007.03502
AUTHORS:        Anh Tran ; Mike Eldred ; Scott McCann ; Yan Wang
HIGHLIGHT:      In this work, we propose a novel multi-objective (MO) extension, called srMO-BO-3GP, to solve the MO optimization problems in a sequential setting.

88, TITLE:      Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously
http://arxiv.org/abs/2007.03514
AUTHORS:        Mikita Sazanovich ; Konstantin Chaika ; Kirill Krinkin ; Aleksei Shpilman
COMMENTS:       Accepted to the Workshop on AI for Autonomous Driving (AIAD), the 37th International Conference on Machine Learning (ICML2020)
HIGHLIGHT:      In this paper, we describe our winning approach to solving the Lane Following Challenge at the AI Driving Olympics Competition through imitation learning on a mixed set of simulation and real-world data.

89, TITLE:      Program Verification via Predicate Constraint Satisfiability Modulo Theories
http://arxiv.org/abs/2007.03656
AUTHORS:        Hiroshi Unno ; Yuki Satake ; Tachio Terauchi ; Eric Koskinen
HIGHLIGHT:      This paper presents a verification framework based on a new class of predicate Constraint Satisfaction Problems called pCSP where constraints are represented as clauses modulo first-order theories over function variables and predicate variables that may represent well-founded predicates.

90, TITLE:      AutoAssign: Differentiable Label Assignment for Dense Object Detection
http://arxiv.org/abs/2007.03496
AUTHORS:        Benjin Zhu ; Jianfeng Wang ; Zhengkai Jiang ; Fuhang Zong ; Songtao Liu ; Zeming Li ; Jian Sun
COMMENTS:       Rejected by ECCV 2020; Reformated
HIGHLIGHT:      In this paper, we propose an anchor-free object detector with a fully differentiable label assignment strategy, named AutoAssign.

91, TITLE:      Strong Generalization and Efficiency in Neural Programs
http://arxiv.org/abs/2007.03629
AUTHORS:        Yujia Li ; Felix Gimeno ; Pushmeet Kohli ; Oriol Vinyals
HIGHLIGHT:      We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction.

92, TITLE:      Multivariate Time Series Classification Using Spiking Neural Networks
http://arxiv.org/abs/2007.03547
AUTHORS:        Haowen Fang ; Amar Shrestha ; Qinru Qiu
HIGHLIGHT:      In this work, we present an encoding scheme to convert time series into sparse spatial temporal spike patterns.

93, TITLE:      Constraint-Based Learning for Continuous-Time Bayesian Networks
http://arxiv.org/abs/2007.03248
AUTHORS:        Alessandro Bregoli ; Marco Scutari ; Fabio Stella
HIGHLIGHT:      In this paper, we propose the first constraint-based algorithm for learning the structure of continuous-time Bayesian networks.

94, TITLE:      Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords
http://arxiv.org/abs/2007.03006
AUTHORS:        Tom Kocmi ; Martin Popel ; Ondrej Bojar
HIGHLIGHT:      We present a new release of the Czech-English parallel corpus CzEng 2.0 consisting of over 2 billion words (2 "gigawords") in each language.

95, TITLE:      Learning to Generate Novel Domains for Domain Generalization
http://arxiv.org/abs/2007.03304
AUTHORS:        Kaiyang Zhou ; Yongxin Yang ; Timothy Hospedales ; Tao Xiang
COMMENTS:       To appear in ECCV'20
HIGHLIGHT:      This paper focuses on domain generalization (DG), the task of learning from multiple source domains a model that generalizes well to unseen domains.

96, TITLE:      Benchmarking in Optimization: Best Practice and Open Issues
http://arxiv.org/abs/2007.03488
AUTHORS:        Thomas Bartz-Beielstein ; Carola Doerr ; Jakob Bossek ; Sowmya Chandrasekaran ; Tome Eftimov ; Andreas Fischbach ; Pascal Kerschke ; Manuel Lopez-Ibanez ; Katherine M. Malan ; Jason H. Moore ; Boris Naujoks ; Patryk Orzechowski ; Vanessa Volz ; Markus Wagner ; Thomas Weise
HIGHLIGHT:      The article discusses eight essential topics in benchmarking: clearly stated goals, well-specified problems, suitable algorithms, adequate performance measures, thoughtful analysis, effective and efficient designs, comprehensible presentations, and guaranteed reproducibility.

97, TITLE:      Single Shot Video Object Detector
http://arxiv.org/abs/2007.03560
AUTHORS:        Jiajun Deng ; Yingwei Pan ; Ting Yao ; Wengang Zhou ; Houqiang Li ; Tao Mei
COMMENTS:       Accepted by IEEE Transactions on Multimedia; The code is available at \url{https://github.com/ddjiajun/SSVD}
HIGHLIGHT:      In this paper, we propose to address the problem by enhancing per-frame features through aggregation of neighboring frames.

98, TITLE:      Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach
http://arxiv.org/abs/2007.03313
AUTHORS:        Kevin Shen Hoong Ong ; Dusit Niyato ; Chau Yuen
COMMENTS:       6 pages, 5 figures, accepted in IEEE WF-IoT 2020
HIGHLIGHT:      In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context.

99, TITLE:      Physics-Based Deep Neural Networks for Beam Dynamics in Charged Particle Accelerators
http://arxiv.org/abs/2007.03555
AUTHORS:        Andrei Ivanov ; Ilya Agapov
HIGHLIGHT:      This paper presents a novel approach for constructing neural networks which model charged particle beam dynamics.

100, TITLE:     RGBT Salient Object Detection: A Large-scale Dataset and Benchmark
http://arxiv.org/abs/2007.03262
AUTHORS:        Zhengzheng Tu ; Yan Ma ; Zhun Li ; Chenglong Li ; Jieming Xu ; Yongtao Liu
COMMENTS:       12 pages, 10 figures
HIGHLIGHT:      % With this dataset, we propose a powerful baseline approach, which extracts multi-level features within each modality and aggregates these features of all modalities with the attention mechanism, for accurate RGBT salient object detection.

101, TITLE:     Deep Contextual Embeddings for Address Classification in E-commerce
http://arxiv.org/abs/2007.03020
AUTHORS:        Shreyas Mangalgi ; Lakshya Kumar ; Ravindra Babu Tallamraju
COMMENTS:       9 Pages, 8 Figures, AI for fashion supply chain, KDD2020 Workshop
HIGHLIGHT:      In this paper, we propose a novel approach towards understanding customer addresses by deriving motivation from recent advances in Natural Language Processing (NLP).

102, TITLE:     Lossless CNN Channel Pruning via Gradient Resetting and Convolutional Re-parameterization
http://arxiv.org/abs/2007.03260
AUTHORS:        Xiaohan Ding ; Tianxiang Hao ; Ji Liu ; Jungong Han ; Yuchen Guo ; Guiguang Ding
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      Inspired by the neurobiology research about the independence of remembering and forgetting, we propose to re-parameterize a CNN into the remembering parts and forgetting parts, where the former learn to maintain the performance and the latter learn for efficiency.

103, TITLE:     Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
http://arxiv.org/abs/2007.03263
AUTHORS:        Lei Shi ; Yifan Zhang ; Jian Cheng ; Hanqing Lu
HIGHLIGHT:      In this work, we present a novel decoupled spatial-temporal attention network(DSTA-Net) for skeleton-based action recognition. Besides, from the data aspect, we introduce a skeletal data decoupling technique to emphasize the specific characteristics of space/time and different motion scales, resulting in a more comprehensive understanding of the human actions.To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.

104, TITLE:     Determination of the most representative descriptor among a set of feature vectors for the same object
http://arxiv.org/abs/2007.03021
AUTHORS:        Dmitry Pozdnyakov
COMMENTS:       8 pages, 1 figure
HIGHLIGHT:      On an example of solution of the face recognition problem the approach for estimation of the most representative descriptor among a set of feature vectors for the same face is considered in present study.

105, TITLE:     scb-mt-en-th-2020: A Large English-Thai Parallel Corpus
http://arxiv.org/abs/2007.03541
AUTHORS:        Lalita Lowphansirikul ; Charin Polpanumas ; Attapol T. Rutherford ; Sarana Nutanong
COMMENTS:       35 pages, 4 figures
HIGHLIGHT:      The primary objective of our work is to build a large-scale English-Thai dataset for machine translation. We construct an English-Thai machine translation dataset with over 1 million segment pairs, curated from various sources, namely news, Wikipedia articles, SMS messages, task-based dialogs, web-crawled data and government documents.

106, TITLE:     3D Topology Transformation with Generative Adversarial Networks
http://arxiv.org/abs/2007.03532
AUTHORS:        Luca Stornaiuolo ; Nima Dehmamy ; Albert-László Barabási ; Mauro Martino
HIGHLIGHT:      We describe how to use our approach to construct customized 3D representations.

107, TITLE:     Meta Corrupted Pixels Mining for Medical Image Segmentation
http://arxiv.org/abs/2007.03538
AUTHORS:        Jixin Wang ; Sanping Zhou ; Chaowei Fang ; Le Wang ; Jinjun Wang
COMMENTS:       Accepted By MICCAI2020
HIGHLIGHT:      Aiming at training deep segmentation models on datasets with probably corrupted annotations, we propose a novel Meta Corrupted Pixels Mining (MCPM) method based on a simple meta mask network.

108, TITLE:     Light Field Image Super-Resolution Using Deformable Convolution
http://arxiv.org/abs/2007.03535
AUTHORS:        Yingqian Wang ; Jungang Yang ; Longguang Wang ; Xinyi Ying ; Tianhao Wu ; Wei An ; Yulan Guo
COMMENTS:       Submitted to IEEE Transactions on Image Processing (TIP)
HIGHLIGHT:      In this paper, we propose a deformable convolution network (i.e., LF-DFnet) to handle the disparity problem for LF image SR. Moreover, we develop a baseline-adjustable LF dataset to evaluate SR performance under different disparities.

109, TITLE:     Text Recognition -- Real World Data and Where to Find Them
http://arxiv.org/abs/2007.03098
AUTHORS:        Klára Janoušková ; Jiri Matas ; Lluis Gomez ; Dimosthenis Karatzas
COMMENTS:       10 pages
HIGHLIGHT:      We present a method for exploiting weakly annotated images to improve text extraction pipelines.

110, TITLE:     Hierarchical nucleation in deep neural networks
http://arxiv.org/abs/2007.03506
AUTHORS:        Diego Doimo ; Aldo Glielmo ; Alessandro Laio ; Alessio Ansuini
HIGHLIGHT:      In this work we study the evolution of the probability density of the ImageNet dataset across the hidden layers in some state-of-the-art DCNs.

111, TITLE:     The Go Transformer: Natural Language Modeling for Game Play
http://arxiv.org/abs/2007.03500
AUTHORS:        David Noever ; Matthew Ciolino ; Josh Kalin
COMMENTS:       8 Pages, 5 Figures, 1 Table
HIGHLIGHT:      This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go.



==========Updates to Previous Papers==========
1, TITLE:       Learning Multimodal Representations for Unseen Activities
http://arxiv.org/abs/1806.08251
AUTHORS:        AJ Piergiovanni ; Michael S. Ryoo
HIGHLIGHT:      We present a method to learn a joint multimodal representation space that enables recognition of unseen activities in videos. In addition to testing on publicly available datasets, we introduce a new, large-scale text/video dataset.

2, TITLE:       S2ORC: The Semantic Scholar Open Research Corpus
http://arxiv.org/abs/1911.02782
AUTHORS:        Kyle Lo ; Lucy Lu Wang ; Mark Neumann ; Rodney Kinney ; Dan S. Weld
COMMENTS:       ACL 2020
HIGHLIGHT:      We introduce S2ORC, a large corpus of 81.1M English-language academic papers spanning many academic disciplines.

3, TITLE:       Rethinking Bottleneck Structure for Efficient Mobile Network Design
http://arxiv.org/abs/2007.02269
AUTHORS:        Zhou Daquan ; Qibin Hou ; Yunpeng Chen ; Jiashi Feng ; Shuicheng Yan
COMMENTS:       24 pages, published as a ECCV20 conference paper
HIGHLIGHT:      In this paper, we rethink the necessity of such design changes and find it may bring risks of information loss and gradient confusion.

4, TITLE:       Lung Segmentation from Chest X-rays using Variational Data Imputation
http://arxiv.org/abs/2005.10052
AUTHORS:        Raghavendra Selvan ; Erik B. Dam ; Nicki S. Detlefsen ; Sofus Rischel ; Kaining Sheng ; Mads Nielsen ; Akshay Pai
COMMENTS:       Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE/
HIGHLIGHT:      In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs.

5, TITLE:       Higher Criticism for Discriminating Word-Frequency Tables and Testing Authorship
http://arxiv.org/abs/1911.01208
AUTHORS:        Alon Kipnis
COMMENTS:       under review (AOAS)
HIGHLIGHT:      We apply this measure to authorship attribution challenges, where the goal is to identify the author of a document using other documents whose authorship is known.

6, TITLE:       Contextualizing Hate Speech Classifiers with Post-hoc Explanation
http://arxiv.org/abs/2005.02439
AUTHORS:        Brendan Kennedy ; Xisen Jin ; Aida Mostafazadeh Davani ; Morteza Dehghani ; Xiang Ren
COMMENTS:       To appear in Proceedings of the 2020 Annual Conference of the Association for Computational Linguistics; Updated references and discussions
HIGHLIGHT:      Our approach improved over baselines in limiting false positives on out-of-domain data while maintaining or improving in-domain performance.

7, TITLE:       Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
http://arxiv.org/abs/2006.09989
AUTHORS:        Elvis Dohmatob
HIGHLIGHT:      Our contributions are three-fold.

8, TITLE:       Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network
http://arxiv.org/abs/2002.01176
AUTHORS:        A. Sheshkus ; A. Chirvonaya ; D. Matveev ; D. Nikolaev ; V. L. Arlazarov
COMMENTS:       9 pages, 9 figures, submitted to "Computer Optics"; extra experiment added, new theorem proof added, references added; typos corrected
HIGHLIGHT:      In this paper, we suggest a new neural network architecture for vanishing point detection in images.

9, TITLE:       Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling
http://arxiv.org/abs/2007.02282
AUTHORS:        Yong Li ; Andrea Turrini ; Xuechao Sun ; Lijun Zhang
COMMENTS:       Accepted to ATVA 2020; typos corrected; authors corrected
HIGHLIGHT:      In this paper, we present $\mathsf{IMC}^2$, a specific algorithm for proving B\"uchi automata non-inclusion $\mathcal{L}(\mathcal{A}) \not\subseteq \mathcal{L}(\mathcal{B})$, based on Grosu and Smolka's algorithm $\mathsf{MC}^2$ developed for Monte Carlo model checking against LTL formulas.

10, TITLE:      Determining Sequence of Image Processing Technique (IPT) to Detect Adversarial Attacks
http://arxiv.org/abs/2007.00337
AUTHORS:        Kishor Datta Gupta ; Zahid Akhtar ; Dipankar Dasgupta
HIGHLIGHT:      In this work, we propose an evolutionary approach to automatically determine Image Processing Techniques Sequence (IPTS) for detecting malicious inputs.

11, TITLE:      COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
http://arxiv.org/abs/2007.00576
AUTHORS:        Qingyun Wang ; Manling Li ; Xuan Wang ; Nikolaus Parulian ; Guangxing Han ; Jiawei Ma ; Jingxuan Tu ; Ying Lin ; Haoran Zhang ; Weili Liu ; Aabhas Chauhan ; Yingjun Guan ; Bangzheng Li ; Ruisong Li ; Xiangchen Song ; Heng Ji ; Jiawei Han ; Shih-Fu Chang ; James Pustejovsky ; Jasmine Rah ; David Liem ; Ahmed Elsayed ; Martha Palmer ; Clare Voss ; Cynthia Schneider ; Boyan Onyshkevych
COMMENTS:       11 pages, submitted to ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID), for resources see http://blender.cs.illinois.edu/covid19/
HIGHLIGHT:      COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

12, TITLE:      Point Proposal Network for Reconstructing 3D Particle Endpoints with Sub-Pixel Precision in Liquid Argon Time Projection Chambers
http://arxiv.org/abs/2006.14745
AUTHORS:        Laura Dominé ; Pierre Côte de Soux ; François Drielsma ; Dae Heun Koh ; Ran Itay ; Qing Lin ; Kazuhiro Terao ; Ka Vang Tsang ; Tracy L. Usher
HIGHLIGHT:      Using as a benchmark the PILArNet public LArTPC data sample in which the voxel resolution is 3mm/voxel, our algorithm successfully predicted 96.8% and 97.8% of 3D points within a distance of 3 and 10~voxels from the provided true point locations respectively.

13, TITLE:      Counterfactual Vision-and-Language Navigation via Adversarial Path Sampling
http://arxiv.org/abs/1911.07308
AUTHORS:        Tsu-Jui Fu ; Xin Eric Wang ; Matthew Peterson ; Scott Grafton ; Miguel Eckstein ; William Yang Wang
COMMENTS:       ECCV 2020 (spotlight)
HIGHLIGHT:      We propose an adversarial-driven counterfactual reasoning model that can consider effective conditions instead of low-quality augmented data.

14, TITLE:      Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
http://arxiv.org/abs/2002.03592
AUTHORS:        Philipp Terhörst ; Jan Niklas Kolf ; Naser Damer ; Florian Kirchbuchner ; Arjan Kuijper
COMMENTS:       Under review in Pattern Recognition Letters
HIGHLIGHT:      In this work, we propose a novel unsupervised fair score normalization approach that is specifically designed to reduce the effect of bias in face recognition and subsequently lead to a significant overall performance boost.

15, TITLE:      Proving P!=NP in first-order PA
http://arxiv.org/abs/2005.10080
AUTHORS:        Rupert McCallum
HIGHLIGHT:      We show that it is provable in PA that there is an arithmetically definable sequence $\{\phi_{n}:n \in \omega\}$ of $\Pi^{0}_{2}$-sentences, such that - PRA+$\{\phi_{n}:n \in \omega\}$ is $\Pi^{0}_{2}$-sound and $\Pi^{0}_{1}$-complete - the length of $\phi_{n}$ is bounded above by a polynomial function of $n$ with positive leading coefficient - PRA+$\phi_{n+1}$ always proves 1-consistency of PRA+$\phi_{n}$.

16, TITLE:      Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
http://arxiv.org/abs/2007.02393
AUTHORS:        Seung-Hun Nam ; Wonhyuk Ahn ; In-Jae Yu ; Myung-Joon Kwon ; Minseok Son ; Heung-Kyu Lee
HIGHLIGHT:      In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion.

17, TITLE:      Improving Interpretability of CNN Models Using Non-Negative Concept Activation Vectors
http://arxiv.org/abs/2006.15417
AUTHORS:        Ruihan Zhang ; Prashan Madumal ; Tim Miller ; Krista A. Ehinger ; Benjamin I. P. Rubinstein
HIGHLIGHT:      In this work, we rethink the ACE algorithm of Ghorbani et~al., proposing an alternative concept-based explanation framework.

18, TITLE:      Bookworm continual learning: beyond zero-shot learning and continual learning
http://arxiv.org/abs/2006.15176
AUTHORS:        Kai Wang ; Luis Herranz ; Anjan Dutta ; Joost van de Weijer
HIGHLIGHT:      We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually.

19, TITLE:      End-to-end Learning Improves Static Object Geo-localization in Monocular Video
http://arxiv.org/abs/2004.05232
AUTHORS:        Mohamed Chaabane ; Lionel Gueguen ; Ameni Trabelsi ; Ross Beveridge ; Stephen O'Hara
HIGHLIGHT:      In this work, we present a system that improves the localization of static objects by jointly-optimizing the components of the system via learning.

20, TITLE:      A Formal Hierarchy of RNN Architectures
http://arxiv.org/abs/2004.08500
AUTHORS:        William Merrill ; Gail Weiss ; Yoav Goldberg ; Roy Schwartz ; Noah A. Smith ; Eran Yahav
COMMENTS:       To appear at ACL 2020. Updated to include computational cost estimates and updated experimental results (in an erratum appendix)
HIGHLIGHT:      We develop a formal hierarchy of the expressive capacity of RNN architectures.

21, TITLE:      Proximal Distilled Evolutionary Reinforcement Learning
http://arxiv.org/abs/1906.09807
AUTHORS:        Cristian Bodnar ; Ben Day ; Pietro Lió
COMMENTS:       Camera-ready version for AAAI-20. Contains 10 pages, 11 figures
HIGHLIGHT:      We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning.

22, TITLE:      A Frobenius Algebraic Analysis for Parasitic Gaps
http://arxiv.org/abs/2005.05639
AUTHORS:        Michael Moortgat ; Mehrnoosh Sadrzadeh ; Gijs Wijnholds
COMMENTS:       SemSpace 2019, to appear in Journal of Applied Logics
HIGHLIGHT:      We identify two types of parasitic gapping where the duplication of semantic content can be confined to the lexicon.

23, TITLE:      ROAM: Random Layer Mixup for Semi-Supervised Learning in Medical Imaging
http://arxiv.org/abs/2003.09439
AUTHORS:        Tariq Bdair ; Nassir Navab ; Shadi Albarqouni
HIGHLIGHT:      Instead, we propose ROAM, a \textit{random layer mixup}, which encourages the network to be less confident for interpolated data points at randomly selected space.

24, TITLE:      On Class Orderings for Incremental Learning
http://arxiv.org/abs/2007.02145
AUTHORS:        Marc Masana ; Bartłomiej Twardowski ; Joost van de Weijer
COMMENTS:       Accepted at CL-ICML 2020. First two authors contributed equally
HIGHLIGHT:      In this paper, we investigate the impact of class orderings for incrementally learned classifiers.

25, TITLE:      What Can Learned Intrinsic Rewards Capture?
http://arxiv.org/abs/1912.05500
AUTHORS:        Zeyu Zheng ; Junhyuk Oh ; Matteo Hessel ; Zhongwen Xu ; Manuel Kroiss ; Hado van Hasselt ; David Silver ; Satinder Singh
COMMENTS:       ICML 2020
HIGHLIGHT:      In this paper, we instead consider the proposition that the reward function itself can be a good locus of learned knowledge.

26, TITLE:      Reflection-based Word Attribute Transfer
http://arxiv.org/abs/2007.02598
AUTHORS:        Yoichi Ishibashi ; Katsuhito Sudoh ; Koichiro Yoshino ; Satoshi Nakamura
COMMENTS:       Accepted at ACL 2020 Student Research Workshop (SRW)
HIGHLIGHT:      In this work, we propose a novel method for word attribute transfer based on reflection mappings without such an analogy operation.

27, TITLE:      Necessary and sufficient conditions for Boolean satisfiability
http://arxiv.org/abs/1910.10708
AUTHORS:        Stepan G. Margaryan
COMMENTS:       53 pages
HIGHLIGHT:      The study in this article seeks to find conditions that are necessary and sufficient for the satisfiability of a Boolean function.

28, TITLE:      Scalable Data Classification for Security and Privacy
http://arxiv.org/abs/2006.14109
AUTHORS:        Paulo Tanaka ; Sameet Sapra ; Nikolay Laptev
HIGHLIGHT:      Scalable Data Classification for Security and Privacy

29, TITLE:      Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning
http://arxiv.org/abs/2003.07618
AUTHORS:        Vladislav Sovrasov ; Dmitry Sidnev
COMMENTS:       Submitted to International Conference on Pattern Recognition (ICPR 2020)
HIGHLIGHT:      We propose to use the metric learning objective, namely AM-Softmax loss, and some additional training practices to build well-generalizing, yet, computationally efficient models.

30, TITLE:      Summarizing and Exploring Tabular Data in Conversational Search
http://arxiv.org/abs/2005.11490
AUTHORS:        Shuo Zhang ; Zhuyun Dai ; Krisztian Balog ; Jamie Callan
COMMENTS:       Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), 2020
HIGHLIGHT:      We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset.

31, TITLE:      NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
http://arxiv.org/abs/2005.14511
AUTHORS:        Navid Alemi Koohbanani ; Mostafa Jahanifar ; Neda Zamani Tajadin ; Nasir Rajpoot
HIGHLIGHT:      For multicellular structures such as glands, we propose a novel approach to provide the NuClick with a squiggle as a guiding signal, enabling it to segment the glandular boundaries. As exemplar outputs of our framework, we are releasing two datasets: 1) a dataset of lymphocyte annotations within IHC images, and 2) a dataset of segmented WBCs in blood smear images.

32, TITLE:      Reinterpreting CTC training as iterative fitting
http://arxiv.org/abs/1904.10619
AUTHORS:        Hongzhu Li ; Weiqiang Wang
COMMENTS:       to be published in Pattern Recognition
HIGHLIGHT:      Reinterpreting CTC training as iterative fitting

33, TITLE:      Soft Anchor-Point Object Detection
http://arxiv.org/abs/1911.12448
AUTHORS:        Chenchen Zhu ; Fangyi Chen ; Zhiqiang Shen ; Marios Savvides
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this work, we boost the performance of the anchor-point detector over the key-point counterparts while maintaining the speed advantage.

34, TITLE:      T-Norms Driven Loss Functions for Machine Learning
http://arxiv.org/abs/1907.11468
AUTHORS:        Giuseppe Marra ; Francesco Giannini ; Michelangelo Diligenti ; Marco Maggini ; Marco Gori
HIGHLIGHT:      These approaches can potentially learn competitive solutions with a significant reduction of the amount of supervised data.

35, TITLE:      Contextual Encoder-Decoder Network for Visual Saliency Prediction
http://arxiv.org/abs/1902.06634
AUTHORS:        Alexander Kroner ; Mario Senden ; Kurt Driessens ; Rainer Goebel
COMMENTS:       Accepted Manuscript
HIGHLIGHT:      Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task.

36, TITLE:      Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
http://arxiv.org/abs/2006.12250
AUTHORS:        Haozhe Xie ; Hongxun Yao ; Shengping Zhang ; Shangchen Zhou ; Wenxiu Sun
COMMENTS:       International Journal of Computer Vision (IJCV). arXiv admin note: text overlap with arXiv:1901.11153
HIGHLIGHT:      To address these issues, we propose a novel framework for single-view and multi-view 3D object reconstruction, named Pix2Vox++.

37, TITLE:      TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
http://arxiv.org/abs/2004.07493
AUTHORS:        Bill Yuchen Lin ; Dong-Ho Lee ; Ming Shen ; Ryan Moreno ; Xiao Huang ; Prashant Shiralkar ; Xiang Ren
COMMENTS:       Accepted to the ACL 2020. Project page: https://inklab.usc.edu/TriggerNER/ (Fixed a few typos and added a new figure.)
HIGHLIGHT:      In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models.

38, TITLE:      STAGE: Spatio-Temporal Attention on Graph Entities for Video Action Detection
http://arxiv.org/abs/1912.04316
AUTHORS:        Matteo Tomei ; Lorenzo Baraldi ; Simone Calderara ; Simone Bronzin ; Rita Cucchiara
COMMENTS:       An extended version of this paper is currently under consideration at Computer Vision and Image Understanding
HIGHLIGHT:      In this paper, we develop a high-level video understanding module which can encode interactions between actors and objects both in space and time.

39, TITLE:      SMiRL: Surprise Minimizing Reinforcement Learning in Dynamic Environments
http://arxiv.org/abs/1912.05510
AUTHORS:        Glen Berseth ; Daniel Geng ; Coline Devin ; Nicholas Rhinehart ; Chelsea Finn ; Dinesh Jayaraman ; Sergey Levine
COMMENTS:       PrePrint
HIGHLIGHT:      We propose that such a search for order amidst chaos might offer a unifying principle for the emergence of useful behaviors in artificial agents.

40, TITLE:      Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
http://arxiv.org/abs/2005.13983
AUTHORS:        Weixia Zhang ; Kede Ma ; Guangtao Zhai ; Xiaokang Yang
COMMENTS:       Under review. The implementations are available at https://github.com/zwx8981/UNIQUE
HIGHLIGHT:      To confront the cross-distortion-scenario challenge, we develop a unified BIQA model and an effective approach of training it for both synthetic and realistic distortions.

41, TITLE:      A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
http://arxiv.org/abs/2007.02180
AUTHORS:        Issam Laradji ; Pau Rodriguez ; Oscar Mañas ; Keegan Lensink ; Marco Law ; Lironne Kurzman ; William Parker ; David Vazquez ; Derek Nowrouzezahrai
HIGHLIGHT:      Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images.

42, TITLE:      The Computational Complexity of Finding Temporal Paths under Waiting Time Constraints
http://arxiv.org/abs/1909.06437
AUTHORS:        Arnaud Casteigts ; Anne-Sophie Himmel ; Hendrik Molter ; Philipp Zschoche
HIGHLIGHT:      We explore several natural parameterizations, presenting FPT algorithms for three kinds of parameters: (1) output-related parameters (here, the maximum length of the path), (2) classical parameters applied to the underlying graph (e.g., feedback edge number), and (3) a new parameter called timed feedback vertex number, which captures finer-grained temporal features of the input temporal graph, and which may be of interest beyond this work.

43, TITLE:      Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
http://arxiv.org/abs/2001.08726
AUTHORS:        Jianyu Chen ; Shengbo Eben Li ; Masayoshi Tomizuka
HIGHLIGHT:      In this paper, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios.

44, TITLE:      Learning Trajectory Dependencies for Human Motion Prediction
http://arxiv.org/abs/1908.05436
AUTHORS:        Wei Mao ; Miaomiao Liu ; Mathieu Salzmann ; Hongdong Li
COMMENTS:       Accepted by ICCV2019(Oral)
HIGHLIGHT:      In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.

45, TITLE:      GIQA: Generated Image Quality Assessment
http://arxiv.org/abs/2003.08932
AUTHORS:        Shuyang Gu ; Jianmin Bao ; Dong Chen ; Fang Wen
HIGHLIGHT:      We introduce three GIQA algorithms from two perspectives: learning-based and data-based.

46, TITLE:      Adversarial Model for Rotated Indoor Scenes Planning
http://arxiv.org/abs/2006.13527
AUTHORS:        Xinhan Di ; Pengqian Yu ; Hong Zhu ; Lei Cai ; Qiuyan Sheng ; Changyu Sun
HIGHLIGHT:      In this paper, we propose an adversarial model for producing furniture layout for interior scene synthesis when the interior room is rotated.

47, TITLE:      Dynamic memory to alleviate catastrophic forgetting in continuous learning settings
http://arxiv.org/abs/2007.02639
AUTHORS:        Johannes Hofmanninger ; Matthias Perkonigg ; James A. Brink ; Oleg Pianykh ; Christian Herold ; Georg Langs
COMMENTS:       The first two authors contributed equally. Accepted at MICCAI 2020
HIGHLIGHT:      Here, we address the problem of data shifts in a continuous learning scenario by adapting a model to unseen variations in the source domain while counteracting catastrophic forgetting effects.

48, TITLE:      Pika parsing: reformulating packrat parsing as a dynamic programming algorithm solves the left recursion and error recovery problems
http://arxiv.org/abs/2005.06444
AUTHORS:        Luke A. D. Hutchison
COMMENTS:       Submitted to ACM
HIGHLIGHT:      Pika parsing: reformulating packrat parsing as a dynamic programming algorithm solves the left recursion and error recovery problems

49, TITLE:      ES-MAML: Simple Hessian-Free Meta Learning
http://arxiv.org/abs/1910.01215
AUTHORS:        Xingyou Song ; Wenbo Gao ; Yuxiang Yang ; Krzysztof Choromanski ; Aldo Pacchiano ; Yunhao Tang
COMMENTS:       Published as a conference paper in ICLR 2020. Code can be found in http://github.com/google-research/google-research/tree/master/es_maml
HIGHLIGHT:      We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).

50, TITLE:      Community detection and Social Network analysis based on the Italian wars of the 15th century
http://arxiv.org/abs/2007.02641
AUTHORS:        J. Fumanal-Idocin ; A. Alonso-Betanzos ; O. Cordón ; H. Bustince ; M. Minárová
COMMENTS:       Corrections in: Revamped affinity section, conclusions and minor changes in the introduction. Also, the dynamic delta section is expanded a bit
HIGHLIGHT:      In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network.

51, TITLE:      MetricUNet: Synergistic Image- and Voxel-Level Learning for Precise CT Prostate Segmentation via Online Sampling
http://arxiv.org/abs/2005.07462
AUTHORS:        Kelei He ; Chunfeng Lian ; Ehsan Adeli ; Jing Huo ; Yinghuan Shi ; Yang Gao ; Bing Zhang ; Junfeng Zhang ; Dinggang Shen
HIGHLIGHT:      To address this problem, we propose a two-stage framework, with the first stage to quickly localize the prostate region, and the second stage to precisely segment the prostate by a multi-task UNet architecture.

52, TITLE:      SUPP.AI: Finding Evidence for Supplement-Drug Interactions
http://arxiv.org/abs/1909.08135
AUTHORS:        Lucy Lu Wang ; Oyvind Tafjord ; Arman Cohan ; Sarthak Jain ; Sam Skjonsberg ; Carissa Schoenick ; Nick Botner ; Waleed Ammar
COMMENTS:       ACL Demo 2020
HIGHLIGHT:      To address this challenge, we present SUPP.AI, an application for browsing evidence of supplement-drug interactions (SDIs) extracted from the biomedical literature.

53, TITLE:      Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware
http://arxiv.org/abs/1901.09181
AUTHORS:        Shiwei Liu ; Decebal Constantin Mocanu ; Amarsagar Reddy Ramapuram Matavalam ; Yulong Pei ; Mykola Pechenizkiy
COMMENTS:       16 pages
HIGHLIGHT:      In this paper, we introduce a technique allowing us to train truly sparse neural networks with fixed parameter count throughout training.

54, TITLE:      Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases
http://arxiv.org/abs/2006.03955
AUTHORS:        Wei Guo ; Aylin Caliskan
COMMENTS:       20 pages, 2 figures, 3 tables
HIGHLIGHT:      We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model, dispensing with templates.

55, TITLE:      Real-MFF: A Large Realistic Multi-focus Image Dataset with Ground Truth
http://arxiv.org/abs/2003.12779
AUTHORS:        Juncheng Zhang ; Qingmin Liao ; Shaojun Liu ; Haoyu Ma ; Wenming Yang ; Jing-Hao Xue
HIGHLIGHT:      In this letter, we introduce a large and realistic multi-focus dataset called Real-MFF, which contains 710 pairs of source images with corresponding ground truth images.

56, TITLE:      A Transformer-based approach to Irony and Sarcasm detection
http://arxiv.org/abs/1911.10401
AUTHORS:        Rolandos Alexandros Potamias ; Georgios Siolas ; Andreas - Georgios Stafylopatis
COMMENTS:       Neural Comput & Applic (2020)
HIGHLIGHT:      In the present paper we employ advanced Deep Learning (DL) methodologies to tackle the problem of identifying the aforementioned FL forms.

57, TITLE:      RP2K: A Large-Scale Retail Product Dataset for Fine-Grained Image Classification
http://arxiv.org/abs/2006.12634
AUTHORS:        Jingtian Peng ; Chang Xiao ; Xun Wei ; Yifan Li
HIGHLIGHT:      We introduce RP2K, a new large-scale retail product dataset for fine-grained image classification.

58, TITLE:      Multi-task Ensembles with Crowdsourced Features Improve Skin Lesion Diagnosis
http://arxiv.org/abs/2004.14745
AUTHORS:        Ralf Raumanns ; Elif K Contar ; Gerard Schouten ; Veronika Cheplygina
HIGHLIGHT:      We propose to address this by instead asking the crowd about visual features of the images, which can be provided more intuitively, and by using these features in a multi-task learning framework through ensemble strategies.

59, TITLE:      Gradient Temporal-Difference Learning with Regularized Corrections
http://arxiv.org/abs/2007.00611
AUTHORS:        Sina Ghiassian ; Andrew Patterson ; Shivam Garg ; Dhawal Gupta ; Adam White ; Martha White
COMMENTS:       22 pages. Accepted to ICML 2020
HIGHLIGHT:      In this paper, we introduce a new method called TD with Regularized Corrections (TDRC), that attempts to balance ease of use, soundness, and performance.

60, TITLE:      So What's the Plan? Mining Strategic Planning Documents
http://arxiv.org/abs/2007.00257
AUTHORS:        Ekaterina Artemova ; Tatiana Batura ; Anna Golenkovskaya ; Vitaly Ivanin ; Vladimir Ivanov ; Veronika Sarkisyan ; Ivan Smurov ; Elena Tutubalina
COMMENTS:       15 pages, 3 figures, 5 tables. The paper has been accepted for the Fifth International Conference on Digital Transformation and Global Society (DTGS 2020)
HIGHLIGHT:      In this paper we present a corpus of Russian strategic planning documents, RuREBus.

61, TITLE:      Robust Physical Hard-Label Attacks on Deep Learning Visual Classification
http://arxiv.org/abs/2002.07088
AUTHORS:        Ryan Feng ; Jiefeng Chen ; Nelson Manohar ; Earlence Fernandes ; Somesh Jha ; Atul Prakash
HIGHLIGHT:      We present GRAPHITE, an algorithm to automatically find small areas to place robust adversarial perturbations in the black-box hard-label setting where the attacker only has access to the model prediction class label.

62, TITLE:      CURL: Contrastive Unsupervised Representations for Reinforcement Learning
http://arxiv.org/abs/2004.04136
AUTHORS:        Aravind Srinivas ; Michael Laskin ; Pieter Abbeel
COMMENTS:       First two authors contributed equally, website: https://mishalaskin.github.io/curl code: https://github.com/MishaLaskin/curl
HIGHLIGHT:      We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning.

63, TITLE:      Educating Text Autoencoders: Latent Representation Guidance via Denoising
http://arxiv.org/abs/1905.12777
AUTHORS:        Tianxiao Shen ; Jonas Mueller ; Regina Barzilay ; Tommi Jaakkola
COMMENTS:       ICML 2020 camera-ready
HIGHLIGHT:      Generative autoencoders offer a promising approach for controllable text generation by leveraging their latent sentence representations.

64, TITLE:      Context Model for Pedestrian Intention Prediction using Factored Latent-Dynamic Conditional Random Fields
http://arxiv.org/abs/1907.11881
AUTHORS:        Satyajit Neogi ; Michael Hoy ; Kang Dang ; Hang Yu ; Justin Dauwels
COMMENTS:       Accepted by IEEE Transactions on Intelligent Transportation Systems
HIGHLIGHT:      In this paper, we show a discernible advance in prediction time aided by the inclusion of such vehicle interaction context.