2020.07.02.txt

==========New Papers==========
1, TITLE:       Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance
http://arxiv.org/abs/2007.00548
AUTHORS:        Dominik Rivoir ; Sebastian Bodenstedt ; Isabel Funke ; Felix von Bechtolsheim ; Marius Distler ; Jürgen Weitz ; Stefanie Speidel
HIGHLIGHT:      We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations.

2, TITLE:       Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges
http://arxiv.org/abs/2007.00541
AUTHORS:        Ramses Sala ; Ralf Müller
HIGHLIGHT:      Based on a mini-review of publications with critical comments, advice, and new approaches, this communication aims to give a constructive perspective on several open challenges and prospective research directions related to systematic and generalizable benchmarking for black-box optimization.

3, TITLE:       Deep Learning for Vision-based Prediction: A Survey
http://arxiv.org/abs/2007.00095
AUTHORS:        Amir Rasouli
HIGHLIGHT:      The objective of this paper is to provide an overview of the field in the past five years with a particular focus on deep learning approaches.

4, TITLE:       Robust navigation with tinyML for autonomous mini-vehicles
http://arxiv.org/abs/2007.00302
AUTHORS:        Miguel de Prado ; Romain Donze ; Alessandro Capotondi ; Manuele Rusci ; Serge Monnerat ; Luca Benini and ; Nuria Pazos
HIGHLIGHT:      In this work, we show an end-to-end integration of data, algorithms, and deployment tools that enables the deployment of a family of tiny-CNNs on extra-low-power MCUs for autonomous driving mini-vehicles (image classification task).

5, TITLE:       Learning unbiased zero-shot semantic segmentation networks via transductive transfer
http://arxiv.org/abs/2007.00515
AUTHORS:        Haiyang Liu ; Yichen Wang ; Jiayi Zhao ; Guowu Yang ; Fengmao Lv
HIGHLIGHT:      In this paper, we propose an easy-to-implement transductive approach to alleviate the prediction bias in zero-shot semantic segmentation.

6, TITLE:       Deep Feature Space: A Geometrical Perspective
http://arxiv.org/abs/2007.00062
AUTHORS:        Ioannis Kansizoglou ; Loukas Bampis ; Antonios Gasteratos
HIGHLIGHT:      We present the findings that can be derived from our model's formulation, and we evaluate them on realistic recognition scenarios, proving its prominence by improving the obtained results.

7, TITLE:       Factoring Polynomials over Finite Fields with Linear Galois Groups: An Additive Combinatorics Approach
http://arxiv.org/abs/2007.00512
AUTHORS:        Zeyu Guo
COMMENTS:       To be published in the proceedings of MFCS 2020
HIGHLIGHT:      To prove our main result, we introduce a family of objects called linear $m$-schemes and reduce the problem of factoring $f(X)$ to a combinatorial problem about these objects.

8, TITLE:       Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities
http://arxiv.org/abs/2007.00059
AUTHORS:        Noam Moscovich ; Ron Bitton ; Yakov Mallah ; Masaki Inokuchi ; Tomohiko Yagyu ; Meir Kalech ; Yuval Elovici ; Asaf Shabtai
HIGHLIGHT:      Since testing all possible system configurations is infeasible, we introduce an efficient approach for testing and searching through all possible configurations of the environment.

9, TITLE:       A Fast Algorithm for Geodesic Active Contours with Applications to Medical Image Segmentation
http://arxiv.org/abs/2007.00525
AUTHORS:        Jun Ma ; Dong Wang ; Xiao-Ping Wang ; Xiaoping Yang
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we use characteristic functions to implicitly approximate the contours, propose a new representation to the GAC and derive an efficient algorithm termed as the iterative convolution-thresholding method (ICTM).

10, TITLE:      Adversarial Mutual Information for Text Generation
http://arxiv.org/abs/2007.00067
AUTHORS:        Boyuan Pan ; Yazheng Yang ; Kaizhao Liang ; Bhavya Kailkhura ; Zhongming Jin ; Xian-Sheng Hua ; Deng Cai ; Bo Li
COMMENTS:       Published at ICML 2020
HIGHLIGHT:      In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between the source and target.

11, TITLE:      Drug discovery with explainable artificial intelligence
http://arxiv.org/abs/2007.00523
AUTHORS:        José Jiménez-Luna ; Francesca Grisoni ; Gisbert Schneider
HIGHLIGHT:      This review summarizes the most prominent algorithmic concepts of explainable artificial intelligence, and dares a forecast of the future opportunities, potential applications, and remaining challenges.

12, TITLE:      Is Robustness To Transformations Driven by Invariant Neural Representations?
http://arxiv.org/abs/2007.00112
AUTHORS:        Syed Suleman Abbas Zaidi ; Xavier Boix ; Neeraj Prasad ; Sharon Gilad-Gutnick ; Shlomit Ben-Ami ; Pawan Sinha
HIGHLIGHT:      In this paper, we analyze the conditions under which invariance emerges.

13, TITLE:      Deep Geometric Texture Synthesis
http://arxiv.org/abs/2007.00074
AUTHORS:        Amir Hertz ; Rana Hanocka ; Raja Giryes ; Daniel Cohen-Or
COMMENTS:       SIGGRAPH 2020
HIGHLIGHT:      In this work, we propose a novel framework for synthesizing geometric textures.

14, TITLE:      A New Basis for Sparse PCA
http://arxiv.org/abs/2007.00596
AUTHORS:        Fan Chen ; Karl Rohe
COMMENTS:       33 pages, 8 figures
HIGHLIGHT:      For this, we propose a new method for sparse PCA.

15, TITLE:      Intention-aware Residual Bidirectional LSTM for Long-term Pedestrian Trajectory Prediction
http://arxiv.org/abs/2007.00113
AUTHORS:        Zhe Huang ; Aamir Hasan ; Katherine Driggs-Campbell
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      We present a novel intention-aware motion prediction framework, which consists of a Residual Bidirectional LSTM (ReBiL) and a mutable intention filter.

16, TITLE:      Few-shots Parameter Tuning via Co-evolution
http://arxiv.org/abs/2007.00501
AUTHORS:        Ke Tang ; Shengcai Liu ; Peng Yang ; Xin Yao
HIGHLIGHT:      This paper suggests competitive co-evolution as a remedy to this challenge and proposes a framework named Co-Evolution of Parameterized Search (CEPS).

17, TITLE:      FathomNet: An underwater image training database for ocean exploration and discovery
http://arxiv.org/abs/2007.00114
AUTHORS:        Océane Boulais ; Ben Woodward ; Brian Schlining ; Lonny Lundsten ; Kevin Barnard ; Katy Croff Bell ; Kakani Katija
COMMENTS:       8 pages, 6 figures, NeurIPS 2020
HIGHLIGHT:      FathomNet: An underwater image training database for ocean exploration and discovery

18, TITLE:      Can Global Optimization Strategy Outperform Myopic Strategy for Bayesian Parameter Estimation?
http://arxiv.org/abs/2007.00373
AUTHORS:        Juanping Zhu ; Hairong Gu
HIGHLIGHT:      This paper provides a discouraging answer based on experimental simulations comparing the performance improvement and computation burden between global and myopic strategies in parameter estimation of multiple models.

19, TITLE:      Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition
http://arxiv.org/abs/2007.00131
AUTHORS:        Maarten Van Segbroeck ; Harish Mallidih ; Brian King ; I-Fan Chen ; Gurpreet Chadha ; Roland Maas
HIGHLIGHT:      A drawback of FLSTM based architectures however is that they operate at a predefined, and tuned, window size and stride, referred to as 'view' in this paper.

20, TITLE:      Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study
http://arxiv.org/abs/2007.00121
AUTHORS:        Elena A. Kaye ; Emily A. Aherne ; Cihan Duzgol ; Ida Häggström ; Erich Kobler ; Yousef Mazaheri ; Maggie M Fung ; Zhigang Zhang ; Ricardo Otazo ; Herbert A. Vargas ; Oguz Akin
COMMENTS:       This manuscript has been accepted for publication in Radiology: Artificial Intelligence (https://pubs.rsna.org/journal/ai), which is published by the Radiological Society of North America (RSNA)
HIGHLIGHT:      A cumulative link mixed regression model was used to compare the readers scores.

21, TITLE:      Medical idioms for clinical Bayesian network development
http://arxiv.org/abs/2007.00364
AUTHORS:        Evangelia Kyrimi ; Mariana Raniere Neves ; Scott McLachlan ; Martin Neil ; William Marsh ; Norman Fenton
HIGHLIGHT:      This paper proposes generally applicable and reusable medical reasoning patterns to aid those developing medical BNs.

22, TITLE:      Situation Calculus by Term Rewriting
http://arxiv.org/abs/2007.00125
AUTHORS:        David A. Plaisted
HIGHLIGHT:      Some examples are given, and a few general methods for constructing such sets of rewrite rules are presented.

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

24, 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 ; David Liem ; Ahmed Elsayed ; Martha Palmer ; Jasmine Rah ; 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

25, TITLE:      Multimodal Text Style Transfer for Outdoor Vision-and-Language Navigation
http://arxiv.org/abs/2007.00229
AUTHORS:        Wanrong Zhu ; Xin Wang ; Tsu-Jui Fu ; An Yan ; Pradyumna Narayana ; Kazoo Sone ; Sugato Basu ; William Yang Wang
HIGHLIGHT:      In this paper, we introduce a Multimodal Text Style Transfer (MTST) learning approach to mitigate the problem of data scarcity in outdoor navigation tasks by effectively leveraging external multimodal resources.

26, TITLE:      A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing
http://arxiv.org/abs/2007.00463
AUTHORS:        Richa Verma ; Aniruddha Singhal ; Harshad Khadilkar ; Ansuma Basumatary ; Siddharth Nayak ; Harsh Vardhan Singh ; Swagat Kumar ; Rajesh Sinha
COMMENTS:       9 pages, 9 figures
HIGHLIGHT:      We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size.

27, TITLE:      Low-light Image Restoration with Short- and Long-exposure Raw Pairs
http://arxiv.org/abs/2007.00199
AUTHORS:        Meng Chang ; Huajun Feng ; Zhihai Xu ; Qi Li
HIGHLIGHT:      In this paper, we propose a new low-light image restoration method by using the complementary information of short- and long-exposure images.

28, TITLE:      Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings
http://arxiv.org/abs/2007.00183
AUTHORS:        Bowen Shi ; Shane Settle ; Karen Livescu
HIGHLIGHT:      We describe an efficient approach for end-to-end whole-word segmental models, with forward-backward and Viterbi decoding performed on a GPU and a simple segment scoring function that reduces space complexity.

29, TITLE:      Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
http://arxiv.org/abs/2007.00178
AUTHORS:        Zhangjie Cao ; Erdem Bıyık ; Woodrow Z. Wang ; Allan Raventos ; Adrien Gaidon ; Guy Rosman ; Dorsa Sadigh
COMMENTS:       10 pages, 7 figures. Published at Robotics: Science and Systems (RSS) 2020
HIGHLIGHT:      To address driving in near-accident scenarios, we propose a hierarchical reinforcement and imitation learning (H-ReIL) approach that consists of low-level policies learned by IL for discrete driving modes, and a high-level policy learned by RL that switches between different driving modes.

30, TITLE:      Learning an arbitrary mixture of two multinomial logits
http://arxiv.org/abs/2007.00204
AUTHORS:        Wenpin Tang
COMMENTS:       12 pages
HIGHLIGHT:      In this paper, we consider mixtures of multinomial logistic models (MNL), which are known to $\epsilon$-approximate any random utility model.

31, TITLE:      Swapping Autoencoder for Deep Image Manipulation
http://arxiv.org/abs/2007.00653
AUTHORS:        Taesung Park ; Jun-Yan Zhu ; Oliver Wang ; Jingwan Lu ; Eli Shechtman ; Alexei A. Efros ; Richard Zhang
HIGHLIGHT:      We propose the Swapping Autoencoder, a deep model designed specifically for image manipulation, rather than random sampling.

32, TITLE:      End-to-End JPEG Decoding and Artifacts Suppression Using Heterogeneous Residual Convolutional Neural Network
http://arxiv.org/abs/2007.00639
AUTHORS:        Jun Niu
HIGHLIGHT:      In this work, we take one step forward to design a true end-to-end heterogeneous residual convolutional neural network (HR-CNN) with spectrum decomposition and heterogeneous reconstruction mechanism.

33, TITLE:      Verification of indefinite-horizon POMDPs
http://arxiv.org/abs/2007.00102
AUTHORS:        Alexander Bork ; Sebastian Junges ; Joost-Pieter Katoen ; Tim Quatmann
COMMENTS:       Technical report for ATVA 2020 paper with the same title
HIGHLIGHT:      We present an abstraction-refinement framework extending previous instantiations of the Lovejoy-approach.

34, TITLE:      Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series
http://arxiv.org/abs/2007.00586
AUTHORS:        Vivien Sainte Fare Garnot ; Loic Landrieu
HIGHLIGHT:      Building on recent work employing multi-headed self-attention mechanisms to classify remote sensing time sequences, we propose a modification of the Temporal Attention Encoder.

35, TITLE:      HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
http://arxiv.org/abs/2007.00584
AUTHORS:        Pushpak Pati ; Guillaume Jaume ; Lauren Alisha Fernandes ; Antonio Foncubierta ; Florinda Feroce ; Anna Maria Anniciello ; Giosue Scognamiglio ; Nadia Brancati ; Daniel Riccio ; Maurizio Do Bonito ; Giuseppe De Pietro ; Gerardo Botti ; Orcun Goksel ; Jean-Philippe Thiran ; Maria Frucci ; Maria Gabrani
HIGHLIGHT:      We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue.

36, TITLE:      Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models
http://arxiv.org/abs/2007.00083
AUTHORS:        Sahar Siddiqui ; Elena Sizikova ; Gemma Roig ; Najib J. Majaj ; Denis G. Pelli
HIGHLIGHT:      Inspired by human reading we characterize two important scene text recognition models by measuring their domains i.e. the range of stimulus images that they can read.

37, TITLE:      FVV Live: A real-time free-viewpoint video system with consumer electronics hardware
http://arxiv.org/abs/2007.00558
AUTHORS:        Pablo Carballeira ; Carlos Carmona ; César Díaz ; Daniel Berjón ; Daniel Corregidor ; Julián Cabrera ; Francisco Morán ; Carmen Doblado ; Sergio Arnaldo ; María del Mar Martín ; Narciso García
HIGHLIGHT:      The paper describes the architecture of the system, including acquisition and encoding of multiview plus depth data in several capture servers and virtual view synthesis on an edge server.

38, TITLE:      Towards Explainable Graph Representations in Digital Pathology
http://arxiv.org/abs/2007.00311
AUTHORS:        Guillaume Jaume ; Pushpak Pati ; Antonio Foncubierta-Rodriguez ; Florinda Feroce ; Giosue Scognamiglio ; Anna Maria Anniciello ; Jean-Philippe Thiran ; Orcun Goksel ; Maria Gabrani
COMMENTS:       ICML'20 workshop on Computational Biology
HIGHLIGHT:      In this paper, we introduce a post-hoc explainer to derive compact per-instance explanations emphasizing diagnostically important entities in the graph.

39, TITLE:      Enforcing Almost-Sure Reachability in POMDPs
http://arxiv.org/abs/2007.00085
AUTHORS:        Sebastian Junges ; Nils Jansen ; Sanjit A. Seshia
HIGHLIGHT:      In particular, we present an iterative symbolic approach that computes a winning region, that is, a set of system configurations such that all policies that stay within this set are guaranteed to satisfy the constraints.

40, TITLE:      Online Domain Adaptation for Occupancy Mapping
http://arxiv.org/abs/2007.00164
AUTHORS:        Anthony Tompkins ; Ransalu Senanayake ; Fabio Ramos
COMMENTS:       Robotics: Science and Systems (RSS) 2020 conference
HIGHLIGHT:      Recognizing the fact that real-world structures exhibit similar geometric features across a variety of urban environments, in this paper, we argue that it is redundant to learn all geometry dependent parameters from scratch.

41, TITLE:      Directional Primitives for Uncertainty-Aware Motion Estimation in Urban Environments
http://arxiv.org/abs/2007.00161
AUTHORS:        Ransalu Senanayake ; Maneekwan Toyungyernsub ; Mingyu Wang ; Mykel J. Kochenderfer ; Mac Schwager
COMMENTS:       The 23rd IEEE International Conference on Intelligent Transportation Systems. September, 2020
HIGHLIGHT:      In this paper, we introduce the concept of directional primitives, which is a representation of prior information of road networks.

42, TITLE:      A Novel RL-assisted Deep Learning Framework for Task-informative Signals Selection and Classification for Spontaneous BCIs
http://arxiv.org/abs/2007.00162
AUTHORS:        Wonjun Ko ; Eunjin Jeon ; Heung-Il Suk
COMMENTS:       8 pages, 6 figures, 2 tables, and under review
HIGHLIGHT:      In this work, we formulate the problem of estimating and selecting task-relevant temporal signal segments from a single EEG trial in the form of a Markov decision process and propose a novel reinforcement-learning mechanism that can be combined with the existing deep-learning based BCI methods.

43, TITLE:      Similarity Search for Efficient Active Learning and Search of Rare Concepts
http://arxiv.org/abs/2007.00077
AUTHORS:        Cody Coleman ; Edward Chou ; Sean Culatana ; Peter Bailis ; Alexander C. Berg ; Roshan Sumbaly ; Matei Zaharia ; I. Zeki Yalniz
HIGHLIGHT:      In this work, we exploit this skew in large training datasets to reduce the number of unlabeled examples considered in each selection round by only looking at the nearest neighbors to the labeled examples.

44, TITLE:      DocVQA: A Dataset for VQA on Document Images
http://arxiv.org/abs/2007.00398
AUTHORS:        Minesh Mathew ; Dimosthenis Karatzas ; R. Manmatha ; C. V. Jawahar
HIGHLIGHT:      We present a new dataset for Visual Question Answering on document images called DocVQA.

45, TITLE:      Reasoning with Contextual Knowledge and Influence Diagrams
http://arxiv.org/abs/2007.00571
AUTHORS:        Erman Acar ; Rafael Peñaloza
HIGHLIGHT:      We define related reasoning problems and study their computational complexity.

46, TITLE:      NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models
http://arxiv.org/abs/2007.00328
AUTHORS:        Hui Li ; Xiao-Jun Wu ; Tariq Durrani
COMMENTS:       12 pages, 13 figures, 6 tables. IEEE Transactions on Instrumentation and Measurement
HIGHLIGHT:      In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models.

47, TITLE:      Iterative Paraphrastic Augmentation with Discriminative Span Alignment
http://arxiv.org/abs/2007.00320
AUTHORS:        Ryan Culkin ; J. Edward Hu ; Elias Stengel-Eskin ; Guanghui Qin ; Benjamin Van Durme
HIGHLIGHT:      We introduce a novel paraphrastic augmentation strategy based on sentence-level lexically constrained paraphrasing and discriminative span alignment.

48, TITLE:      Future Urban Scenes Generation Through Vehicles Synthesis
http://arxiv.org/abs/2007.00323
AUTHORS:        Alessandro Simoni ; Luca Bergamini ; Andrea Palazzi ; Simone Calderara ; Rita Cucchiara
HIGHLIGHT:      In this work we propose a deep learning pipeline to predict the visual future appearance of an urban scene.

49, TITLE:      Fused Text Recogniser and Deep Embeddings Improve Word Recognition and Retrieval
http://arxiv.org/abs/2007.00166
AUTHORS:        Siddhant Bansal ; Praveen Krishnan ; C. V. Jawahar
COMMENTS:       15 pages, 8 figures, Accepted in IAPR International Workshop on Document Analysis Systems (DAS) 2020, "Visit project page, at http://cvit.iiit.ac.in/research/projects/cvit-projects/fused-text-recogniser-and-deep-embeddings-improve-word-recognition-and-retrieval"
HIGHLIGHT:      In this paper, we fuse the noisy output of text recogniser with a deep embeddings representation derived out of the entire word.

50, TITLE:      Causal Discovery in Physical Systems from Videos
http://arxiv.org/abs/2007.00631
AUTHORS:        Yunzhu Li ; Antonio Torralba ; Animashree Anandkumar ; Dieter Fox ; Animesh Garg
COMMENTS:       Project page: https://yunzhuli.github.io/V-CDN/
HIGHLIGHT:      In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of the dynamical system.

51, TITLE:      Unsupervised Semantic Hashing with Pairwise Reconstruction
http://arxiv.org/abs/2007.00380
AUTHORS:        Casper Hansen ; Christian Hansen ; Jakob Grue Simonsen ; Stephen Alstrup ; Christina Lioma
COMMENTS:       Accepted at SIGIR'20
HIGHLIGHT:      Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model.

52, TITLE:      Adversarial Open Set Domain Adaptation Based on Mutual Information
http://arxiv.org/abs/2007.00384
AUTHORS:        Tasfia Shermin ; Guojun Lu ; Ferdous Sohel ; Shyh Wei Teng ; Manzur Murshed
HIGHLIGHT:      To this end, we propose a novel approach to OSDA, Domain Adaptation based on Mutual Information (DAMI).

53, TITLE:      Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNet
http://arxiv.org/abs/2007.00649
AUTHORS:        Hao Chen ; Abhinav Shrivastava
HIGHLIGHT:      In this paper, we present Group Ensemble Network (GENet), an architecture incorporating an ensemble of ConvNets in a single ConvNet.

54, TITLE:      Measuring Robustness to Natural Distribution Shifts in Image Classification
http://arxiv.org/abs/2007.00644
AUTHORS:        Rohan Taori ; Achal Dave ; Vaishaal Shankar ; Nicholas Carlini ; Benjamin Recht ; Ludwig Schmidt
HIGHLIGHT:      We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets.

55, TITLE:      Convex Regularization in Monte-Carlo Tree Search
http://arxiv.org/abs/2007.00391
AUTHORS:        Tuan Dam ; Carlo D'Eramo ; Jan Peters ; Joni Pajarinen
HIGHLIGHT:      In this paper, we overcome these limitations by considering convex regularization in Monte-Carlo Tree Search (MCTS), which has been successfully used in RL to efficiently drive exploration.

56, TITLE:      Object Goal Navigation using Goal-Oriented Semantic Exploration
http://arxiv.org/abs/2007.00643
AUTHORS:        Devendra Singh Chaplot ; Dhiraj Gandhi ; Abhinav Gupta ; Ruslan Salakhutdinov
COMMENTS:       Winner of the CVPR 2020 AI-Habitat Object Goal Navigation Challenge. See the project webpage at https://devendrachaplot.github.io/projects/SemExp
HIGHLIGHT:      We propose a modular system called, `Goal-Oriented Semantic Exploration' which builds an episodic semantic map and uses it to explore the environment efficiently based on the goal object category.

57, TITLE:      The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
http://arxiv.org/abs/2007.00394
AUTHORS:        Yizhak Ben-Shabat ; Xin Yu ; Fatemeh Sadat Saleh ; Dylan Campbell ; Cristian Rodriguez-Opazo ; Hongdong Li ; Stephen Gould
HIGHLIGHT:      To enable richer analysis and understanding of human activities, we introduce IKEA ASM---a three million frame, multi-view, furniture assembly video dataset that includes depth, atomic actions, object segmentation, and human pose.

58, TITLE:      Equational Reasoning for MTL Type Classes
http://arxiv.org/abs/2007.00616
AUTHORS:        Härmel Nestra
HIGHLIGHT:      This is impossible in the case of Haskell type class methods unless a particular instance type is specified, since class methods can be defined differently for different instances.

59, TITLE:      Early-Learning Regularization Prevents Memorization of Noisy Labels
http://arxiv.org/abs/2007.00151
AUTHORS:        Sheng Liu ; Jonathan Niles-Weed ; Narges Razavian ; Carlos Fernandez-Granda
HIGHLIGHT:      We propose a novel framework to perform classification via deep learning in the presence of noisy annotations.

60, 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.

61, TITLE:      Modality-Agnostic Attention Fusion for visual search with text feedback
http://arxiv.org/abs/2007.00145
AUTHORS:        Eric Dodds ; Jack Culpepper ; Simao Herdade ; Yang Zhang ; Kofi Boakye
COMMENTS:       14 pages, 8 figures
HIGHLIGHT:      Our Modality-Agnostic Attention Fusion (MAAF) model combines image and text features and outperforms existing approaches on two visual search with modifying phrase datasets, Fashion IQ and CSS, and performs competitively on a dataset with only single-word modifications, Fashion200k. We also introduce two new challenging benchmarks adapted from Birds-to-Words and Spot-the-Diff, which provide new settings with rich language inputs, and we show that our approach without modification outperforms strong baselines.

62, TITLE:      Latent Compositional Representations Improve Systematic Generalization in Grounded Question Answering
http://arxiv.org/abs/2007.00266
AUTHORS:        Ben Bogin ; Sanjay Subramanian ; Matt Gardner ; Jonathan Berant
HIGHLIGHT:      In this work, we propose a model that computes a representation and denotation for all question spans in a bottom-up, compositional manner using a CKY-style parser.

63, TITLE:      Data-driven Regularization via Racecar Training for Generalizing Neural Networks
http://arxiv.org/abs/2007.00024
AUTHORS:        You Xie ; Nils Thuerey
COMMENTS:       https://github.com/tum-pbs/racecar
HIGHLIGHT:      We propose a novel training approach for improving the generalization in neural networks.

64, TITLE:      Generating Adversarial Examples with an Optimized Quality
http://arxiv.org/abs/2007.00146
AUTHORS:        Aminollah Khormali ; DaeHun Nyang ; David Mohaisen
HIGHLIGHT:      In this paper, we incorporateImage Quality Assessment (IQA) metrics into the design and generation process of AEs.

65, TITLE:      Enhancing the Association in Multi-Object Tracking via Neighbor Graph
http://arxiv.org/abs/2007.00265
AUTHORS:        Tianyi Liang ; Long Lan ; Zhigang Luo
HIGHLIGHT:      In this work, we propose to handle this problem via making full use of the neighboring information.

66, TITLE:      A Dataset for Evaluating Multi-spectral Motion Estimation Methods
http://arxiv.org/abs/2007.00622
AUTHORS:        Weichen Dai ; Yu Zhang ; Shenzhou Chen ; Donglei Sun ; Da Kong
HIGHLIGHT:      In this paper, a novel dataset for evaluating the performance of multi-spectral motion estimation systems is presented.

67, TITLE:      Effects for Efficiency: Asymptotic Speedup with First-Class Control
http://arxiv.org/abs/2007.00605
AUTHORS:        Daniel Hillerström ; Sam Lindley ; John Longley
HIGHLIGHT:      We consider the generic count problem using a pure PCF-like base language $\lambda_b$ and its extension with effect handlers $\lambda_h$.

68, TITLE:      Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction
http://arxiv.org/abs/2007.00603
AUTHORS:        Christina Runkel ; Stefan Dorenkamp ; Hartmut Bauermeister ; Michael Moeller
COMMENTS:       First two authors contributed equally. Accepted at ICPR 2020
HIGHLIGHT:      The contribution of this paper is to propose a convolutional neural network for spell-correction that expects the softmax outputs of the character recognition network (instead of a misspelled word) as an input.

69, TITLE:      FlowControl: Optical Flow Based Visual Servoing
http://arxiv.org/abs/2007.00291
AUTHORS:        Max Argus ; Lukas Hermann ; Jon Long ; Thomas Brox
HIGHLIGHT:      We present a practical method for realizing one-shot imitation for manipulation tasks, exploiting modern learning-based optical flow to perform real-time visual servoing.

70, TITLE:      Robust Semantic Segmentation in Adverse Weather Conditions by means of Fast Video-Sequence Segmentation
http://arxiv.org/abs/2007.00290
AUTHORS:        Andreas Pfeuffer ; Klaus Dietmayer
HIGHLIGHT:      Hence, in this work, the LSTM-ICNet is sped up by modifying the recurrent units of the network so that it becomes real-time capable again.

71, TITLE:      Automatic Crack Detection on Road Pavements Using Encoder Decoder Architecture
http://arxiv.org/abs/2007.00477
AUTHORS:        Zhun Fan ; Chong Li ; Ying Chen ; Jiahong Wei ; Giuseppe Loprencipe ; Xiaopeng Chen ; Paola Di Mascio
HIGHLIGHT:      Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method.

72, TITLE:      SemEval-2020 Task 4: Commonsense Validation and Explanation
http://arxiv.org/abs/2007.00236
AUTHORS:        Cunxiang Wang ; Shuailong Liang ; Yili Jin ; Yilong Wang ; Xiaodan Zhu ; Yue Zhang
COMMENTS:       Task description paper of SemEval-2020 Task 4: Commonsense Validation and Explanation
HIGHLIGHT:      In this paper, we present SemEval-2020 Task 4, Commonsense Validation and Explanation (ComVE), which includes three subtasks, aiming to evaluate whether a system can distinguish a natural language statement that makes sense to human from one that does not, and provide the reasons.

73, TITLE:      Unifying Model Explainability and Robustness via Machine-Checkable Concepts
http://arxiv.org/abs/2007.00251
AUTHORS:        Vedant Nanda ; Till Speicher ; John P. Dickerson ; Krishna P. Gummadi ; Muhammad Bilal Zafar
COMMENTS:       22 pages, 12 figures, 11 tables
HIGHLIGHT:      In this paper, we propose a robustness-assessment framework, at the core of which is the idea of using machine-checkable concepts.

74, TITLE:      Optimisation of the PointPillars network for 3D object detection in point clouds
http://arxiv.org/abs/2007.00493
AUTHORS:        Joanna Stanisz ; Konrad Lis ; Tomasz Kryjak ; Marek Gorgon
COMMENTS:       7 pages, 2 figures, submitted to SPA 2020 conference
HIGHLIGHT:      In this paper we present our research on the optimisation of a deep neural network for 3D object detection in a point cloud.

75, TITLE:      Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking
http://arxiv.org/abs/2007.00491
AUTHORS:        Dominika Przewlocka ; Mateusz Wasala ; Hubert Szolc ; Krzysztof Blachut ; Tomasz Kryjak
COMMENTS:       12 pages, accepted for ICCVG 2020
HIGHLIGHT:      In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented.

76, TITLE:      Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
http://arxiv.org/abs/2007.00487
AUTHORS:        Timoth'ee Lesort
COMMENTS:       Thesis Manuscript
HIGHLIGHT:      In this thesis, we propose to explore continual algorithms with replay processes.

77, TITLE:      OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
http://arxiv.org/abs/2007.00049
AUTHORS:        Sunipa Dev ; Tao Li ; Jeff M Phillips ; Vivek Srikumar
HIGHLIGHT:      To address this challenge, we propose OSCaR (Orthogonal Subspace Correction and Rectification), a bias-mitigating method that focuses on disentangling biased associations between concepts instead of removing concepts wholesale.

78, TITLE:      BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture
http://arxiv.org/abs/2007.00243
AUTHORS:        Tiange Xiang ; Chaoyi Zhang ; Dongnan Liu ; Yang Song ; Heng Huang ; Weidong Cai
COMMENTS:       10 pages, 4 figures, MICCAI2020
HIGHLIGHT:      To tackle this issue in such U-Net variants, in this paper, we present a novel Bi-directional O-shape network (BiO-Net) that reuses the building blocks in a recurrent manner without introducing any extra parameters.

79, TITLE:      Transferability of Natural Language Inference to Biomedical Question Answering
http://arxiv.org/abs/2007.00217
AUTHORS:        Minbyul Jeong ; Mujeen Sung ; Gangwoo Kim ; Donghyeon Kim ; Wonjin Yoon ; Jaehyo Yoo ; Jaewoo Kang
COMMENTS:       submit for the 8th BioASQ workshop 2020
HIGHLIGHT:      In this paper, we focus on facilitating the transferability by unifying the experimental setup from natural language inference (NLI) to biomedical QA.

80, TITLE:      M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning
http://arxiv.org/abs/2007.00453
AUTHORS:        Karol Gotkowski ; Camila Gonzalez ; Andreas Bucher ; Anirban Mukhopadhyay
HIGHLIGHT:      M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning

81, TITLE:      So What's the Plan? Mining Strategic Planning Document
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.

82, TITLE:      Improvement on Extrapolation of Species Abundance Distribution Across Scales from Moments Across Scales
http://arxiv.org/abs/2007.00451
AUTHORS:        Saeid Alirezazadeh ; Khadijeh Alibabaei
HIGHLIGHT:      The main result is introducing new techniques for evaluating a more accurate species abundance distributions across scales through moments across scales.



==========Updates to Previous Papers==========
1, TITLE:       A Brief Look at Generalization in Visual Meta-Reinforcement Learning
http://arxiv.org/abs/2006.07262
AUTHORS:        Safa Alver ; Doina Precup
COMMENTS:       8 pages, 4 figures
HIGHLIGHT:      In this paper, we assess the generalization performance of these algorithms by leveraging high-dimensional, procedurally generated environments.

2, TITLE:       Unsupervised video summarization framework using keyframe extraction and video skimming
http://arxiv.org/abs/1910.04792
AUTHORS:        Shruti Jadon ; Mahmood Jasim
COMMENTS:       5 pages, 3 figures. Technical Report
HIGHLIGHT:      In this paper, we attempt to solve video summarization through unsupervised learning by employing traditional vision-based algorithmic methodologies for accurate feature extraction from video frames.

3, TITLE:       Hybrid Deep Learning for Detecting Lung Diseases from X-ray Images
http://arxiv.org/abs/2003.00682
AUTHORS:        Subrato Bharati ; Prajoy Podder ; M. Rubaiyat Hossain Mondal
COMMENTS:       13 figures
HIGHLIGHT:      Therefore, we propose a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN.

4, TITLE:       hxtorch: PyTorch for BrainScaleS-2 -- Perceptrons on Analog Neuromorphic Hardware
http://arxiv.org/abs/2006.13138
AUTHORS:        Philipp Spilger ; Eric Müller ; Arne Emmel ; Aron Leibfried ; Christian Mauch ; Christian Pehle ; Johannes Weis ; Oliver Breitwieser ; Sebastian Billaudelle ; Sebastian Schmitt ; Timo C. Wunderlich ; Yannik Stradmann ; Johannes Schemmel
HIGHLIGHT:      As an application of the introduced framework, we present a model that classifies activities of daily living with smartphone sensor data.

5, TITLE:       XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
http://arxiv.org/abs/2003.08561
AUTHORS:        Sung Whan Yoon ; Do-Yeon Kim ; Jun Seo ; Jaekyun Moon
COMMENTS:       In Proceedings of the 37th International Conference on Machine Learning (ICML) 2020, Vienna, Austria, PMLR 119; *Equal contribution
HIGHLIGHT:      We propose XtarNet, which learns to extract task-adaptive representation (TAR) for facilitating incremental few-shot learning.

6, TITLE:       Frivolous Units Help to Explain Non-Overfitting in Overparametrized Deep Neural Networks
http://arxiv.org/abs/1912.04783
AUTHORS:        Stephen Casper ; Xavier Boix ; Vanessa D'Amario ; Ling Guo ; Martin Schrimpf ; Kasper Vinken ; Gabriel Kreiman
HIGHLIGHT:      We identify two distinct types of "frivolous" units that proliferate when the network's width is increased: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others.

7, TITLE:       HYDRA: Pruning Adversarially Robust Neural Networks
http://arxiv.org/abs/2002.10509
AUTHORS:        Vikash Sehwag ; Shiqi Wang ; Prateek Mittal ; Suman Jana
COMMENTS:       20 pages, 12 figures, 12 tables
HIGHLIGHT:      To overcome this challenge, we propose to make pruning techniques aware of the robust training objective and let the training objective guide the search for which connections to prune.

8, TITLE:       A Deep Learning-Based Method for Automatic Segmentation of Proximal Femur from Quantitative Computed Tomography Images
http://arxiv.org/abs/2006.05513
AUTHORS:        Chen Zhao ; Joyce H. Keyak ; Jinshan Tang ; Tadashi S. Kaneko ; Sundeep Khosla ; Shreyasee Amin ; Elizabeth J. Atkinson ; Lan-Juan Zhao ; Michael J. Serou ; Chaoyang Zhang ; Hui Shen ; Hong-Wen Deng ; Weihua Zhou
HIGHLIGHT:      We aim to develop a deep-learning-based method for automatic proximal femur segmentation.

9, TITLE:       Data-Efficient Image Recognition with Contrastive Predictive Coding
http://arxiv.org/abs/1905.09272
AUTHORS:        Olivier J. Hénaff ; Aravind Srinivas ; Jeffrey De Fauw ; Ali Razavi ; Carl Doersch ; S. M. Ali Eslami ; Aaron van den Oord
HIGHLIGHT:      We therefore revisit and improve Contrastive Predictive Coding, an unsupervised objective for learning such representations.

10, TITLE:      Inference with Artificial Neural Networks on Analog Neuromorphic Hardware
http://arxiv.org/abs/2006.13177
AUTHORS:        Johannes Weis ; Philipp Spilger ; Sebastian Billaudelle ; Yannik Stradmann ; Arne Emmel ; Eric Müller ; Oliver Breitwieser ; Andreas Grübl ; Joscha Ilmberger ; Vitali Karasenko ; Mitja Kleider ; Christian Mauch ; Korbinian Schreiber ; Johannes Schemmel
HIGHLIGHT:      In this paper, we discuss BrainScaleS-2 as an analog inference accelerator and present calibration as well as optimization strategies, highlighting the advantages of training with hardware in the loop.

11, TITLE:      CORD-19: The COVID-19 Open Research Dataset
http://arxiv.org/abs/2004.10706
AUTHORS:        Lucy Lu Wang ; Kyle Lo ; Yoganand Chandrasekhar ; Russell Reas ; Jiangjiang Yang ; Doug Burdick ; Darrin Eide ; Kathryn Funk ; Yannis Katsis ; Rodney Kinney ; Yunyao Li ; Ziyang Liu ; William Merrill ; Paul Mooney ; Dewey Murdick ; Devvret Rishi ; Jerry Sheehan ; Zhihong Shen ; Brandon Stilson ; Alex Wade ; Kuansan Wang ; Nancy Xin Ru Wang ; Chris Wilhelm ; Boya Xie ; Douglas Raymond ; Daniel S. Weld ; Oren Etzioni ; Sebastian Kohlmeier
COMMENTS:       9 pages, 3 figures, 3 tables, 1 appendix
HIGHLIGHT:      In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and describe several shared tasks built around the dataset.

12, TITLE:      Multistage s-t Path: Confronting Similarity with Dissimilarity
http://arxiv.org/abs/2002.07569
AUTHORS:        Till Fluschnik ; Rolf Niedermeier ; Carsten Schubert ; Philipp Zschoche
HIGHLIGHT:      Motivated by this fact and natural applications of this scenario e.g. in traffic route planning, we perform a parameterized complexity analysis.

13, TITLE:      Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research
http://arxiv.org/abs/2005.00357
AUTHORS:        Soujanya Poria ; Devamanyu Hazarika ; Navonil Majumder ; Rada Mihalcea
HIGHLIGHT:      In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field that are necessary to attain true sentiment understanding.

14, TITLE:      Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
http://arxiv.org/abs/2006.15271
AUTHORS:        Dongdong Chen ; Mike E. Davies ; Mohammad Golbabaee
COMMENTS:       To appear in MICCAI 2020
HIGHLIGHT:      To address this, we propose ProxNet, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism.

15, TITLE:      Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
http://arxiv.org/abs/2006.14779
AUTHORS:        Gagan Bansal ; Tongshuang Wu ; Joyce Zhou ; Raymond Fok ; Besmira Nushi ; Ece Kamar ; Marco Tulio Ribeiro ; Daniel S. Weld
COMMENTS:       Draft/pre-print
HIGHLIGHT:      Can we develop explanatory approaches that help humans decide whether and when to trust AI input?

16, 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
COMMENTS:       EDM 2020
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.

17, TITLE:      Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks
http://arxiv.org/abs/2002.06753
AUTHORS:        Micah Goldblum ; Steven Reich ; Liam Fowl ; Renkun Ni ; Valeriia Cherepanova ; Tom Goldstein
COMMENTS:       ICML 2020
HIGHLIGHT:      In doing so, we introduce and verify several hypotheses for why meta-learned models perform better.

18, TITLE:      MOPO: Model-based Offline Policy Optimization
http://arxiv.org/abs/2005.13239
AUTHORS:        Tianhe Yu ; Garrett Thomas ; Lantao Yu ; Stefano Ermon ; James Zou ; Sergey Levine ; Chelsea Finn ; Tengyu Ma
COMMENTS:       First two authors contributed equally. Last two authors advised equally
HIGHLIGHT:      In this paper, we observe that an existing model-based RL algorithm on its own already produces significant gains in the offline setting, as compared to model-free approaches, despite not being designed for this setting.

19, TITLE:      AvE: Assistance via Empowerment
http://arxiv.org/abs/2006.14796
AUTHORS:        Yuqing Du ; Stas Tiomkin ; Emre Kiciman ; Daniel Polani ; Pieter Abbeel ; Anca Dragan
COMMENTS:       Fix missing citation on page 4
HIGHLIGHT:      We propose a new paradigm for assistance by instead increasing the human's ability to control their environment, and formalize this approach by augmenting reinforcement learning with human empowerment.

20, TITLE:      WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
http://arxiv.org/abs/2006.04598
AUTHORS:        Hyeongju Kim ; Hyeonseung Lee ; Woo Hyun Kang ; Sung Jun Cheon ; Byoung Jin Choi ; Nam Soo Kim
COMMENTS:       8 pages, 4 figures, Second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020)
HIGHLIGHT:      In this paper, we propose a novel generative model called WaveNODE which exploits a continuous normalizing flow for speech synthesis.

21, TITLE:      About a certain NP complete problem
http://arxiv.org/abs/1905.06104
AUTHORS:        Stepan Margaryan
HIGHLIGHT:      In this article we introduce the concept of special decomposition of a set and the concept of special covering of a set under such a decomposition.

22, TITLE:      FP-Stereo: Hardware-Efficient Stereo Vision for Embedded Applications
http://arxiv.org/abs/2006.03250
AUTHORS:        Jieru Zhao ; Tingyuan Liang ; Liang Feng ; Wenchao Ding ; Sharad Sinha ; Wei Zhang ; Shaojie Shen
COMMENTS:       IEEE International Conference on Field Programmable Logic and Applications (FPL), 2020
HIGHLIGHT:      To reduce the design effort and achieve the right balance, we propose FP-Stereo for building high-performance stereo matching pipelines on FPGAs automatically.

23, TITLE:      Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation
http://arxiv.org/abs/2002.09479
AUTHORS:        Cong Wang ; Witold Pedrycz ; ZhiWu Li ; MengChu Zhou
COMMENTS:       This paper has been withdrawn by the author due to a crucial definition error of objective function
HIGHLIGHT:      Considering these feature sets as data of clustering, an modified FCM algorithm is proposed, which introduces a KL divergence term in the partition matrix into its objective function.

24, TITLE:      Characterizing Sociolinguistic Variation in the Competing Vaccination Communities
http://arxiv.org/abs/2006.04334
AUTHORS:        Shahan Ali Memon ; Aman Tyagi ; David R. Mortensen ; Kathleen M. Carley
COMMENTS:       11 pages, 4 tables, 1 figure, 1 algorithm, accepted to SBP-BRiMS 2020 -- International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction and Behavior Representation in Modeling and Simulation
HIGHLIGHT:      Hence, in this paper, we conduct a sociolinguistic analysis of the two competing vaccination communities on Twitter: "pro-vaxxers" or individuals who believe in the effectiveness of vaccinations, and "anti-vaxxers" or individuals who are opposed to vaccinations.

25, TITLE:      Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing
http://arxiv.org/abs/2002.07033
AUTHORS:        Youngduck Choi ; Youngnam Lee ; Junghyun Cho ; Jineon Baek ; Byungsoo Kim ; Yeongmin Cha ; Dongmin Shin ; Chan Bae ; Jaewe Heo
COMMENTS:       L@S 2020
HIGHLIGHT:      In this paper, we propose a novel Transformer based model for knowledge tracing, SAINT: Separated Self-AttentIve Neural Knowledge Tracing.

26, TITLE:      Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping
http://arxiv.org/abs/2006.11708
AUTHORS:        Vasileios Lioutas
COMMENTS:       Paper completed in April 2019
HIGHLIGHT:      Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem.

27, TITLE:      A Fast and Robust Place Recognition Approach for Stereo Visual Odometry using LiDAR Descriptors
http://arxiv.org/abs/1909.07267
AUTHORS:        Jiawei Mo ; Junaed Sattar
COMMENTS:       Accepted by IROS2020
HIGHLIGHT:      In this paper, we propose an alternative approach that adapts LiDAR descriptors on 3D points obtained from stereo-visual odometry for place recognition.

28, TITLE:      Natural Backdoor Attack on Text Data
http://arxiv.org/abs/2006.16176
AUTHORS:        Lichao Sun
COMMENTS:       The paper contains many issues yet. We will update the formal version once all issues are fixed
HIGHLIGHT:      In this paper, we systematically study the backdoor attack against models on text data.

29, TITLE:      Streaming automatic speech recognition with the transformer model
http://arxiv.org/abs/2001.02674
AUTHORS:        Niko Moritz ; Takaaki Hori ; Jonathan Le Roux
HIGHLIGHT:      In this work, we propose a transformer based end-to-end ASR system for streaming ASR, where an output must be generated shortly after each spoken word.

30, TITLE:      Ranger: Boosting Error Resilience of Deep Neural Networks through Range Restriction
http://arxiv.org/abs/2003.13874
AUTHORS:        Zitao Chen ; Guanpeng Li ; Karthik Pattabiraman
COMMENTS:       12 pages, 12 figures
HIGHLIGHT:      In this work, we exploit the inherent resilience of DNNs to protect the DNNs from critical faults.

31, TITLE:      The computerization of archaeology: survey on AI techniques
http://arxiv.org/abs/2005.02863
AUTHORS:        Lorenzo Mantovan ; Loris Nanni
HIGHLIGHT:      The computerization of archaeology: survey on AI techniques

32, TITLE:      Deep Mask For X-ray Based Heart Disease Classification
http://arxiv.org/abs/1808.08277
AUTHORS:        Xupeng Chen ; Binbin Shi
COMMENTS:       outdated work
HIGHLIGHT:      We build a deep learning model to detect and classify heart disease using $X-ray$. We collect data from several hospitals and public datasets.

33, TITLE:      EdNet: A Large-Scale Hierarchical Dataset in Education
http://arxiv.org/abs/1912.03072
AUTHORS:        Youngduck Choi ; Youngnam Lee ; Dongmin Shin ; Junghyun Cho ; Seoyon Park ; Seewoo Lee ; Jineon Baek ; Chan Bae ; Byungsoo Kim ; Jaewe Heo
COMMENTS:       AIED 2020
HIGHLIGHT:      To this end, we introduce EdNet, a large-scale hierarchical dataset of diverse student activities collected by Santa, a multi-platform self-study solution equipped with artificial intelligence tutoring system.

34, TITLE:      Exploring Exploration: Comparing Children with RL Agents in Unified Environments
http://arxiv.org/abs/2005.02880
AUTHORS:        Eliza Kosoy ; Jasmine Collins ; David M. Chan ; Sandy Huang ; Deepak Pathak ; Pulkit Agrawal ; John Canny ; Alison Gopnik ; Jessica B. Hamrick
COMMENTS:       Published as a workshop paper at "Bridging AI and Cognitive Science" (ICLR 2020)
HIGHLIGHT:      In this work we propose using DeepMind Lab (Beattie et al., 2016) as a platform to directly compare child and agent behaviors and to develop new exploration techniques.

35, TITLE:      Similarity Learning Networks for Animal Individual Re-Identification -- Beyond the Capabilities of a Human Observer
http://arxiv.org/abs/1902.09324
AUTHORS:        Stefan Schneider ; Graham W. Taylor ; Stefan Linquist ; Stefan C. Kremer
COMMENTS:       9 pages, 4 figures, 3 table. WACV 2020 - Deep Learning for Animal Re-ID Workshop
HIGHLIGHT:      The ability for researchers to re-identify an animal individual upon re-encounter is fundamental for addressing a broad range of questions in the study of population dynamics and community/behavioural ecology.

36, TITLE:      Deep Attentive Study Session Dropout Prediction in Mobile Learning Environment
http://arxiv.org/abs/2002.11624
AUTHORS:        Youngnam Lee ; Dongmin Shin ; HyunBin Loh ; Jaemin Lee ; Piljae Chae ; Junghyun Cho ; Seoyon Park ; Jinhwan Lee ; Jineon Baek ; Byungsoo Kim ; Youngduck Choi
COMMENTS:       CSEDU 2020
HIGHLIGHT:      In this paper, we investigate the study session dropout prediction problem in a mobile learning environment.

37, TITLE:      Intrinsic Autoencoders for Joint Neural Rendering and Intrinsic Image Decomposition
http://arxiv.org/abs/2006.16011
AUTHORS:        Hassan Abu Alhaija ; Siva Karthik Mustikovela ; Justus Thies ; Varun Jampani ; Matthias Nießner ; Andreas Geiger ; Carsten Rother
HIGHLIGHT:      The main contribution of this work is to lift this restriction by training a neural rendering algorithm from unpaired data.

38, TITLE:      Computing strong regular characteristic pairs with Groebner bases
http://arxiv.org/abs/1907.13537
AUTHORS:        Rina Dong ; Dongming Wang
COMMENTS:       18 pages
HIGHLIGHT:      In this paper, we show that for any polynomial ideal I with given generators one can either detect that I is unit, or construct a strong regular characteristic pair (G,C) by computing Groebner bases such that I$\subseteq$sat(C)=<G> and sat(C) divides I, so the ideal I can be split into the saturated ideal sat(C) and the quotient ideal I:sat(C).

39, TITLE:      End-To-End Speech Synthesis Applied to Brazilian Portuguese
http://arxiv.org/abs/2005.05144
AUTHORS:        Edresson Casanova ; Arnaldo Candido Junior ; Christopher Shulby ; Frederico Santos de Oliveira ; João Paulo Teixeira ; Moacir Antonelli Ponti ; Sandra Maria Aluisio
COMMENTS:       This paper is under consideration at COLING'2020 - The 28th International Conference on Computational Linguistics
HIGHLIGHT:      In the proposed scenario, a model based on Mozilla TTS and RTISI-LA vocoder presented the best performance, achieving a 4.03 MOS value.

40, TITLE:      Planning to Explore via Self-Supervised World Models
http://arxiv.org/abs/2005.05960
AUTHORS:        Ramanan Sekar ; Oleh Rybkin ; Kostas Daniilidis ; Pieter Abbeel ; Danijar Hafner ; Deepak Pathak
COMMENTS:       Accepted at ICML 2020. Videos and code at https://ramanans1.github.io/plan2explore/
HIGHLIGHT:      We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast adaptation to new tasks, which need not be known during exploration.

41, TITLE:      GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
http://arxiv.org/abs/2006.06438
AUTHORS:        Nasir Ahmad ; Marcel A. J. van Gerven ; Luca Ambrogioni
COMMENTS:       12 pages, 4 figures
HIGHLIGHT:      Traditional backpropagation of error, though a highly successful algorithm for learning in artificial neural network models, includes features which are biologically implausible for learning in real neural circuits.

42, TITLE:      How useful is Active Learning for Image-based Plant Phenotyping?
http://arxiv.org/abs/2006.04255
AUTHORS:        Koushik Nagasubramanian ; Talukder Z. Jubery ; Fateme Fotouhi Ardakani ; Seyed Vahid Mirnezami ; Asheesh K. Singh ; Arti Singh ; Soumik Sarkar ; Baskar Ganapathysubramanian
HIGHLIGHT:      To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance.

43, TITLE:      Conditional Set Generation with Transformers
http://arxiv.org/abs/2006.16841
AUTHORS:        Adam R Kosiorek ; Hyunjik Kim ; Danilo J Rezende
COMMENTS:       6 pages, 6 figures, ICML 2020 Workshop on Object-Oriented Learning
HIGHLIGHT:      We introduce the Transformer Set Prediction Network (TSPN), a flexible permutation-equivariant model for set prediction based on the transformer, that builds upon and outperforms DSPN in the quality of predicted set elements and in the accuracy of their predicted sizes.

44, TITLE:      Data-Driven Continuum Dynamics via Transport-Teleport Duality
http://arxiv.org/abs/2005.13358
AUTHORS:        Jong-Hoon Ahn
COMMENTS:       11 pages, 10 figures (Added references, Added figures, Reorganized sections)
HIGHLIGHT:      In this study, we introduce a clever mathematical transform to represent the classical dynamics as a point-wise process of disappearance and reappearance of a quantity, which dramatically reduces model complexity and training data for machine learning of transport phenomena.

45, TITLE:      Overcoming Statistical Shortcuts for Open-ended Visual Counting
http://arxiv.org/abs/2006.10079
AUTHORS:        Corentin Dancette ; Remi Cadene ; Xinlei Chen ; Matthieu Cord
COMMENTS:       17 pages, 8 figures
HIGHLIGHT:      We aim to develop models that learn a proper mechanism of counting regardless of the output label.

46, TITLE:      Improved algorithm for permutation testing
http://arxiv.org/abs/2006.08473
AUTHORS:        Xiaojin Zhang
HIGHLIGHT:      We study the problem of testing forbidden patterns.

47, TITLE:      RECAST: Interactive Auditing of Automatic Toxicity Detection Models
http://arxiv.org/abs/2001.01819
AUTHORS:        Austin P. Wright ; Omar Shaikh ; Haekyu Park ; Will Epperson ; Muhammed Ahmed ; Stephane Pinel ; Diyi Yang ; Duen Horng Chau
COMMENTS:       8 Pages, 3 figures, The eighth International Workshop of Chinese CHI Proceedings
HIGHLIGHT:      We present our ongoing work, RECAST, an interactive tool for examining toxicity detection models by visualizing explanations for predictions and providing alternative wordings for detected toxic speech.

48, TITLE:      Multistage Vertex Cover
http://arxiv.org/abs/1906.00659
AUTHORS:        Till Fluschnik ; Rolf Niedermeier ; Valentin Rohm ; Philipp Zschoche
COMMENTS:       An extended abstract of this paper appeared in Proc. of IPEC'19
HIGHLIGHT:      We show that, different from classic Vertex Cover and some other dynamic or temporal variants of it, Multistage Vertex Cover is computationally hard even in fairly restricted settings.

49, TITLE:      UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
http://arxiv.org/abs/2003.10608
AUTHORS:        Shangbang Long ; Cong Yao
COMMENTS:       updated citation and comparison with a previous work
HIGHLIGHT:      In this paper, we introduce UnrealText, an efficient image synthesis method that renders realistic images via a 3D graphics engine.

50, TITLE:      GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
http://arxiv.org/abs/1911.05351
AUTHORS:        João C. Neves ; Ruben Tolosana ; Ruben Vera-Rodriguez ; Vasco Lopes ; Hugo Proença ; Julian Fierrez
HIGHLIGHT:      In this study, we focus on the synthesis of entire facial images, which is a specific type of facial manipulation.

51, TITLE:      Conceptual Content in Deep Convolutional Neural Networks: An analysis into multi-faceted properties of neurons
http://arxiv.org/abs/1811.00161
AUTHORS:        Zahra Sadeghi
COMMENTS:       12 pages, 6 figures, 1 table
HIGHLIGHT:      In this paper, convolutional layers of pre-trained VGG16 model are analyzed.

52, TITLE:      Making DensePose fast and light
http://arxiv.org/abs/2006.15190
AUTHORS:        Ruslan Rakhimov ; Emil Bogomolov ; Alexandr Notchenko ; Fung Mao ; Alexey Artemov ; Denis Zorin ; Evgeny Burnaev
HIGHLIGHT:      In this work, we target the problem of redesigning the DensePose R-CNN model's architecture so that the final network retains most of its accuracy but becomes more light-weight and fast.

53, TITLE:      Liquid Information Flow Control
http://arxiv.org/abs/1607.03445
AUTHORS:        Nadia Polikarpova ; Deian Stefan ; Jean Yang ; Shachar Itzhaky ; Travis Hance ; Armando Solar-Lezama
HIGHLIGHT:      We present Lifty, a domain-specific language for data-centric applications that manipulate sensitive data.

54, TITLE:      G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
http://arxiv.org/abs/2006.11510
AUTHORS:        Cong Wang ; Witold Pedrycz ; ZhiWu Li ; MengChu Zhou ; Shuzhi Sam Ge
COMMENTS:       This paper has been withdrawn by the author since some statements are not right as raised by other researchers
HIGHLIGHT:      This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images.

55, TITLE:      Revisit Knowledge Distillation: a Teacher-free Framework
http://arxiv.org/abs/1909.11723
AUTHORS:        Li Yuan ; Francis E. H. Tay ; Guilin Li ; Tao Wang ; Jiashi Feng
COMMENTS:       CVPR2020: Revisiting Knowledge Distillation via Label Smoothing Regularization
HIGHLIGHT:      In this work, we challenge this common belief by following experimental observations: 1) beyond the acknowledgment that the teacher can improve the student, the student can also enhance the teacher significantly by reversing the KD procedure; 2) a poorly-trained teacher with much lower accuracy than the student can still improve the latter significantly.

56, TITLE:      A Simple Framework for Contrastive Learning of Visual Representations
http://arxiv.org/abs/2002.05709
AUTHORS:        Ting Chen ; Simon Kornblith ; Mohammad Norouzi ; Geoffrey Hinton
COMMENTS:       ICML'2020. Code and pretrained models at https://github.com/google-research/simclr
HIGHLIGHT:      This paper presents SimCLR: a simple framework for contrastive learning of visual representations.

57, TITLE:      Text Detection and Recognition in the Wild: A Review
http://arxiv.org/abs/2006.04305
AUTHORS:        Zobeir Raisi ; Mohamed A. Naiel ; Paul Fieguth ; Steven Wardell ; John Zelek
HIGHLIGHT:      Thus, unlike previous surveys in this field, the objectives of this survey are as follows: first, offering the reader not only a review on the recent advancement in scene text detection and recognition, but also presenting the results of conducting extensive experiments using a unified evaluation framework that assesses pre-trained models of the selected methods on challenging cases, and applies the same evaluation criteria on these techniques.

58, TITLE:      Assessment Modeling: Fundamental Pre-training Tasks for Interactive Educational Systems
http://arxiv.org/abs/2002.05505
AUTHORS:        Youngduck Choi ; Youngnam Lee ; Junghyun Cho ; Jineon Baek ; Dongmin Shin ; Seewoo Lee ; Jonghun Shin ; Chan Bae ; Byungsoo Kim ; Jaewe Heo
HIGHLIGHT:      To this end, we propose assessment modeling, fundamental pre-training tasks for IESs.

59, TITLE:      Imitation Learning of Factored Multi-agent Reactive Models
http://arxiv.org/abs/1903.04714
AUTHORS:        Michael Teng ; Tuan Anh Le ; Adam Scibior ; Frank Wood
COMMENTS:       incorporated into another paper with different motivations
HIGHLIGHT:      We apply recent advances in deep generative modeling to the task of imitation learning from biological agents.

60, TITLE:      Targeted sampling from massive block model graphs with personalized PageRank
http://arxiv.org/abs/1910.12937
AUTHORS:        Fan Chen ; Yini Zhang ; Karl Rohe
COMMENTS:       61 pages, 5 figures
HIGHLIGHT:      The paper provides statistical theory and intuition for personalized PageRank (called "PPR"): a popular technique that samples a small community from a massive network.

61, TITLE:      Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction
http://arxiv.org/abs/2002.09625
AUTHORS:        Jiangpeng Yan ; Shuo Chen ; Xiu Li ; Yongbing Zhang
COMMENTS:       10 pages, submitted to Computerized Medical Imaging and Graphics, Major Revision
HIGHLIGHT:      In this manuscript, we proposed a novel and efficient MR image reconstruction framework by Neural Architecture Search (NAS) algorithm.

62, TITLE:      Predicting In-game Actions from Interviews of NBA Players
http://arxiv.org/abs/1910.11292
AUTHORS:        Nadav Oved ; Amir Feder ; Roi Reichart
COMMENTS:       First two authors contributed equally. To be published in the Computational Linguistics journal. Code is available at: https://github.com/nadavo/mood
HIGHLIGHT:      Sports competitions are widely researched in computer and social science, with the goal of understanding how players act under uncertainty. We collected a dataset of transcripts from key NBA players' pre-game interviews and their in-game performance metrics, totalling in 5,226 interview-metric pairs.

63, TITLE:      Learning Shared Filter Bases for Efficient ConvNets
http://arxiv.org/abs/2006.05066
AUTHORS:        Daeyeon Kim ; Woochul Kang
HIGHLIGHT:      In this paper, we propose to exploit the linear structure of convolution filters for effective and efficient sharing of parameters among iterative convolution layers.

64, TITLE:      A survey of loss functions for semantic segmentation
http://arxiv.org/abs/2006.14822
AUTHORS:        Shruti Jadon
COMMENTS:       5 pages, 5 figures, 2 tables
HIGHLIGHT:      In this paper, we have summarized most of the well-known loss functions widely used in Image segmentation and listed out the cases where their usage can help in fast and better convergence of a Model.