2020.04.20.txt

==========New Papers==========
1, TITLE:       Generative Adversarial Networks for Video-to-Video Domain Adaptation
http://arxiv.org/abs/2004.08058
AUTHORS:        Jiawei Chen ; Yuexiang Li ; Kai Ma ; Yefeng Zheng
COMMENTS:       Accepted by AAAI 2020
HIGHLIGHT:      In this work, we propose a novel generative adversarial network (GAN), namely VideoGAN, to transfer the video-based data across different domains.

2, TITLE:       Enriching the Transformer with Linguistic and Semantic Factors for Low-Resource Machine Translation
http://arxiv.org/abs/2004.08053
AUTHORS:        Jordi Armengol-Estapé ; Marta R. Costa-jussà ; Carlos Escolano
HIGHLIGHT:      This study proposes enhancing the current state-of-the-art neural machine translation architecture, the Transformer, so that it allows to introduce external knowledge.

3, TITLE:       A New Modified Deep Convolutional Neural Network for Detecting COVID-19 from X-ray Images
http://arxiv.org/abs/2004.08052
AUTHORS:        Mohammad Rahimzadeh ; Abolfazl Attar
COMMENTS:       The Code of this paper is available on : https://github.com/mr7495/covid19
HIGHLIGHT:      In this paper, we have trained several deep convolutional networks with the introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19, based on two open-source datasets.

4, TITLE:       Approximate Inverse Reinforcement Learning from Vision-based Imitation Learning
http://arxiv.org/abs/2004.08051
AUTHORS:        Keuntaek Lee ; Bogdan Vlahov ; Jason Gibson ; James M. Rehg ; Evangelos A. Theodorou
HIGHLIGHT:      In this work, we present a method for obtaining an implicit objective function for vision-based navigation.

5, TITLE:       Diversity-based Design Assist for Large Legged Robots
http://arxiv.org/abs/2004.08057
AUTHORS:        David Howard ; Thomas Lowe ; Wade Geles
COMMENTS:       Expanded version of poster for GECCO 2020
HIGHLIGHT:      We combine MAP-Elites and highly parallelisable simulation to explore the design space of a class of large legged robots, which stand at around 2m tall and whose design and construction is not well-studied.

6, TITLE:       Dialogue-Based Relation Extraction
http://arxiv.org/abs/2004.08056
AUTHORS:        Dian Yu ; Kai Sun ; Claire Cardie ; Dong Yu
COMMENTS:       To appear in ACL 2020
HIGHLIGHT:      We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.

7, TITLE:       IDDA: a large-scale multi-domain dataset for autonomous driving
http://arxiv.org/abs/2004.08298
AUTHORS:        Emanuele Alberti ; Antonio Tavera ; Carlo Masone ; Barbara Caputo
COMMENTS:       Under review
HIGHLIGHT:      To support work in this direction, this paper contributes a new large scale, synthetic dataset for semantic segmentation with more than 100 different source visual domains.

8, TITLE:       Self-Learning with Rectification Strategy for Human Parsing
http://arxiv.org/abs/2004.08055
AUTHORS:        Tao Li ; Zhiyuan Liang ; Sanyuan Zhao ; Jiahao Gong ; Jianbing Shen
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we solve the sample shortage problem in the human parsing task.

9, TITLE:       Active Sentence Learning by Adversarial Uncertainty Sampling in Discrete Space
http://arxiv.org/abs/2004.08046
AUTHORS:        Dongyu Ru ; Yating Luo ; Lin Qiu ; Hao Zhou ; Lei Li ; Weinan Zhang ; Yong Yu
COMMENTS:       10 pages, 3 figures, 4 tables
HIGHLIGHT:      In this paper, we focus on reducing the labeled data size for sentence learning.

10, TITLE:      Transform and Tell: Entity-Aware News Image Captioning
http://arxiv.org/abs/2004.08070
AUTHORS:        Alasdair Tran ; Alexander Mathews ; Lexing Xie
COMMENTS:       To be published in CVPR 2020. Code is available at https://github.com/alasdairtran/transform-and-tell and demo is available at https://transform-and-tell.ml
HIGHLIGHT:      We propose an end-to-end model which generates captions for images embedded in news articles. We also introduce the NYTimes800k dataset which is 70% larger than GoodNews, has higher article quality, and includes the locations of images within articles as an additional contextual cue.

11, TITLE:      Adaptive Neuron-wise Discriminant Criterion and Adaptive Center Loss at Hidden Layer for Deep Convolutional Neural Network
http://arxiv.org/abs/2004.08074
AUTHORS:        Motoshi Abe ; Junichi Miyao ; Takio Kurita
COMMENTS:       Accepted to IJCNN 2020
HIGHLIGHT:      In this paper, we propose to use the neuron-wise discriminant criterion at the output layer and the center-loss at the hidden layer.

12, TITLE:      Image Processing Based Scene-Text Detection and Recognition with Tesseract
http://arxiv.org/abs/2004.08079
AUTHORS:        Ebin Zacharias ; Martin Teuchler ; Bénédicte Bernier
COMMENTS:       6 pages, 4 figures
HIGHLIGHT:      This paper outlines the stages of development, the major challenges and some of the interesting findings of the project.

13, TITLE:      Neural Approaches for Data Driven Dependency Parsing in Sanskrit
http://arxiv.org/abs/2004.08076
AUTHORS:        Amrith Krishna ; Ashim Gupta ; Deepak Garasangi ; Jivnesh Sandhan ; Pavankumar Satuluri ; Pawan Goyal
COMMENTS:       submitted to WSC 2021
HIGHLIGHT:      In this work, we analyse the performance of the parsers using both an in-domain and an out-of-domain test dataset.

14, TITLE:      One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
http://arxiv.org/abs/2004.08067
AUTHORS:        Jaeyeon Jang ; Chang Ouk Kim
COMMENTS:       16 pages, 11 figures
HIGHLIGHT:      In this paper, we propose a DNN structure in which multiple one-vs-rest sigmoid networks follow a convolutional neural network feature extractor.

15, TITLE:      YuruGAN: Yuru-Chara Mascot Generator Using Generative Adversarial Networks With Clustering Small Dataset
http://arxiv.org/abs/2004.08066
AUTHORS:        Yuki Hagiwara ; Toshihisa Tanaka
COMMENTS:       conference
HIGHLIGHT:      In this paper, we propose a class conditional GAN based on clustering and data augmentation.

16, TITLE:      Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning
http://arxiv.org/abs/2004.08097
AUTHORS:        Joongbo Shin ; Yoonhyung Lee ; Seunghyun Yoon ; Kyomin Jung
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      To resolve the limitation, we propose a novel deep bidirectional language model called Transformer-based Text Autoencoder (T-TA).

17, TITLE:      Fast Soft Color Segmentation
http://arxiv.org/abs/2004.08096
AUTHORS:        Naofumi Akimoto ; Huachun Zhu ; Yanghua Jin ; Yoshimitsu Aoki
COMMENTS:       Accepted at CVPR 2020
HIGHLIGHT:      To address this issue, we propose a neural network based method for this task that decomposes a given image into multiple layers in a single forward pass.

18, TITLE:      Meta-Meta-Classification for One-Shot Learning
http://arxiv.org/abs/2004.08083
AUTHORS:        Arkabandhu Chowdhury ; Dipak Chaudhari ; Swarat Chaudhuri ; Chris Jermaine
COMMENTS:       8 pages without references, 2 figures
HIGHLIGHT:      We present a new approach, called meta-meta-classification, to learning in small-data settings.

19, TITLE:      Women worry about family, men about the economy: Gender differences in emotional responses to COVID-19
http://arxiv.org/abs/2004.08202
AUTHORS:        Isabelle van der Vegt ; Bennett Kleinberg
COMMENTS:       pre-print
HIGHLIGHT:      We examine gender differences and the effect of document length on worries about the ongoing COVID-19 situation.

20, TITLE:      Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
http://arxiv.org/abs/2004.08206
AUTHORS:        Friedrich Kruber ; Eduardo Sánchez Morales ; Samarjit Chakraborty ; Michael Botsch
COMMENTS:       Copyright 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      This work describes a process to estimate a precise vehicle position from aerial imagery.

21, TITLE:      Defining and Verifying Durable Opacity: Correctness for Persistent Software Transactional Memory
http://arxiv.org/abs/2004.08200
AUTHORS:        Eleni Bila ; Simon Doherty ; Brijesh Dongol ; John Derrick ; Gerhard Schellhorn ; Heike Wehrheim
COMMENTS:       This is the full version of the paper that is to appear in FORTE 2020 (https://www.discotec.org/2020/forte)
HIGHLIGHT:      In this paper, we transfer the principle of durable concurrent correctness to the area of software transactional memory (STM).

22, TITLE:      Learning to Predict Context-adaptive Convolution for Semantic Segmentation
http://arxiv.org/abs/2004.08222
AUTHORS:        Jianbo Liu ; Junjun He ; Jimmy S. Ren ; Yu Qiao ; Hongsheng Li
HIGHLIGHT:      In this paper, we propose a Context-adaptive Convolution Network (CaC-Net) to predict a spatially-varying feature weighting vector for each spatial location of the semantic feature maps.

23, TITLE:      How recurrent networks implement contextual processing in sentiment analysis
http://arxiv.org/abs/2004.08013
AUTHORS:        Niru Maheswaranathan ; David Sussillo
HIGHLIGHT:      Here, we propose general methods for reverse engineering recurrent neural networks (RNNs) to identify and elucidate contextual processing.

24, TITLE:      DepthNet Nano: A Highly Compact Self-Normalizing Neural Network for Monocular Depth Estimation
http://arxiv.org/abs/2004.08008
AUTHORS:        Linda Wang ; Mahmoud Famouri ; Alexander Wong
COMMENTS:       9 pages
HIGHLIGHT:      In this study, we introduce DepthNet Nano, a highly compact self normalizing network for monocular depth estimation designed using a human machine collaborative design strategy, where principled network design prototyping based on encoder-decoder design principles are coupled with machine-driven design exploration.

25, TITLE:      Understanding the Difficulty of Training Transformers
http://arxiv.org/abs/2004.08249
AUTHORS:        Liyuan Liu ; Xiaodong Liu ; Jianfeng Gao ; Weizhu Chen ; Jiawei Han
HIGHLIGHT:      In this paper, we study Transformer training from both theoretical and empirical perspectives.

26, TITLE:      Automating Cutting Planes is NP-Hard}
http://arxiv.org/abs/2004.08037
AUTHORS:        Mika Göös ; Sajin Koroth ; Ian Mertz ; Toniann Pitassi
COMMENTS:       Full version of the conference version at STOC 2020 by the same title
HIGHLIGHT:      We show that Cutting Planes (CP) proofs are hard to find: Given an unsatisfiable formula $F$, 1) It is NP-hard to find a CP refutation of $F$ in time polynomial in the length of the shortest such refutation; and 2)unless Gap-Hitting-Set admits a nontrivial algorithm, one cannot find a tree-like CP refutation of $F$ in time polynomial in the length of the shortest such refutation.

27, TITLE:      AlloVera: A Multilingual Allophone Database
http://arxiv.org/abs/2004.08031
AUTHORS:        David R. Mortensen ; Xinjian Li ; Patrick Littell ; Alexis Michaud ; Shruti Rijhwani ; Antonios Anastasopoulos ; Alan W. Black ; Florian Metze ; Graham Neubig
COMMENTS:       8 pages, LREC 2020
HIGHLIGHT:      We introduce a new resource, AlloVera, which provides mappings from 218 allophones to phonemes for 14 languages.

28, TITLE:      Smartphone camera based pointer
http://arxiv.org/abs/2004.08030
AUTHORS:        Predrag Lazic
COMMENTS:       13 pages, 8 figures
HIGHLIGHT:      In this paper, we present a novel idea that turns a smartphone into a direct virtual pointer on the screen using the phone's camera.

29, TITLE:      Weakly Supervised Geodesic Segmentation of Egyptian Mummy CT Scans
http://arxiv.org/abs/2004.08270
AUTHORS:        Avik Hati ; Matteo Bustreo ; Diego Sona ; Vittorio Murino ; Alessio Del Bue
HIGHLIGHT:      In this paper, we tackle the task of automatically analyzing 3D volumetric scans obtained from computed tomography (CT) devices.

30, TITLE:      CPARR: Category-based Proposal Analysis for Referring Relationships
http://arxiv.org/abs/2004.08028
AUTHORS:        Chuanzi He ; Haidong Zhu ; Jiyang Gao ; Kan Chen ; Ram Nevatia
COMMENTS:       CVPR 2020 Workshop on Multimodal Learning
HIGHLIGHT:      We introduce a simple yet effective proposal-based method for referring relationships.

31, TITLE:      Conservative Plane Releasing for Spatial Privacy Protection in Mixed Reality
http://arxiv.org/abs/2004.08029
AUTHORS:        Jaybie A. de Guzman ; Kanchana Thilakarathna ; Aruna Seneviratne
COMMENTS:       15 pages
HIGHLIGHT:      In this work, we propose to leverage spatial generalizations coupled with conservative releasing to provide spatial privacy while maintaining data utility.

32, TITLE:      Rigid Formats Controlled Text Generation
http://arxiv.org/abs/2004.08022
AUTHORS:        Piji Li ; Haisong Zhang ; Xiaojiang Liu ; Shuming Shi
COMMENTS:       ACL2020, 10 pages
HIGHLIGHT:      Therefore, we propose a simple and elegant framework named SongNet to tackle this problem.

33, TITLE:      Symmetry as an Organizing Principle for Geometric Intelligence
http://arxiv.org/abs/2004.07879
AUTHORS:        Snejana Sheghava ; Ashok Goel
COMMENTS:       11 pages, 12 figures
HIGHLIGHT:      We present a computational technique for building artificial intelligence (AI) agents that use symmetry as the organizing principle for addressing Dehaene's test of geometric intelligence \cite{dehaene2006core}.

34, TITLE:      Bridging Anaphora Resolution as Question Answering
http://arxiv.org/abs/2004.07898
AUTHORS:        Yufang Hou
COMMENTS:       accepted at ACL2020
HIGHLIGHT:      In this paper, we cast bridging anaphora resolution as question answering based on context.

35, TITLE:      Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
http://arxiv.org/abs/2004.08170
AUTHORS:        Javier Del Ser ; Ibai Lana ; Eric L. Manibardo ; Izaskun Oregi ; Eneko Osaba ; Jesus L. Lobo ; Miren Nekane Bilbao ; Eleni I. Vlahogianni
COMMENTS:       6 pages, 4 figures
HIGHLIGHT:      In this work we elaborate on the performance of Deep Echo State Networks for this particular task.

36, TITLE:      Highway Transformer: Self-Gating Enhanced Self-Attentive Networks
http://arxiv.org/abs/2004.08178
AUTHORS:        Yekun Chai ; Jin Shuo ; Xinwen Hou
HIGHLIGHT:      Through a pseudo information highway, we introduce a gated component self-dependency units (SDU) that incorporates LSTM-styled gating units to replenish internal semantic importance within the multi-dimensional latent space of individual representations.

37, TITLE:      Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness
http://arxiv.org/abs/2004.08166
AUTHORS:        Yavuz Selim Kartal ; Busra Guvenen ; Mucahid Kutlu
HIGHLIGHT:      In this paper, we propose a model prioritizing the claims based on their check-worthiness.

38, TITLE:      Structured Landmark Detection via Topology-Adapting Deep Graph Learning
http://arxiv.org/abs/2004.08190
AUTHORS:        Weijian Li ; Yuhang Lu ; Kang Zheng ; Haofu Liao ; Chihung Lin ; Jiebo Luo ; Chi-Tung Cheng ; Jing Xiao ; Le Lu ; Chang-Fu Kuo ; Shun Miao
HIGHLIGHT:      In this work, we present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical (e.g., hand, pelvis) landmark detection.

39, TITLE:      The FaceChannel: A Light-weight Deep Neural Network for Facial Expression Recognition
http://arxiv.org/abs/2004.08195
AUTHORS:        Pablo Barros ; Nikhil Churamani ; Alessandra Sciutti
COMMENTS:       Accepted at the Workshop on Affect Recognition in-the-wild: Uni/Multi-Modal Analysis & VA-AU-Expression Challenges, FG2020
HIGHLIGHT:      In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks.

40, TITLE:      An Annotated Dataset of Stack Overflow Post Edits
http://arxiv.org/abs/2004.08193
AUTHORS:        Sebastian Baltes ; Markus Wagner
HIGHLIGHT:      To be able to dig deeper and to extract insights at a higher resolution, we hereby present an annotated dataset that contains over 7 million edits of code and text on Stack Overflow.

41, TITLE:      MOPT: Multi-Object Panoptic Tracking
http://arxiv.org/abs/2004.08189
AUTHORS:        Juana Valeria Hurtado ; Rohit Mohan ; Abhinav Valada
COMMENTS:       Code & models are available at http://rl.uni-freiburg.de/research/panoptictracking
HIGHLIGHT:      As a step towards encouraging research in this direction, we introduce a new perception task that we name Multi-Object Panoptic Tracking (MOPT).

42, TITLE:      Natural Language Processing with Deep Learning for Medical Adverse Event Detection from Free-Text Medical Narratives: A Case Study of Detecting Total Hip Replacement Dislocation
http://arxiv.org/abs/2004.08333
AUTHORS:        Alireza Borjali ; Martin Magneli ; David Shin ; Henrik Malchau ; Orhun K. Muratoglu ; Kartik M. Varadarajan
HIGHLIGHT:      In this study we proposed deep learning based NLP (DL-NLP) models for efficient and accurate hip dislocation AE detection following total hip replacement from standard (radiology notes) and non-standard (follow-up telephone notes) free-text medical narratives.

43, TITLE:      Hitting forbidden induced subgraphs on bounded treewidth graphs
http://arxiv.org/abs/2004.08324
AUTHORS:        Ignasi Sau ; Uéverton S. Souza
COMMENTS:       24 pages, 3 figures
HIGHLIGHT:      We present a number of lower bounds by generalizing a reduction of Cygan et al. [MFCS 2014] for the subgraph version.

44, TITLE:      SpEx: Multi-Scale Time Domain Speaker Extraction Network
http://arxiv.org/abs/2004.08326
AUTHORS:        Chenglin Xu ; Wei Rao ; Eng Siong Chng ; Haizhou Li
COMMENTS:       ACCEPTED in IEEE/ACM Transactions on Audio, Speech, and Language Processing (TASLP)
HIGHLIGHT:      Inspired by Conv-TasNet, we propose a time-domain speaker extraction network (SpEx) that converts the mixture speech into multi-scale embedding coefficients instead of decomposing the speech signal into magnitude and phase spectra.

45, TITLE:      Triplet Loss for Knowledge Distillation
http://arxiv.org/abs/2004.08116
AUTHORS:        Hideki Oki ; Motoshi Abe ; Junichi Miyao ; Takio Kurita
COMMENTS:       Accepted to IJCNN 2020, Source code is at https://github.com/i13abe/Triplet-Loss-for-Knowledge-Distillation
HIGHLIGHT:      In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed.

46, TITLE:      Show Us the Way: Learning to Manage Dialog from Demonstrations
http://arxiv.org/abs/2004.08114
AUTHORS:        Gabriel Gordon-Hall ; Philip John Gorinski ; Gerasimos Lampouras ; Ignacio Iacobacci
COMMENTS:       8 pages + 2 pages references, 4 figures, 4 tables, accepted to DSTC8 Workshop at AAAI2020
HIGHLIGHT:      We present our submission to the End-to-End Multi-Domain Dialog Challenge Track of the Eighth Dialog System Technology Challenge.

47, TITLE:      Goal-conditioned Batch Reinforcement Learning for Rotation Invariant Locomotion
http://arxiv.org/abs/2004.08356
AUTHORS:        Aditi Mavalankar
COMMENTS:       Accepted to the BeTR-RL workshop at ICLR 2020. Link to code: https://github.com/aditimavalankar/gc-batch-rl-locomotion
HIGHLIGHT:      We propose a novel approach to learn goal-conditioned policies for locomotion in a batch RL setting.

48, TITLE:      Towards an Interoperable Ecosystem of AI and LT Platforms: A Roadmap for the Implementation of Different Levels of Interoperability
http://arxiv.org/abs/2004.08355
AUTHORS:        Georg Rehm ; Dimitrios Galanis ; Penny Labropoulou ; Stelios Piperidis ; Martin Welß ; Ricardo Usbeck ; Joachim Köhler ; Miltos Deligiannis ; Katerina Gkirtzou ; Johannes Fischer ; Christian Chiarcos ; Nils Feldhus ; Julián Moreno-Schneider ; Florian Kintzel ; Elena Montiel ; Víctor Rodríguez Doncel ; John P. McCrae ; David Laqua ; Irina Patricia Theile ; Christian Dittmar ; Kalina Bontcheva ; Ian Roberts ; Andrejs Vasiljevs ; Andis Lagzdiņš
COMMENTS:       Proceedings of the 1st International Workshop on Language Technology Platforms (IWLTP 2020). To appear
HIGHLIGHT:      We devise five different levels (of increasing complexity) of platform interoperability that we suggest to implement in a wider federation of AI/LT platforms.

49, TITLE:      Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle
http://arxiv.org/abs/2004.08118
AUTHORS:        Ebin Zacharias ; Didier Stricker ; Martin Teuchler ; Kripasindhu Sarkar
COMMENTS:       13 pages 9 figures 2 tables
HIGHLIGHT:      This project aims to jointly perform object detection of a swap-body and to find the type of swap-body by reading an ILU code using an efficient optical character recognition (OCR) method.

50, TITLE:      LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification
http://arxiv.org/abs/2004.08112
AUTHORS:        Rui Li ; Chenxi Duan
HIGHLIGHT:      In this paper, to reduce dependence on massive calculation and labelled samples, we propose a lightweight network architecture (LiteDenseNet) based on DenseNet for Hyperspectral Image Classification.

51, TITLE:      Motion and Region Aware Adversarial Learning for Fall Detection with Thermal Imaging
http://arxiv.org/abs/2004.08352
AUTHORS:        Vineet Mehta ; Abhinav Dhall ; Sujata Pal ; Shehroz Khan
COMMENTS:       9 pages,5 figures
HIGHLIGHT:      We introduce a differential constraint, a technique to track the region of interest and a joint discriminator to compute the reconstruction error.

52, TITLE:      An integrated light management system with real-time light measurement and human perception
http://arxiv.org/abs/2004.08346
AUTHORS:        Theodore Tsesmelis ; Irtiza Hasan ; Marco Cristani ; Alessio Del Bue ; Fabio Galasso
HIGHLIGHT:      Here we propose the first fully-automated light management system (LMS) which measures lighting in real-time, leveraging an RGBD sensor and a radiosity-based light propagation model.

53, TITLE:      Complexity Analysis of an Edge Preserving CNN SAR Despeckling Algorithm
http://arxiv.org/abs/2004.08345
AUTHORS:        Sergio Vitale ; Giampaolo Ferraioli ; Vito Pascazio
COMMENTS:       Accpeted to International Geoscience and Remote Sensing Symposium, IGARSS 2020
HIGHLIGHT:      In the last decades several methods for SAR denoising have been proposed and in the last years great attention has moved towards deep learning based solutions.

54, TITLE:      Cascaded Context Enhancement for Automated Skin Lesion Segmentation
http://arxiv.org/abs/2004.08107
AUTHORS:        Ruxin Wang ; Shuyuan Chen ; Jianping Fan ; Ye Li
HIGHLIGHT:      In this paper, we formulate a cascaded context enhancement neural network for skin lesion segmentation.

55, TITLE:      Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
http://arxiv.org/abs/2004.08340
AUTHORS:        Zifeng Guo ; Joao P. Leitao ; Nuno E. Simoes ; Vahid Moosavi
HIGHLIGHT:      To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process.

56, TITLE:      Exploring the Combination of Contextual Word Embeddings and Knowledge Graph Embeddings
http://arxiv.org/abs/2004.08371
AUTHORS:        Lea Dieudonat ; Kelvin Han ; Phyllicia Leavitt ; Esteban Marquer
COMMENTS:       pre-publication, 16 pages, 4 figures
HIGHLIGHT:      In this work, we begin exploring another approach using contextual and KB embeddings jointly at the same level and propose two tasks -- an entity typing and a relation typing task -- that evaluate the performance of contextual and KB embeddings.

57, TITLE:      Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
http://arxiv.org/abs/2004.08134
AUTHORS:        Christoph Alt ; Aleksandra Gabryszak ; Leonhard Hennig
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8.

58, TITLE:      On Regularity of Max-CSPs and Min-CSPs
http://arxiv.org/abs/2004.08374
AUTHORS:        Aleksa Stankovic
HIGHLIGHT:      We study approximability of regular constraint satisfaction problems, i.e., CSPs where each variable in an instance has the same number of occurrences.

59, TITLE:      Robotic Room Traversal using Optical Range Finding
http://arxiv.org/abs/2004.08368
AUTHORS:        Cole Smith ; Eric Lin ; Dennis Shasha
COMMENTS:       Technical Report TR2018-991
HIGHLIGHT:      Our findings suggest a method of inexpensive optical range finding for robotic room traversal.

60, TITLE:      DynamicEmbedding: Extending TensorFlow for Colossal-Scale Applications
http://arxiv.org/abs/2004.08366
AUTHORS:        Yun Zeng ; Siqi Zuo ; Dongcai Shen
HIGHLIGHT:      With this paper we propose both a theory and a working system design which remove this limitation, and show that the resulting models are able to perform better and efficiently run at a much larger scale.

61, TITLE:      Whence the Expected Free Energy?
http://arxiv.org/abs/2004.08128
AUTHORS:        Beren Millidge ; Alexander Tschantz ; Christopher L Buckley
COMMENTS:       24 pages, 0 figures
HIGHLIGHT:      In this paper, we investigate the origins of the EFE in detail and show that it is not simply "the free energy in the future".

62, TITLE:      Unsupervised Discovery of Implicit Gender Bias
http://arxiv.org/abs/2004.08361
AUTHORS:        Anjalie Field ; Yulia Tsvetkov
HIGHLIGHT:      Therefore, we take an unsupervised approach to identifying gender bias at a comment or sentence level, and present a model that can surface text likely to contain bias.

63, TITLE:      Batch Clustering for Multilingual News Streaming
http://arxiv.org/abs/2004.08123
AUTHORS:        Mathis Linger ; Mhamed Hajaiej
COMMENTS:       7 pages, 2 figures
HIGHLIGHT:      Here, we introduce a novel "replaying" strategy to link monolingual local topics into stories.

64, TITLE:      A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy
http://arxiv.org/abs/2004.08122
AUTHORS:        Laurens Beljaards ; Mohamed S. Elmahdy ; Fons Verbeek ; Marius Staring
COMMENTS:       Accepted to MIDL 2020
HIGHLIGHT:      In this work, we investigate joining these tasks at the architectural level.

65, TITLE:      Detailed 2D-3D Joint Representation for Human-Object Interaction
http://arxiv.org/abs/2004.08154
AUTHORS:        Yong-Lu Li ; Xinpeng Liu ; Han Lu ; Shiyi Wang ; Junqi Liu ; Jiefeng Li ; Cewu Lu
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available:https://github.com/DirtyHarryLYL/DJ-RN
HIGHLIGHT:      In light of these, we propose a detailed 2D-3D joint representation learning method. To better evaluate the 2D ambiguity processing capacity of models, we propose a new benchmark named Ambiguous-HOI consisting of hard ambiguous images.

66, TITLE:      Modeling Extent-of-Texture Information for Ground Terrain Recognition
http://arxiv.org/abs/2004.08141
AUTHORS:        Shuvozit Ghose ; Pinaki Nath Chowdhury ; Partha Pratim Roy ; Umapada Pal
HIGHLIGHT:      In this paper, we propose a novel approach towards ground-terrain recognition via modeling the Extent-of-Texture information to establish a balance between the order-less texture component and ordered-spatial information locally.

67, TITLE:      GEVO: GPU Code Optimization using EvolutionaryComputation
http://arxiv.org/abs/2004.08140
AUTHORS:        Jhe-Yu Liou ; Xiaodong Wang ; Stephanie Forrest ; Carole-Jean Wu
HIGHLIGHT:      We demonstrate that GEVO improves the execution time of the GPU programs in the Rodinia benchmark suite and the machine learning models, SVM and ResNet18, on NVIDIA Tesla P100.

68, TITLE:      Intention as Commitment toward Time
http://arxiv.org/abs/2004.08144
AUTHORS:        Marc van Zee ; Dragan Doder ; Leendert van der Torre ; Mehdi Dastani ; Thomas Icard ; Eric Pacuit
COMMENTS:       83 pages, 4 figures, Artificial Intelligence journal pre-print
HIGHLIGHT:      In this paper we address the interplay among intention, time, and belief in dynamic environments.

69, TITLE:      A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
http://arxiv.org/abs/2004.07995
AUTHORS:        Ruizhe Li ; Dorothee Auer ; Christian Wagner ; Xin Chen
COMMENTS:       Accepted for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 2020
HIGHLIGHT:      To address this problem, we propose a generic semi-supervised learning framework for image segmentation based on a deep convolutional neural network (DCNN).

70, TITLE:      ViBE: A Tool for Measuring and Mitigating Bias in Image Datasets
http://arxiv.org/abs/2004.07999
AUTHORS:        Angelina Wang ; Arvind Narayanan ; Olga Russakovsky
COMMENTS:       Tool available at: https://github.com/princetonvisualai/vibe-tool
HIGHLIGHT:      We present the Visual Bias Extraction (ViBE) Tool that assists in the investigation of a visual dataset, surfacing potential dataset biases along three dimensions: (1) object-based, (2) gender-based, and (3) geography-based.

71, TITLE:      Light-Weighted CNN for Text Classification
http://arxiv.org/abs/2004.07922
AUTHORS:        Ritu Yadav
HIGHLIGHT:      As a solution to this problem, we introduced a whole new architecture based on separable convolution.

72, TITLE:      Divergent Search for Few-Shot Image Classification
http://arxiv.org/abs/2004.07903
AUTHORS:        Jeremy Tan ; Bernhard Kainz
COMMENTS:       Submitted to GECCO2020 for review
HIGHLIGHT:      We propose a method for divergent search in the few-shot image classification setting and evaluate with Omniglot and Mini-ImageNet.

73, TITLE:      Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
http://arxiv.org/abs/2004.07936
AUTHORS:        Weijian Li ; Haofu Liao ; Shun Miao ; Le Lu ; Jiebo Luo
HIGHLIGHT:      We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images.

74, TITLE:      Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for Scene Understanding
http://arxiv.org/abs/2004.07944
AUTHORS:        Panagiotis Meletis ; Xiaoxiao Wen ; Chenyang Lu ; Daan de Geus ; Gijs Dubbelman
HIGHLIGHT:      In this technical report, we present two novel datasets for image scene understanding.

75, TITLE:      Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training
http://arxiv.org/abs/2004.07923
AUTHORS:        R. Jafari ; P. Spincemaille ; J. Zhang ; T. D. Nguyen ; M. R. Prince ; X. Luo ; J. Cho ; D. Margolis ; Y. Wang
COMMENTS:       19 pages, 5 figures
HIGHLIGHT:      Here we propose two novel DNN water/fat separation methods 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no-training of DNN (NTD) using physical cost and backpropagation to directly reconstruct a single dataset.

76, TITLE:      MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library
http://arxiv.org/abs/2004.07928
AUTHORS:        Dmitry Kazhdan ; Zohreh Shams ; Pietro Liò
COMMENTS:       Presented at the KR2ML workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
HIGHLIGHT:      In this work, we introduce MARLeME: a MARL model extraction library, designed to improve explainability of MARL systems by approximating them with symbolic models.

77, TITLE:      Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS
http://arxiv.org/abs/2004.07965
AUTHORS:        Pradeeban Kathiravelu ; Ashish Sharma ; Saptarshi Purkayastha ; Priyanshu Sinha ; Alexandre Cadrin-Chenevert ; Imon Banerjee ; Judy Wawira Gichoya
HIGHLIGHT:      We propose Niffler, an integrated ML framework that runs in research clusters that receives radiology images in real-time from hospitals' Picture Archiving and Communication Systems (PACS).

78, TITLE:      Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
http://arxiv.org/abs/2004.07967
AUTHORS:        Huy Manh Nguyen ; Tomo Miyazaki ; Yoshihiro Sugaya ; Shinichiro Omachi
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      In this paper, we propose a novel framework that maps instances into multiple individual embedding spaces so that we can capture multiple relationships between instances, leading to compelling video retrieval.



==========Updates to Previous Papers==========
1, TITLE:       Multi-Gradient Descent for Multi-Objective Recommender Systems
http://arxiv.org/abs/2001.00846
AUTHORS:        Nikola Milojkovic ; Diego Antognini ; Giancarlo Bergamin ; Boi Faltings ; Claudiu Musat
COMMENTS:       9 pages, 4 figures, Accepted at AAAI 2020 - Workshop on Interactive and Conversational Recommendation Systems (WICRS)
HIGHLIGHT:      We introduce a stochastic multi-gradient descent approach to recommender systems (MGDRec) to solve this problem.

2, TITLE:       A Simple Baseline for Multi-Object Tracking
http://arxiv.org/abs/2004.01888
AUTHORS:        Yifu Zhang ; Chunyu Wang ; Xinggang Wang ; Wenjun Zeng ; Wenyu Liu
HIGHLIGHT:      In this work, we study the essential reasons behind the failure, and accordingly present a simple baseline to addresses the problems.

3, TITLE:       Info Intervention
http://arxiv.org/abs/1907.11090
AUTHORS:        Gong Heyang ; Zhu Ke
COMMENTS:       See more information on Causal AI: https://sites.google.com/view/minituring/home
HIGHLIGHT:      To highlight the view of causality as information transfer we propose the info intervention, which intervening the sending out information.

4, TITLE:       MDEA: Malware Detection with Evolutionary Adversarial Learning
http://arxiv.org/abs/2002.03331
AUTHORS:        Xiruo Wang ; Risto Miikkulainen
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      This paper proposes a new approach: MDEA, an Adversarial Malware Detection model uses evolutionary optimization to create attack samples to make the network robust against evasion attacks.

5, TITLE:       Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
http://arxiv.org/abs/1909.01520
AUTHORS:        Tyler L. Hayes ; Christopher Kanan
COMMENTS:       To appear in the IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPR-W) on Continual Learning in Computer Vision (CLVision) 2020
HIGHLIGHT:      Here, we revisit streaming linear discriminant analysis, which has been widely used in the data mining research community.

6, TITLE:       Cognitively-Inspired Model for Incremental Learning Using a Few Examples
http://arxiv.org/abs/2002.12411
AUTHORS:        Ali Ayub ; Alan Wagner
COMMENTS:       Accepted at IEEE CVPRW 2020
HIGHLIGHT:      To solve this problem, we propose a novel approach inspired by the concept learning model of the hippocampus and the neocortex that represents each image class as centroids and does not suffer from catastrophic forgetting.

7, TITLE:       Stochastic Latent Residual Video Prediction
http://arxiv.org/abs/2002.09219
AUTHORS:        Jean-Yves Franceschi ; Edouard Delasalles ; Mickaël Chen ; Sylvain Lamprier ; Patrick Gallinari
HIGHLIGHT:      In this paper, we overcome these difficulties by introducing a novel stochastic temporal model whose dynamics are governed in a latent space by a residual update rule.

8, TITLE:       One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases
http://arxiv.org/abs/1810.05241
AUTHORS:        Xingdi Yuan ; Tong Wang ; Rui Meng ; Khushboo Thaker ; Peter Brusilovsky ; Daqing He ; Adam Trischler
COMMENTS:       ACL 2020
HIGHLIGHT:      In this study, we address this problem from both modeling and evaluation perspectives.

9, TITLE:       Representation Learning of Histopathology Images using Graph Neural Networks
http://arxiv.org/abs/2004.07399
AUTHORS:        Mohammed Adnan ; Shivam Kalra ; Hamid R. Tizhoosh
COMMENTS:       Published in CVMI at CVPR Workshops, 2020
HIGHLIGHT:      We propose a two-stage framework for WSI representation learning.

10, TITLE:      Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task
http://arxiv.org/abs/2002.03737
AUTHORS:        Chuanguang Yang ; Zhulin An ; Xiaolong Hu ; Hui Zhu ; Yongjun Xu
HIGHLIGHT:      In this paper, we construct an interpretable AnchorNet equipped with our carefully designed RFs and linearly spatial aggregation to provide patch-wise interpretability of the input media meanwhile localizing multi-scale informative patches only supervised on media-level labels without any extra bounding box annotations.

11, TITLE:      Memorize, Then Recall: A Generative Framework for Low Bit-rate Surveillance Video Compression
http://arxiv.org/abs/1912.12847
AUTHORS:        Yaojun Wu ; Tianyu He ; Zhibo Chen
COMMENTS:       accepted to ISCAS2020, 5 pages
HIGHLIGHT:      In this paper, we alleviate this issue by decomposing surveillance video signals into the structure of a global spatio-temporal feature (memory) and skeleton for each frame (clue).

12, TITLE:      Histogram Layers for Texture Analysis
http://arxiv.org/abs/2001.00215
AUTHORS:        Joshua Peeples ; Weihuang Xu ; Alina Zare
COMMENTS:       16 pages, 6 figures
HIGHLIGHT:      We present a histogram layer for artificial neural networks (ANNs).

13, TITLE:      RWF-2000: An Open Large Scale Video Database for Violence Detection
http://arxiv.org/abs/1911.05913
AUTHORS:        Ming Cheng ; Kunjing Cai ; Ming Li
COMMENTS:       Submitted to ICPR'20
HIGHLIGHT:      In this paper, we summarize several existing video datasets for violence detection and propose a new video dataset with 2,000 videos all captured by surveillance cameras in real-world scenes.

14, TITLE:      Quaternion Equivariant Capsule Networks for 3D Point Clouds
http://arxiv.org/abs/1912.12098
AUTHORS:        Yongheng Zhao ; Tolga Birdal ; Jan Eric Lenssen ; Emanuele Menegatti ; Leonidas Guibas ; Federico Tombari
HIGHLIGHT:      We present a 3D capsule architecture for processing of point clouds that is equivariant with respect to the $SO(3)$ rotation group, translation and permutation of the unordered input sets.

15, TITLE:      Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm
http://arxiv.org/abs/2004.07657
AUTHORS:        Muhammad Zaigham Zaheer ; Jin-ha Lee ; Marcella Astrid ; Seung-Ik Lee
COMMENTS:       Accepted at CVPR2020
HIGHLIGHT:      In this study, we propose a framework that effectively generates stable results across a wide range of training steps and allows us to use both the generator and the discriminator of an adversarial model for efficient and robust anomaly detection.

16, TITLE:      SECRET: Semantically Enhanced Classification of Real-world Tasks
http://arxiv.org/abs/1905.12356
AUTHORS:        Ayten Ozge Akmandor ; Jorge Ortiz ; Irene Manotas ; Bongjun Ko ; Niraj K. Jha
COMMENTS:       16 pages, 20 figures, 2 tables - IEEE Transactions on Computers
HIGHLIGHT:      In this paper, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks).

17, TITLE:      Is it Easier to Prove Theorems that are Guaranteed to be True?
http://arxiv.org/abs/1906.10837
AUTHORS:        Rafael Pass ; Muthuramakrishnan Venkitasubramaniam
HIGHLIGHT:      As another consequence of this treatment, we show that the existence of O(1)-round public-coin non-trivial arguments (i.e., argument systems that are not proofs) imply the existence of a hard-on-average problem in NP/poly.

18, TITLE:      Knowledge Graphs
http://arxiv.org/abs/2003.02320
AUTHORS:        Aidan Hogan ; Eva Blomqvist ; Michael Cochez ; Claudia d'Amato ; Gerard de Melo ; Claudio Gutierrez ; José Emilio Labra Gayo ; Sabrina Kirrane ; Sebastian Neumaier ; Axel Polleres ; Roberto Navigli ; Axel-Cyrille Ngonga Ngomo ; Sabbir M. Rashid ; Anisa Rula ; Lukas Schmelzeisen ; Juan Sequeda ; Steffen Staab ; Antoine Zimmermann
COMMENTS:       Revision from v1: Fixing Figure 3 - Giving Figure 4 analogous data to Figure 3 - Updating Section 2.1.3. - Updating Example B.6. Revision from v2: Fixed Figure 8 - Adding Table 1 and related literature - Adding Figure 21 - Clarification in the discussion on UNA/NUNA - Renaming Section 3.2.1 to Persistent Identifiers and extending discussion - Other minor fixes
HIGHLIGHT:      In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.

19, TITLE:      On the Variance of the Adaptive Learning Rate and Beyond
http://arxiv.org/abs/1908.03265
AUTHORS:        Liyuan Liu ; Haoming Jiang ; Pengcheng He ; Weizhu Chen ; Xiaodong Liu ; Jianfeng Gao ; Jiawei Han
COMMENTS:       ICLR 2020. Fix several typos in the previous version
HIGHLIGHT:      Pursuing the theory behind warmup, we identify a problem of the adaptive learning rate (i.e., it has problematically large variance in the early stage), suggest warmup works as a variance reduction technique, and provide both empirical and theoretical evidence to verify our hypothesis.

20, TITLE:      Soft Threshold Weight Reparameterization for Learnable Sparsity
http://arxiv.org/abs/2002.03231
AUTHORS:        Aditya Kusupati ; Vivek Ramanujan ; Raghav Somani ; Mitchell Wortsman ; Prateek Jain ; Sham Kakade ; Ali Farhadi
COMMENTS:       18 pages, 9 figures
HIGHLIGHT:      This work proposes Soft Threshold Reparameterization (STR), a novel use of the soft-threshold operator on DNN weights.

21, TITLE:      Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network
http://arxiv.org/abs/1905.05622
AUTHORS:        Boyi Jiang ; Juyong Zhang ; Jianfei Cai ; Jianmin Zheng
COMMENTS:       This manuscript is accepted for publication in the IEEE Transactions on Visualization and Computer Graphics Journal (IEEE TVCG). The Code is available at https://github.com/Juyong/DHNN_BodyRepresentation
HIGHLIGHT:      To improve the reconstruction accuracy, we propose a hierarchical reconstruction pipeline for the disentangling process and construct a large dataset of human body models with consistent connectivity for the learning of the neural network.

22, TITLE:      SMT-Friendly Formalization of the Solidity Memory Model
http://arxiv.org/abs/2001.03256
AUTHORS:        Ákos Hajdu ; Dejan Jovanović
COMMENTS:       Authors' manuscript. Published in P. M\"uller (Ed.): ESOP 2020, LNCS 12075, 2020. The final publication is available at Springer via https://doi.org/10.1007/978-3-030-44914-8_9
HIGHLIGHT:      This paper presents a high-level formalization of the Solidity language with a focus on the memory model. The formalization is implemented in the solc-verify verifier and we provide an extensive set of tests that covers the breadth of the required semantics.

23, TITLE:      Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
http://arxiv.org/abs/2003.14166
AUTHORS:        Sai Rajeswar ; Fahim Mannan ; Florian Golemo ; Jérôme Parent-Lévesque ; David Vazquez ; Derek Nowrouzezahrai ; Aaron Courville
COMMENTS:       This is a pre-print of an article published in International Journal of Computer Vision. The final authenticated version is available online at: https://doi.org/10.1007/s11263-020-01322-1
HIGHLIGHT:      We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2.5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution.

24, TITLE:      Model-based actor-critic: GAN + DRL (actor-critic) => AGI
http://arxiv.org/abs/2004.04574
AUTHORS:        Aras Dargazany
COMMENTS:       arXiv admin note: text overlap with arXiv:1610.01945, arXiv:1903.04411, arXiv:1910.01007 by other authors
HIGHLIGHT:      To evaluate it, we compare it with (model-free) DDPG by applying them both to a variety (wide range) of independent simulated robotic and control task environments in OpenAI Gym and Unity Agents.

25, TITLE:      The surprising little effectiveness of cooperative algorithms in parallel problem solving
http://arxiv.org/abs/1912.03347
AUTHORS:        Sandro M. Reia ; Larissa F. Aquino ; José F. Fontanari
HIGHLIGHT:      Here we study the performances of a cultural-inspired algorithm -- the imitative learning search -- as well as of asexual and sexual variants of evolutionary algorithms in finding the global maxima of NK-fitness landscapes.

26, TITLE:      Towards a Zero-One Law for Column Subset Selection
http://arxiv.org/abs/1811.01442
AUTHORS:        Zhao Song ; David P. Woodruff ; Peilin Zhong
COMMENTS:       NeurIPS 2019
HIGHLIGHT:      In this work we give approximation algorithms for $\textit{every}$ function $g$ which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary.

27, TITLE:      Accurate Vision-based Manipulation through Contact Reasoning
http://arxiv.org/abs/1911.03112
AUTHORS:        Alina Kloss ; Maria Bauza ; Jiajun Wu ; Joshua B. Tenenbaum ; Alberto Rodriguez ; Jeannette Bohg
COMMENTS:       accepted at ICRA 2020
HIGHLIGHT:      We present an approach that addresses these two challenges for the problem of vision-based manipulation.

28, TITLE:      SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning
http://arxiv.org/abs/1810.04991
AUTHORS:        Xiaoming Yu ; Xing Cai ; Zhenqiang Ying ; Thomas Li ; Ge Li
COMMENTS:       Accepted in ACCV 2018. Code is available at https://github.com/Xiaoming-Yu/SingleGAN
HIGHLIGHT:      In this paper, we propose a novel method, SingleGAN, to perform multi-domain image-to-image translations with a single generator.

29, TITLE:      Multi-Mapping Image-to-Image Translation with Central Biasing Normalization
http://arxiv.org/abs/1806.10050
AUTHORS:        Xiaoming Yu ; Zhenqiang Ying ; Thomas Li ; Shan Liu ; Ge Li
HIGHLIGHT:      To solve these problems, we propose the consistency within diversity criteria for designing the multi-mapping model.

30, TITLE:      Active Learning with Point Supervision for Cost-Effective Panicle Detection in Cereal Crops
http://arxiv.org/abs/1910.01789
AUTHORS:        Akshay L Chandra ; Sai Vikas Desai ; Vineeth N Balasubramanian ; Seishi Ninomiya ; Wei Guo
COMMENTS:       Accepted as a journal paper at BMC Plant Methods (February 2020)
HIGHLIGHT:      We propose a point supervision based active learning approach for panicle detection in cereal crops.

31, TITLE:      Deep Neural Rejection against Adversarial Examples
http://arxiv.org/abs/1910.00470
AUTHORS:        Angelo Sotgiu ; Ambra Demontis ; Marco Melis ; Battista Biggio ; Giorgio Fumera ; Xiaoyi Feng ; Fabio Roli
HIGHLIGHT:      In this work, we propose a deep neural rejection mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous feature representations at different network layers.

32, TITLE:      Quantifying Exposure Bias for Neural Language Generation
http://arxiv.org/abs/1905.10617
AUTHORS:        Tianxing He ; Jingzhao Zhang ; Zhiming Zhou ; James Glass
HIGHLIGHT:      We conduct experiments for the LSTM/transformer model, in both real and synthetic settings.

33, TITLE:      READ: Recursive Autoencoders for Document Layout Generation
http://arxiv.org/abs/1909.00302
AUTHORS:        Akshay Gadi Patil ; Omri Ben-Eliezer ; Or Perel ; Hadar Averbuch-Elor
HIGHLIGHT:      In this paper, we present a novel framework, coined READ, for REcursive Autoencoders for Document layout generation, to generate plausible 2D layouts of documents in large quantities and varieties.

34, TITLE:      COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images
http://arxiv.org/abs/2004.02696
AUTHORS:        Parnian Afshar ; Shahin Heidarian ; Farnoosh Naderkhani ; Anastasia Oikonomou ; Konstantinos N. Plataniotis ; Arash Mohammadi
HIGHLIGHT:      The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19.

35, TITLE:      Work in Progress: Temporally Extended Auxiliary Tasks
http://arxiv.org/abs/2004.00600
AUTHORS:        Craig Sherstan ; Bilal Kartal ; Pablo Hernandez-Leal ; Matthew E. Taylor
COMMENTS:       Accepted for the Adaptive and Learning Agents (ALA) Workshop at AAMAS 2020
HIGHLIGHT:      The primary purpose of the work presented here is to investigate the impact that an auxiliary task's prediction timescale has on the agent's policy performance.

36, TITLE:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
http://arxiv.org/abs/2004.06165
AUTHORS:        Xiujun Li ; Xi Yin ; Chunyuan Li ; Pengchuan Zhang ; Xiaowei Hu ; Lei Zhang ; Lijuan Wang ; Houdong Hu ; Li Dong ; Furu Wei ; Yejin Choi ; Jianfeng Gao
HIGHLIGHT:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

37, TITLE:      Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study
http://arxiv.org/abs/2003.11145
AUTHORS:        Dinh-Luan Nguyen ; Sunpreet S. Arora ; Yuhang Wu ; Hao Yang
COMMENTS:       To appear in the proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR) Biometrics Workshop 2020 - 9 pages, 8 figures
HIGHLIGHT:      We investigate the feasibility of conducting real-time physical attacks on face recognition systems using adversarial light projections.

38, TITLE:      Contextual Word Representations: A Contextual Introduction
http://arxiv.org/abs/1902.06006
AUTHORS:        Noah A. Smith
HIGHLIGHT:      It targets a wide audience with a basic understanding of computer programming, but avoids a detailed mathematical treatment, and it does not present any algorithms.

39, TITLE:      Gastric histopathology image segmentation using a hierarchical conditional random field
http://arxiv.org/abs/2003.01302
AUTHORS:        Changhao Sun ; Chen Li ; Jinghua Zhang ; Shiliang Ai ; Hao Chen ; Frank Kulwa ; Xiaoyan Li ; Tao Jiang
HIGHLIGHT:      In this paper, a novel hierarchical conditional random field (HCRF) model based gastric histopathology image segmentation (GHIS) method is proposed, which can automatically localize abnormal (cancer) regions in gastric histopathology images obtained by the optical microscope to assist histopathologists in medical work.

40, TITLE:      One Model to Recognize Them All: Marginal Distillation from NER Models with Different Tag Sets
http://arxiv.org/abs/2004.05140
AUTHORS:        Keunwoo Peter Yu ; Yi Yang
COMMENTS:       9 pages, LaTeX; column header of Table 2 corrected
HIGHLIGHT:      This paper presents a marginal distillation (MARDI) approach for training a unified NER model from resources with disjoint or heterogeneous tag sets.

41, TITLE:      Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
http://arxiv.org/abs/2001.12004
AUTHORS:        Joseph Suarez ; Yilun Du ; Igor Mordatch ; Phillip Isola
HIGHLIGHT:      We present Neural MMO, a massively multiagent game environment inspired by MMOs and discuss our progress on two more general challenges in multiagent systems engineering for AI research: distributed infrastructure and game IO.

42, TITLE:      LDPC Codes Achieve List Decoding Capacity
http://arxiv.org/abs/1909.06430
AUTHORS:        Jonathan Mosheiff ; Nicolas Resch ; Noga Ron-Zewi ; Shashwat Silas ; Mary Wootters
COMMENTS:       36 pages, 3 figures. In this updated version, the concept of a threshold for random linear codes is developed in more detail
HIGHLIGHT:      We show that Gallager's ensemble of Low-Density Parity Check (LDPC) codes achieves list-decoding capacity with high probability.

43, TITLE:      Quality Diversity for Multi-task Optimization
http://arxiv.org/abs/2003.04407
AUTHORS:        Jean-Baptiste Mouret ; Glenn Maguire
HIGHLIGHT:      In this paper, we propose an extension of the MAP-Elites algorithm, called Multi-task MAP-Elites, that solves multiple tasks when the fitness function depends on the task.

44, TITLE:      How Additional Knowledge can Improve Natural Language Commonsense Question Answering?
http://arxiv.org/abs/1909.08855
AUTHORS:        Arindam Mitra ; Pratyay Banerjee ; Kuntal Kumar Pal ; Swaroop Mishra ; Chitta Baral
COMMENTS:       14 pages, 14 figures, 3 tables
HIGHLIGHT:      Our goal in this work is to develop methods to incorporate additional (commonsense) knowledge into language model-based approaches for better question-answering in such domains.

45, TITLE:      A Robust Reputation-based Group Ranking System and its Resistance to Bribery
http://arxiv.org/abs/2004.06223
AUTHORS:        Joao Saude ; Guilherme Ramos ; Ludovico Boratto ; Carlos Caleiro
COMMENTS:       28 pages, 14 figures
HIGHLIGHT:      In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity.

46, TITLE:      There is Strength in Numbers: Avoiding the Hypothesis-Only Bias in Natural Language Inference via Ensemble Adversarial Training
http://arxiv.org/abs/2004.07790
AUTHORS:        Joe Stacey ; Pasquale Minervini ; Haim Dubossarsky ; Sebastian Riedel ; Tim Rocktäschel
COMMENTS:       8 pages
HIGHLIGHT:      As a solution, we propose using an ensemble of adversaries during the training, encouraging the model to jointly decrease the accuracy of these different adversaries while fitting the data.

47, TITLE:      Knowledge Guided Named Entity Recognition for BioMedical Text
http://arxiv.org/abs/1911.03869
AUTHORS:        Pratyay Banerjee ; Kuntal Kumar Pal ; Murthy Devarakonda ; Chitta Baral
COMMENTS:       6 pages, 2 figures, 5 tables, WIP
HIGHLIGHT:      In this work, we formulate the NER task as a multi-answer question answering (MAQA) task and provide different knowledge contexts, such as entity types, questions, definitions and definitions with examples.

48, TITLE:      Self-Paced Deep Regression Forests for Facial Age Estimation
http://arxiv.org/abs/1910.03244
AUTHORS:        Shijie Ai ; Lili Pan ; Yazhou Ren
COMMENTS:       7 pages, 5 figures, 2 tables
HIGHLIGHT:      To this end, we propose self-paced deep regression forests (SP-DRFs) -- a gradual learning DNNs framework for age estimation.

49, TITLE:      Multi-source Attention for Unsupervised Domain Adaptation
http://arxiv.org/abs/2004.06608
AUTHORS:        Xia Cui ; Danushka Bollegala
HIGHLIGHT:      Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain.

50, TITLE:      Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering
http://arxiv.org/abs/2004.03101
AUTHORS:        Pratyay Banerjee ; Chitta Baral
COMMENTS:       9 pages. 4 figures, 4 tables
HIGHLIGHT:      In our work, we learn a semantic knowledge ranking model to re-rank knowledge retrieved through Lucene based information retrieval systems.

51, TITLE:      Learning from Small Data Through Sampling an Implicit Conditional Generative Latent Optimization Model
http://arxiv.org/abs/2003.14297
AUTHORS:        Idan Azuri ; Daphna Weinshall
COMMENTS:       10 pages, 6 figures. Correct typos
HIGHLIGHT:      We propose a new model based on conditional Generative Latent Optimization (cGLO).

52, TITLE:      Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion
http://arxiv.org/abs/2003.04490
AUTHORS:        Adam Kortylewski ; Ju He ; Qing Liu ; Alan Yuille
COMMENTS:       CVPR 2020; Code is available https://github.com/AdamKortylewski/CompositionalNets; Supplementary material: https://adamkortylewski.com/data/compnet_supp.pdf
HIGHLIGHT:      The code used in this work is publicly available.

53, TITLE:      Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition
http://arxiv.org/abs/1904.01189
AUTHORS:        Pengfei Zhang ; Cuiling Lan ; Wenjun Zeng ; Junliang Xing ; Jianru Xue ; Nanning Zheng
COMMENTS:       Accepted by CVPR2020. The source code is available at https://github.com/microsoft/SGN
HIGHLIGHT:      In this paper, we propose a simple yet effective semantics-guided neural network (SGN) for skeleton-based action recognition.

54, TITLE:      Butterfly Transform: An Efficient FFT Based Neural Architecture Design
http://arxiv.org/abs/1906.02256
AUTHORS:        Keivan Alizadeh Vahid ; Anish Prabhu ; Ali Farhadi ; Mohammad Rastegari
HIGHLIGHT:      In this paper, we show that extending the butterfly operations from the FFT algorithm to a general Butterfly Transform (BFT) can be beneficial in building an efficient block structure for CNN designs. We introduce a set of criteria for channel fusion and prove that BFT yields an asymptotically optimal FLOP count with respect to these criteria.