2020.06.08.txt

==========New Papers==========
1, TITLE:       SOLO: A Corpus of Tweets for Examining the State of Being Alone
http://arxiv.org/abs/2006.03096
AUTHORS:        Svetlana Kiritchenko ; Will E. Hipson ; Robert J. Coplan ; Saif M. Mohammad
COMMENTS:       In Proceedings of the 12th edition of the Language Resources and Evaluation Conference (LREC), May 2020
HIGHLIGHT:      We present SOLO (State of Being Alone), a corpus of over 4 million tweets collected with query terms 'solitude', 'lonely', and 'loneliness'.

2, TITLE:       The Importance of Open-Endedness (for the Sake of Open-Endedness)
http://arxiv.org/abs/2006.03079
AUTHORS:        Tim Taylor
COMMENTS:       To appear in Proceedings of the Artificial Life Conference 2020 (ALIFE 2020), MIT Press
HIGHLIGHT:      In the same issue of the journal, I presented a "high-level conceptual framework to help orient the discussion and implementation of open-endedness in evolutionary systems" (Taylor, 2019).

3, TITLE:       Evaluating Text Coherence at Sentence and Paragraph Levels
http://arxiv.org/abs/2006.03221
AUTHORS:        Sennan Liu ; Shuang Zeng ; Sujian Li
COMMENTS:       Long paper accepted by LREC 2020
HIGHLIGHT:      In this paper, to evaluate text coherence, we propose the paragraph ordering task as well as conducting sentence ordering. We collected four distinct corpora from different domains on which we investigate the adaptation of existing sentence ordering methods to a paragraph ordering task.

4, TITLE:       Sponge Examples: Energy-Latency Attacks on Neural Networks
http://arxiv.org/abs/2006.03463
AUTHORS:        Ilia Shumailov ; Yiren Zhao ; Daniel Bates ; Nicolas Papernot ; Robert Mullins ; Ross Anderson
HIGHLIGHT:      In this work, we introduce a novel threat vector against neural networks whose energy consumption or decision latency are critical.

5, TITLE:       Content and Context Features for Scene Image Representation
http://arxiv.org/abs/2006.03217
AUTHORS:        Chiranjibi Sitaula ; Sunil Aryal ; Yong Xiang ; Anish Basnet ; Xuequan Lu
COMMENTS:       Submitted to IEEE Transactions on Multimedia (TMM) (under review), 11 pages and 8 images
HIGHLIGHT:      In this paper, we propose new techniques to compute content features and context features, and then fuse them together.

6, TITLE:       Sentence Compression as Deletion with Contextual Embeddings
http://arxiv.org/abs/2006.03210
AUTHORS:        Minh-Tien Nguyen ; Bui Cong Minh ; Dung Tien Le ; Le Thai Linh
COMMENTS:       12 pages, 3 figures, accepted by ICCCI 2020
HIGHLIGHT:      In this paper, we extend the task of compression by deletion with the use of contextual embeddings.

7, TITLE:       Content-Aware Inter-Scale Cost Aggregation for Stereo Matching
http://arxiv.org/abs/2006.03209
AUTHORS:        Chengtang Yao ; Yunde Jia ; Huijun Di ; Yuwei Wu ; Lidong Yu
COMMENTS:       19 pages, 14 figures, 5 tables
HIGHLIGHT:      In this paper, we present a content-aware inter-scale cost aggregation method that adaptively aggregates and upsamples the cost volume from coarse-scale to fine-scale by learning dynamic filter weights according to the content of the left and right views on the two scales.

8, TITLE:       Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment
http://arxiv.org/abs/2006.03202
AUTHORS:        Sharon Levy ; William Yang Wang
HIGHLIGHT:      To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment.

9, TITLE:       Black-box Explanation of Object Detectors via Saliency Maps
http://arxiv.org/abs/2006.03204
AUTHORS:        Vitali Petsiuk ; Rajiv Jain ; Varun Manjunatha ; Vlad I. Morariu ; Ashutosh Mehra ; Vicente Ordonez ; Kate Saenko
HIGHLIGHT:      We propose D-RISE, a method for generating visual explanations for the predictions of object detectors.

10, TITLE:      Egocentric Object Manipulation Graphs
http://arxiv.org/abs/2006.03201
AUTHORS:        Eadom Dessalene ; Michael Maynord ; Chinmaya Devaraj ; Cornelia Fermuller ; Yiannis Aloimonos
HIGHLIGHT:      We introduce Egocentric Object Manipulation Graphs (Ego-OMG) - a novel representation for activity modeling and anticipation of near future actions integrating three components: 1) semantic temporal structure of activities, 2) short-term dynamics, and 3) representations for appearance.

11, TITLE:      Artificial Intelligence-based Clinical Decision Support for COVID-19 -- Where Art Thou?
http://arxiv.org/abs/2006.03434
AUTHORS:        Mathias Unberath ; Kimia Ghobadi ; Scott Levin ; Jeremiah Hinson ; Gregory D Hager
COMMENTS:       Invited perspective piece on AI in the fight against COVID-19 to appear in Advanced Intelligent Systems
HIGHLIGHT:      In this perspective piece, we identify opportunities and requirements for AI-based clinical decision support systems and highlight challenges that impact "AI readiness" for rapidly emergent healthcare challenges.

12, TITLE:      Learning Neural Light Transport
http://arxiv.org/abs/2006.03427
AUTHORS:        Paul Sanzenbacher ; Lars Mescheder ; Andreas Geiger
COMMENTS:       31 pages, 17 figures
HIGHLIGHT:      In this work, we investigate the importance of 3D reasoning for photorealistic rendering.

13, TITLE:      Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
http://arxiv.org/abs/2006.03429
AUTHORS:        Robert Müller ; Fabian Ritz ; Steffen Illium ; Claudia Linnhoff-Popien
COMMENTS:       submitted to INTERSPEECH, 5 pages, 2 figures, 1 table
HIGHLIGHT:      In this paper, we consider acoustic malfunction detection via transfer learning.

14, TITLE:      SEAL: Scientific Keyphrase Extraction and Classification
http://arxiv.org/abs/2006.03292
AUTHORS:        Ayush Garg ; Sammed Shantinath Kagi ; Mayank Singh
COMMENTS:       Accepted at JCDL 2020
HIGHLIGHT:      In this paper, we introduce SEAL, a scholarly tool for automatic keyphrase extraction and classification.

15, TITLE:      Biometric Quality: Review and Application to Face Recognition with FaceQnet
http://arxiv.org/abs/2006.03298
AUTHORS:        Javier Hernandez-Ortega ; Javier Galbally ; Julian Fierrez ; Laurent Beslay
HIGHLIGHT:      After a gentle introduction to the general topic of biometric quality and a review of past efforts in face quality metrics, in the present work, we address the need for better face quality metrics by developing FaceQnet.

16, TITLE:      NewB: 200,000+ Sentences for Political Bias Detection
http://arxiv.org/abs/2006.03051
AUTHORS:        Jerry Wei
HIGHLIGHT:      We present the Newspaper Bias Dataset (NewB), a text corpus of more than 200,000 sentences from eleven news sources regarding Donald Trump.

17, TITLE:      Conflict-Based Search for Connected Multi-Agent Path Finding
http://arxiv.org/abs/2006.03280
AUTHORS:        Arthur Queffelec ; Ocan Sankur ; François Schwarzentruber
HIGHLIGHT:      We study a variant of the multi-agent path finding problem (MAPF) in which agents are required to remain connected to each other and to a designated base.

18, TITLE:      GMAT: Global Memory Augmentation for Transformers
http://arxiv.org/abs/2006.03274
AUTHORS:        Ankit Gupta ; Jonathan Berant
HIGHLIGHT:      In this work, we propose to augment sparse Transformer blocks with a dense attention-based $\textit{global memory}$ of length $M$ ($\ll L$) which provides an aggregate global view of the entire input sequence to each position.

19, TITLE:      Understanding Self-Attention of Self-Supervised Audio Transformers
http://arxiv.org/abs/2006.03265
AUTHORS:        Shu-wen Yang ; Andy T. Liu ; Hung-yi Lee
COMMENTS:       5 pages, 6 figures
HIGHLIGHT:      In this work, we present multiple strategies for the analysis of attention mechanisms in SAT.

20, TITLE:      Convolutional Neural Networks for Global Human Settlements Mapping from Sentinel-2 Satellite Imagery
http://arxiv.org/abs/2006.03267
AUTHORS:        Christina Corbane ; Vasileios Syrris ; Filip Sabo ; Panagiotis Politis ; Michele Melchiorri ; Martino Pesaresi ; Pierre Soille ; Thomas Kemper
COMMENTS:       51 pages including supplementary material, 13 Figures in the main manuscript, under review in Neural Computing and Applications journal
HIGHLIGHT:      This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 meters from a global composite of Sentinel-2 imagery.

21, TITLE:      Optimising Tours for the Weighted Traveling Salesperson Problem and the Traveling Thief Problem: A Structural Comparison of Solutions
http://arxiv.org/abs/2006.03260
AUTHORS:        Jakob Bossek ; Aneta Neumann ; Frank Neumann
HIGHLIGHT:      In this paper, we compare W-TSP and TTP.

22, TITLE:      Aspect-based Sentiment Analysis of Scientific Reviews
http://arxiv.org/abs/2006.03257
AUTHORS:        Souvic Chakraborty ; Pawan Goyal ; Animesh Mukherjee
COMMENTS:       Accepted in JCDL'20
HIGHLIGHT:      In this paper, we propose to use aspect-based sentiment analysis of scientific reviews to be able to extract useful information, which correlates well with the accept/reject decision.

23, TITLE:      Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
http://arxiv.org/abs/2006.03259
AUTHORS:        Xin Cheng ; Lei Zhang ; Yin Tang ; Yue Liu ; Hao Wu ; Jun He
COMMENTS:       10 pages,14 figures
HIGHLIGHT:      In this paper, we for the first time propose a computation efficient CNN using conditionally parametrized convolution for real-time HAR on mobile and wearable devices.

24, TITLE:      TCDesc: Learning Topology Consistent Descriptors
http://arxiv.org/abs/2006.03254
AUTHORS:        Honghu Pan ; Fanyang Meng ; Zhenyu He ; Yongsheng Liang ; Wei Liu
HIGHLIGHT:      In this paper, we propose topology measure besides Euclidean distance to learn topology consistent descriptors by considering kNN descriptors of positive sample.

25, TITLE:      "To Target or Not to Target": Identification and Analysis of Abusive Text Using Ensemble of Classifiers
http://arxiv.org/abs/2006.03256
AUTHORS:        Gaurav Verma ; Niyati Chhaya ; Vishwa Vinay
COMMENTS:       In ICWSM'20 Safety Data Challenge
HIGHLIGHT:      With rising concern around abusive and hateful behavior on social media platforms, we present an ensemble learning method to identify and analyze the linguistic properties of such content.

26, TITLE:      VALUE: Large Scale Voting-based Automatic Labelling for Urban Environments
http://arxiv.org/abs/2006.03492
AUTHORS:        Giacomo Dabisias ; Emanuele Ruffaldi ; Hugo Grimmett ; Peter Ondruska
COMMENTS:       Presented at ICRA-2018 conference, 20-25th May 2018, Brisbane, Australia
HIGHLIGHT:      This paper presents a simple and robust method for the automatic localisation of static 3D objects in large-scale urban environments. To evaluate the method we collected city-scale data sets from New York City and San Francisco consisting of almost 400k images spanning the area of 40 km$^2$ and used it to accurately recover the 3D positions of traffic lights.

27, 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.

28, TITLE:      Segmentation of Surgical Instruments for Minimally-Invasive Robot-Assisted Procedures Using Generative Deep Neural Networks
http://arxiv.org/abs/2006.03486
AUTHORS:        Iñigo Azqueta-Gavaldon ; Florian Fröhlich ; Klaus Strobl ; Rudolph Triebel
HIGHLIGHT:      This work proves that semantic segmentation on minimally invasive surgical instruments can be improved by using training data that has been augmented through domain adaptation.

29, TITLE:      Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
http://arxiv.org/abs/2006.03236
AUTHORS:        Zihang Dai ; Guokun Lai ; Yiming Yang ; Quoc V. Le
HIGHLIGHT:      With this intuition, we propose Funnel-Transformer which gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost.

30, TITLE:      Population-Based Black-Box Optimization for Biological Sequence Design
http://arxiv.org/abs/2006.03227
AUTHORS:        Christof Angermueller ; David Belanger ; Andreea Gane ; Zelda Mariet ; David Dohan ; Kevin Murphy ; Lucy Colwell ; D Sculley
HIGHLIGHT:      To improve robustness, we propose Population-Based Black-Box Optimization (P3BO), which generates batches of sequences by sampling from an ensemble of methods.

31, TITLE:      Brain-inspired global-local hybrid learning towards human-like intelligence
http://arxiv.org/abs/2006.03226
AUTHORS:        Yujie Wu ; Rong Zhao ; Jun Zhu ; Feng Chen ; Mingkun Xu ; Guoqi Li ; Sen Song ; Lei Deng ; Guanrui Wang ; Hao Zheng ; Jing Pei ; Youhui Zhang ; Mingguo Zhao ; Luping Shi
COMMENTS:       5 figures, 2 tables
HIGHLIGHT:      Here, we report a hybrid spiking neural network model that integrates the two approaches by introducing a meta-local module and a two-phase causality modelling method.

32, TITLE:      Scene Image Representation by Foreground, Background and Hybrid Features
http://arxiv.org/abs/2006.03199
AUTHORS:        Chiranjibi Sitaula ; Yong Xiang ; Sunil Aryal ; Xuequan Lu
COMMENTS:       Submitted to Expert Systems with Applications (ESWA), 28 pages and 17 images
HIGHLIGHT:      In this paper, we propose to use hybrid features in addition to foreground and background features to represent scene images.

33, TITLE:      Novel Object Viewpoint Estimation through Reconstruction Alignment
http://arxiv.org/abs/2006.03586
AUTHORS:        Mohamed El Banani ; Jason J. Corso ; David F. Fouhey
COMMENTS:       To appear at CVPR 2020. Project page: https://mbanani.github.io/novelviewpoints/
HIGHLIGHT:      The goal of this paper is to estimate the viewpoint for a novel object.

34, TITLE:      Interactive Music and Synchronous Reactive Programming
http://arxiv.org/abs/2006.03102
AUTHORS:        Bertrand Petit ; Manuel Serrano
HIGHLIGHT:      This paper presents Skini, a programming methodology and an execution environment for interactive structured music.

35, TITLE:      MANTRA: Memory Augmented Networks for Multiple Trajectory Prediction
http://arxiv.org/abs/2006.03340
AUTHORS:        Francesco Marchetti ; Federico Becattini ; Lorenzo Seidenari ; Alberto Del Bimbo
COMMENTS:       Accepted at CVPR20
HIGHLIGHT:      In this paper we address the problem of multimodal trajectory prediction exploiting a Memory Augmented Neural Network.

36, TITLE:      Clique-Width: Harnessing the Power of Atoms
http://arxiv.org/abs/2006.03578
AUTHORS:        Konrad K. Dabrowski ; Tomáš Masařík ; Jana Novotná ; Daniël Paulusma ; Paweł Rzążewski
COMMENTS:       33 pages, 18 figures, accepted to WG 2020
HIGHLIGHT:      Several of these problems are polynomially solvable on a hereditary graph class G if they are so on the atoms (graphs with no clique cut-set) of G. Hence, we initiate a systematic study into boundedness of clique-width of atoms of hereditary graph classes.

37, TITLE:      ELITR Non-Native Speech Translation at IWSLT 2020
http://arxiv.org/abs/2006.03331
AUTHORS:        Dominik Macháček ; Jonáš Kratochvíl ; Sangeet Sagar ; Matúš Žilinec ; Ondřej Bojar ; Thai-Son Nguyen ; Felix Schneider ; Philip Williams ; Yuekun Yao
COMMENTS:       IWSLT 2020
HIGHLIGHT:      We describe systems for offline ASR, real-time ASR, and our cascaded approach to offline SLT and real-time SLT.

38, TITLE:      Spoken dialect identification in Twitter using a multi-filter architecture
http://arxiv.org/abs/2006.03564
AUTHORS:        Mohammadreza Banaei ; Rémi Lebret ; Karl Aberer
HIGHLIGHT:      This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter.

39, TITLE:      Masked Language Modeling for Proteins via Linearly Scalable Long-Context Transformers
http://arxiv.org/abs/2006.03555
AUTHORS:        Krzysztof Choromanski ; Valerii Likhosherstov ; David Dohan ; Xingyou Song ; Jared Davis ; Tamas Sarlos ; David Belanger ; Lucy Colwell ; Adrian Weller
COMMENTS:       14 pages, 11 figures
HIGHLIGHT:      To address this challenge, we present a new Transformer architecture, Performer, based on Fast Attention Via Orthogonal Random features (FAVOR).

40, TITLE:      PLANS: Robust Program Learning from Neurally Inferred Specifications
http://arxiv.org/abs/2006.03312
AUTHORS:        Raphaël Dang-Nhu
COMMENTS:       16 pages, 6 figures, 5 tables
HIGHLIGHT:      We introduce PLANS (Program LeArning from Neurally inferred Specifications), a hybrid model for program synthesis from visual observations that gets the best of both worlds, relying on (i) a neural architecture trained to extract abstract, high-level information from each raw individual input (ii) a rule-based system using the extracted information as I/O specifications to synthesize a program capturing the different observations.

41, TITLE:      Multi-modal Feature Fusion with Feature Attention for VATEX Captioning Challenge 2020
http://arxiv.org/abs/2006.03315
AUTHORS:        Ke Lin ; Zhuoxin Gan ; Liwei Wang
HIGHLIGHT:      This report describes our model for VATEX Captioning Challenge 2020.

42, TITLE:      Logical Team Q-learning: An approach towards factored policies in cooperative MARL
http://arxiv.org/abs/2006.03553
AUTHORS:        Lucas Cassano ; Ali H. Sayed
HIGHLIGHT:      In this work we make contributions to both the dynamic programming and reinforcement learning settings.

43, TITLE:      Sentiment Analysis Based on Deep Learning: A Comparative Study
http://arxiv.org/abs/2006.03541
AUTHORS:        Nhan Cach Dang ; María N. Moreno-García ; Fernando De la Prieta
HIGHLIGHT:      The study of public opinion can provide us with valuable information.

44, TITLE:      Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access
http://arxiv.org/abs/2006.03533
AUTHORS:        Seokhwan Kim ; Mihail Eric ; Karthik Gopalakrishnan ; Behnam Hedayatnia ; Yang Liu ; Dilek Hakkani-Tur
COMMENTS:       To be presented at SIGDIAL 2020
HIGHLIGHT:      In this paper, we propose to expand coverage of task-oriented dialogue systems by incorporating external unstructured knowledge sources.

45, TITLE:      CoCon: A Self-Supervised Approach for Controlled Text Generation
http://arxiv.org/abs/2006.03535
AUTHORS:        Alvin Chan ; Yew-Soon Ong ; Bill Pung ; Aston Zhang ; Jie Fu
HIGHLIGHT:      Here, we propose Content-Conditioner (CoCon) to control an LM's output text with a target content, at a fine-grained level.

46, TITLE:      A Meta-Bayesian Model of Intentional Visual Search
http://arxiv.org/abs/2006.03531
AUTHORS:        Maell Cullen ; Jonathan Monney ; M. Berk Mirza ; Rosalyn Moran
COMMENTS:       Submitted to NeurIPS 2020
HIGHLIGHT:      We propose a computational model of visual search that incorporates Bayesian interpretations of the neural mechanisms that underlie categorical perception and saccade planning.

47, TITLE:      Eliminating Intermediate Measurements in Space-Bounded Quantum Computation
http://arxiv.org/abs/2006.03530
AUTHORS:        Bill Fefferman ; Zachary Remscrim
HIGHLIGHT:      We give an affirmative answer to this question by exhibiting a procedure to eliminate all intermediate measurements that is simultaneously space-efficient and time-efficient.

48, TITLE:      Runtime Analysis of a Heavy-Tailed $(1+(λ,λ))$ Genetic Algorithm on Jump Functions
http://arxiv.org/abs/2006.03523
AUTHORS:        Denis Antipov ; Benjamin Doerr
COMMENTS:       An extended version of the same-titled paper from PPSN 2020
HIGHLIGHT:      To overcome this difficulty, we propose to choose two parameters of the $(1+(\lambda,\lambda))$ genetic algorithm randomly from a power-law distribution.

49, TITLE:      Unsupervised Translation of Programming Languages
http://arxiv.org/abs/2006.03511
AUTHORS:        Marie-Anne Lachaux ; Baptiste Roziere ; Lowik Chanussot ; Guillaume Lample
HIGHLIGHT:      In this paper, we propose to leverage recent approaches in unsupervised machine translation to train a fully unsupervised neural transcompiler.

50, TITLE:      MSDU-net: A Multi-Scale Dilated U-net for Blur Detection
http://arxiv.org/abs/2006.03182
AUTHORS:        Fan Yang ; Xiao Xiao
HIGHLIGHT:      In this work, we regard blur detection as an image segmentation problem.

51, TITLE:      Human or Machine: Automating Human Likeliness Evaluation of NLG Texts
http://arxiv.org/abs/2006.03189
AUTHORS:        Erion Çano ; Ondřej Bojar
COMMENTS:       9 pages, 5 equations, 1 table
HIGHLIGHT:      In this paper, we present an attempt to automate the human likeliness evaluation of the output text samples coming from natural language generation methods used to solve several tasks.

52, TITLE:      Pick-Object-Attack: Type-Specific Adversarial Attack for Object Detection
http://arxiv.org/abs/2006.03184
AUTHORS:        Omid Mohamad Nezami ; Akshay Chaturvedi ; Mark Dras ; Utpal Garain
HIGHLIGHT:      In this paper, we generate adversarial examples for object detection, which entails detecting bounding boxes around multiple objects present in the image and classifying them at the same time, making it a harder task than against image classification.

53, TITLE:      Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval
http://arxiv.org/abs/2006.03185
AUTHORS:        Limin Chen ; Zhiwen Tang ; Grace Hui Yang
COMMENTS:       Accepted by SIGIR 2020
HIGHLIGHT:      To successfully apply RL methods to IIR, one challenge is to obtain sufficient relevance labels to train the RL agents, which are infamously known as sample inefficient.

54, TITLE:      Hardness of Learning Neural Networks with Natural Weights
http://arxiv.org/abs/2006.03177
AUTHORS:        Amit Daniely ; Gal Vardi
HIGHLIGHT:      We prove negative results in this regard, and show that for depth-$2$ networks, and many "natural" weights distributions such as the normal and the uniform distribution, most networks are hard to learn.

55, TITLE:      Discovering Parametric Activation Functions
http://arxiv.org/abs/2006.03179
AUTHORS:        Garrett Bingham ; Risto Miikkulainen
COMMENTS:       11 pages, 6 figures/tables, under review
HIGHLIGHT:      This paper proposes a technique for customizing activation functions automatically, resulting in reliable improvements in performance.

56, TITLE:      Decomposable sparse polynomial systems
http://arxiv.org/abs/2006.03154
AUTHORS:        Taylor Brysiewicz ; Jose Israel Rodriguez ; Frank Sottile ; Thomas Yahl
COMMENTS:       7 pages, software available at https://www.math.tamu.edu/~thomasjyahl/research/DSS/DSSsite.html
HIGHLIGHT:      We describe the structure of decomposable sparse systems and explain how the methods in this package may be used to exploit this structure, with examples.

57, TITLE:      Single-machine scheduling with an external resource
http://arxiv.org/abs/2006.03399
AUTHORS:        Dirk Briskorn ; Morteza Davari ; Jannik Matuschke
HIGHLIGHT:      We provide a thorough complexity analysis (NP-hardness proofs and pseudo-polynomial algorithms) for different members of these three classes.

58, TITLE:      Unsupervised clustering of Roman pottery profiles from their SSAE representation
http://arxiv.org/abs/2006.03156
AUTHORS:        Simone Parisotto ; Alessandro Launaro ; Ninetta Leone ; Carola-Bibiane Schönlieb
COMMENTS:       18 pages, 10 figures
HIGHLIGHT:      In this paper we introduce the ROman COmmonware POTtery (ROCOPOT) database, which comprises of more than 2000 black and white imaging profiles of pottery shapes extracted from 11 Roman catalogues and related to different excavation sites.

59, TITLE:      Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels
http://arxiv.org/abs/2006.03394
AUTHORS:        Hans Pinckaers ; Wouter Bulten ; Jeroen van der Laak ; Geert Litjens
HIGHLIGHT:      In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet-34) with 21 million parameters end-to-end on 4712 prostate biopsies.

60, TITLE:      A New Method Towards Speech Files Local Features Investigation
http://arxiv.org/abs/2006.03388
AUTHORS:        Rustam Latypov ; Evgeni Stolov
HIGHLIGHT:      In this paper, we suggest a new approach to the exploration of such properties.

61, TITLE:      Structurally aware bidirectional unpaired image to image translation between CT and MR
http://arxiv.org/abs/2006.03374
AUTHORS:        Vismay Agrawal ; Avinash Kori ; Vikas Kumar Anand ; Ganapathy Krishnamurthi
COMMENTS:       9 pages, 4 figures
HIGHLIGHT:      In this manuscript, we have implemented two different variations of Generative Adversarial Networks exploiting the cycling consistency and structural similarity between both CT and MR image modalities on a pelvis dataset, thus facilitating a bidirectional exchange of content and style between these image modalities.

62, TITLE:      SIDU: Similarity Difference and Uniqueness Method for Explainable AI
http://arxiv.org/abs/2006.03122
AUTHORS:        Satya M. Muddamsetty ; Mohammad N. S. Jahromi ; Thomas B. Moeslund
COMMENTS:       Accepted manuscript in IEEE International Conference on Image Processing
HIGHLIGHT:      This paper presents a novel visual explanation method for deep learning networks in the form of a saliency map that can effectively localize entire object regions.

63, TITLE:      From Checking to Inference: Actual Causality Computations as Optimization Problems
http://arxiv.org/abs/2006.03363
AUTHORS:        Amjad Ibrahim ; Alexander Pretschner
HIGHLIGHT:      In this paper, we present a novel approach to formulate different notions of causal reasoning, over binary acyclic models, as optimization problems, based on quantifiable notions within counterfactual computations.

64, TITLE:      Learning to Rank Learning Curves
http://arxiv.org/abs/2006.03361
AUTHORS:        Martin Wistuba ; Tejaswini Pedapati
COMMENTS:       Accepted at the International Conference on Machine Learning (ICML) 2020
HIGHLIGHT:      In this work, we present a new method that saves computational budget by terminating poor configurations early on in the training.

65, TITLE:      Curiosity Killed the Cat and the Asymptotically Optimal Agent
http://arxiv.org/abs/2006.03357
AUTHORS:        Michael K. Cohen ; Marcus Hutter
COMMENTS:       8 pages, with 4 page appendix; 1 figure
HIGHLIGHT:      Rather than assuming away the problem, we present an agent with the modest guarantee of approaching the performance of a mentor, doing safe exploration instead of reckless exploration.

66, TITLE:      Topic Detection from Conversational Dialogue Corpus with Parallel Dirichlet Allocation Model and Elbow Method
http://arxiv.org/abs/2006.03353
AUTHORS:        Haider Khalid ; Vincent Wade
HIGHLIGHT:      In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique.

67, TITLE:      Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
http://arxiv.org/abs/2006.03354
AUTHORS:        Xingyi Song ; Johann Petrak ; Ye Jiang ; Iknoor Singh ; Diana Maynard ; Kalina Bontcheva
HIGHLIGHT:      This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework.

68, TITLE:      Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
http://arxiv.org/abs/2006.03349
AUTHORS:        Abdelrahman Eldesokey ; Michael Felsberg ; Karl Holmquist ; Mikael Persson
COMMENTS:       CVPR2020 (8 pages + supplementary)
HIGHLIGHT:      In this work, we thus focus on modeling the uncertainty of depth data in depth completion starting from the sparse noisy input all the way to the final prediction.

69, TITLE:      SMIE: Weakness is Power!: Auto-indentation with incomplete information
http://arxiv.org/abs/2006.03103
AUTHORS:        Stefan Monnier
HIGHLIGHT:      I present the design of Emacs's Simple-Minded Indentation Engine (SMIE), which gets its power from the weakness of the underlying parsing technique.

70, TITLE:      Explaining Autonomous Driving by Learning End-to-End Visual Attention
http://arxiv.org/abs/2006.03347
AUTHORS:        Luca Cultrera ; Lorenzo Seidenari ; Federico Becattini ; Pietro Pala ; Alberto Del Bimbo
COMMENTS:       accepted at CVPR20 Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD20)
HIGHLIGHT:      In this work we propose to train an imitation learning based agent equipped with an attention model.

71, TITLE:      XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
http://arxiv.org/abs/2006.03589
AUTHORS:        Thomas Schnake ; Oliver Eberle ; Jonas Lederer ; Shinichi Nakajima ; Kristof T. Schütt ; Klaus-Robert Müller ; Grégoire Montavon
COMMENTS:       12 pages + 12 pages supplement
HIGHLIGHT:      In this paper, we contribute by proposing a new XAI approach for GNNs.



==========Updates to Previous Papers==========
1, TITLE:       Multi-modal Self-Supervision from Generalized Data Transformations
http://arxiv.org/abs/2003.04298
AUTHORS:        Mandela Patrick ; Yuki M. Asano ; Polina Kuznetsova ; Ruth Fong ; João F. Henriques ; Geoffrey Zweig ; Andrea Vedaldi
HIGHLIGHT:      In this paper, we introduce the framework of Generalized Data Transformations to (1) reduce several recent self-supervised learning objectives to a single formulation for ease of comparison, analysis, and extension, (2) allow a choice between being invariant or distinctive to data transformations, obtaining different supervisory signals, and (3) derive the conditions that combinations of transformations must obey in order to lead to well-posed learning objectives.

2, TITLE:       Language Models are Few-Shot Learners
http://arxiv.org/abs/2005.14165
AUTHORS:        Tom B. Brown ; Benjamin Mann ; Nick Ryder ; Melanie Subbiah ; Jared Kaplan ; Prafulla Dhariwal ; Arvind Neelakantan ; Pranav Shyam ; Girish Sastry ; Amanda Askell ; Sandhini Agarwal ; Ariel Herbert-Voss ; Gretchen Krueger ; Tom Henighan ; Rewon Child ; Aditya Ramesh ; Daniel M. Ziegler ; Jeffrey Wu ; Clemens Winter ; Christopher Hesse ; Mark Chen ; Eric Sigler ; Mateusz Litwin ; Scott Gray ; Benjamin Chess ; Jack Clark ; Christopher Berner ; Sam McCandlish ; Alec Radford ; Ilya Sutskever ; Dario Amodei
COMMENTS:       40+32 pages
HIGHLIGHT:      Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

3, TITLE:       A simple protocol for verifiable delegation of quantum computation in one round
http://arxiv.org/abs/1711.09585
AUTHORS:        Alex B. Grilo
HIGHLIGHT:      In this work, we propose the first protocol where the client delegates her quantum computation to two servers in one-round of communication.

4, TITLE:       Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision Processes
http://arxiv.org/abs/1908.08526
AUTHORS:        Nathan Kallus ; Masatoshi Uehara
HIGHLIGHT:      We show existing OPE estimators may fail to be efficient in this setting.

5, TITLE:       Spiking Inception Module for Multi-layer Unsupervised Spiking Neural Networks
http://arxiv.org/abs/2001.10696
AUTHORS:        Mingyuan Meng ; Xingyu Yang ; Shanlin Xiao ; Zhiyi Yu
COMMENTS:       Extended from arXiv:2001.01680, 8 pages, 7 figures, 5 tables, accepted at International Joint Conference on Neural Networks (IJCNN) in 2020
HIGHLIGHT:      In this paper, we eased this limitation by: 1)We proposed a Spiking Inception (Sp-Inception) module, inspired by the Inception module in the Artificial Neural Network (ANN) literature.

6, TITLE:       Essentially Optimal Sparse Polynomial Multiplication
http://arxiv.org/abs/2001.11959
AUTHORS:        Pascal Giorgi ; Bruno Grenet ; Armelle Perret du Cray
COMMENTS:       12 pages
HIGHLIGHT:      We present a probabilistic algorithm to compute the product of two univariate sparse polynomials over a field with a number of bit operations that is quasi-linear in the size of the input and the output.

7, TITLE:       DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip (SoC) Resource Scheduling
http://arxiv.org/abs/2005.07666
AUTHORS:        Tegg Taekyong Sung ; Jeongsoo Ha ; Jeewoo Kim ; Alex Yahja ; Chae-Bong Sohn ; Bo Ryu
COMMENTS:       18 pages, Accepted by Electronics 2020
HIGHLIGHT:      In this paper, we~present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by a directed acyclic graph.

8, TITLE:       Experiments on Paraphrase Identification Using Quora Question Pairs Dataset
http://arxiv.org/abs/2006.02648
AUTHORS:        Andreas Chandra ; Ruben Stefanus
HIGHLIGHT:      We tried several methods and algorithms and different approach from previous works.

9, TITLE:       Cascade Cost Volume for High-Resolution Multi-View Stereo and Stereo Matching
http://arxiv.org/abs/1912.06378
AUTHORS:        Xiaodong Gu ; Zhiwen Fan ; Zuozhuo Dai ; Siyu Zhu ; Feitong Tan ; Ping Tan
COMMENTS:       Accepted by CVPR2020 Oral
HIGHLIGHT:      In this paper, we propose a both memory and time efficient cost volume formulation that is complementary to existing multi-view stereo and stereo matching approaches based on 3D cost volumes.

10, TITLE:      Ordered Functional Decision Diagrams
http://arxiv.org/abs/2003.09340
AUTHORS:        Joan Thibault ; Khalil Ghorbal
HIGHLIGHT:      Several BDD variants were designed to exploit special features of Boolean functions to achieve better compression rates.Deciding a priori which variant to use is as hard as constructing the diagrams themselves and the conversion between variants comes in general with a prohibitive cost.This observation leads naturally to a growing interest into when and how one can combine existing variants to benefit from their respective sweet spots.In this paper, we introduce a novel framework, termed $\lambda$ Decision Diagram ($\lambda$DD), that revisits BDD from a purely functional point of view.

11, TITLE:      Scaling MAP-Elites to Deep Neuroevolution
http://arxiv.org/abs/2003.01825
AUTHORS:        Cédric Colas ; Joost Huizinga ; Vashisht Madhavan ; Jeff Clune
COMMENTS:       Accepted to GECCO 2020
HIGHLIGHT:      In this paper, we propose to leverage the efficiency of Evolution Strategies (ES) to scale MAP-Elites to high-dimensional controllers parameterized by large neural networks.

12, TITLE:      What's Sex Got To Do With Fair Machine Learning?
http://arxiv.org/abs/2006.01770
AUTHORS:        Lily Hu ; Issa Kohler-Hausmann
COMMENTS:       11 pages, 5 figures, ACM Conference on Fairness, Accountability, and Transparency
HIGHLIGHT:      Causal models of sex propose two substantive claims: 1) There exists a feature, sex-on-its-own, that is an inherent trait of an individual that causally brings about social phenomena external to it in the world; and 2) the relations between sex and its effects can be modified in whichever ways and the former feature would still retain the meaning that sex has in our world.

13, TITLE:      Did JHotDraw Respect the Law of Good Style?: A deep dive into the nature of false positives of bad code smells
http://arxiv.org/abs/2002.06191
AUTHORS:        Daniel Speicher
HIGHLIGHT:      If we were convinced that we had found a false positive, we described the relationships with design ideas.

14, TITLE:      Reward-rational (implicit) choice: A unifying formalism for reward learning
http://arxiv.org/abs/2002.04833
AUTHORS:        Hong Jun Jeon ; Smitha Milli ; Anca D. Dragan
HIGHLIGHT:      Our key insight is that different types of behavior can be interpreted in a single unifying formalism - as a reward-rational choice that the human is making, often implicitly.

15, TITLE:      Pointwise Paraphrase Appraisal is Potentially Problematic
http://arxiv.org/abs/2005.11996
AUTHORS:        Hannah Chen ; Yangfeng Ji ; David Evans
COMMENTS:       ACL 2020 Student Research Workshop
HIGHLIGHT:      This pointwise-based evaluation method does not match well the objective of most real world applications, so the goal of our work is to understand how models which perform well under pointwise evaluation may fail in practice and find better methods for evaluating paraphrase identification models.

16, TITLE:      Disentangling Multiple Features in Video Sequences using Gaussian Processes in Variational Autoencoders
http://arxiv.org/abs/2001.02408
AUTHORS:        Sarthak Bhagat ; Shagun Uppal ; Vivian Yin ; Nengli Lim
HIGHLIGHT:      We introduce MGP-VAE, a variational autoencoder which uses Gaussian processes (GP) to model the latent space for the unsupervised learning of disentangled representations in video sequences.

17, TITLE:      Separating Variables in Bivariate Polynomial Ideals
http://arxiv.org/abs/2002.01541
AUTHORS:        Manfred Buchacher ; Manuel Kauers ; Gleb Pogudin
HIGHLIGHT:      We present an algorithm which for any given ideal $I\subseteq\mathbb{K} [x,y]$ finds all elements of $I$ that have the form $f(x) - g(y)$, i.e., all elements in which no monomial is a multiple of $xy$.

18, TITLE:      Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification
http://arxiv.org/abs/2006.00878
AUTHORS:        Hanrong Ye ; Hong Liu ; Fanyang Meng ; Xia Li
COMMENTS:       First Submission: April 2019
HIGHLIGHT:      As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors.

19, TITLE:      Parameter Sharing is Surprisingly Useful for Multi-Agent Deep Reinforcement Learning
http://arxiv.org/abs/2005.13625
AUTHORS:        Justin K Terry ; Nathaniel Grammel ; Ananth Hari ; Luis Santos
HIGHLIGHT:      We use the MAILP model to show that increasing training centralization arbitrarily mitigates the slowing of convergence due to nonstationarity.

20, TITLE:      Learning to Detect 3D Objects from Point Clouds in Real Time
http://arxiv.org/abs/2006.01250
AUTHORS:        Abhinav Sagar
COMMENTS:       13 pages
HIGHLIGHT:      In this paper, we present a combined architecture using dilated and transposed convolutional neural networks for accurate and efficient semantic image segmentation.

21, TITLE:      Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings
http://arxiv.org/abs/1907.04152
AUTHORS:        Adam Gabriel Dobrakowski ; Agnieszka Mykowiecka ; Małgorzata Marciniak ; Wojciech Jaworski ; Przemysław Biecek
HIGHLIGHT:      In this paper we show NLP methods and a unique corpus of documents to validate this claim.

22, TITLE:      Prototypical Contrastive Learning of Unsupervised Representations
http://arxiv.org/abs/2005.04966
AUTHORS:        Junnan Li ; Pan Zhou ; Caiming Xiong ; Richard Socher ; Steven C. H. Hoi
HIGHLIGHT:      This paper presents Prototypical Contrastive Learning (PCL), an unsupervised representation learning method that addresses the fundamental limitations of instance-wise contrastive learning.

23, TITLE:      A Report on the 2020 Sarcasm Detection Shared Task
http://arxiv.org/abs/2005.05814
AUTHORS:        Debanjan Ghosh ; Avijit Vajpayee ; Smaranda Muresan
COMMENTS:       2nd Workshop on Figurative Language Processing (FigLang2020) at ACL 2020
HIGHLIGHT:      We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020.

24, TITLE:      Quantum Lower Bounds for Approximate Counting via Laurent Polynomials
http://arxiv.org/abs/1904.08914
AUTHORS:        Scott Aaronson ; Robin Kothari ; William Kretschmer ; Justin Thaler
COMMENTS:       This paper subsumes preprints arXiv:1808.02420 and arXiv:1902.02398. v1: 43 pages. v2: Results strengthened. v3: Minor revisions and references to followup work. 50 pages, 3 figures. To appear in CCC 2020
HIGHLIGHT:      We study quantum algorithms that are given access to trusted and untrusted quantum witnesses.

25, TITLE:      Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning
http://arxiv.org/abs/1905.12127
AUTHORS:        Shariq Iqbal ; Fei Sha
HIGHLIGHT:      In this paper we propose an approach for learning how to dynamically select between proposed intrinsic reward types which consider not just what an individual agent has explored, but all agents, such that the agents can coordinate their exploration and maximize extrinsic returns.

26, TITLE:      Learning Architectures from an Extended Search Space for Language Modeling
http://arxiv.org/abs/2005.02593
AUTHORS:        Yinqiao Li ; Chi Hu ; Yuhao Zhang ; Nuo Xu ; Yufan Jiang ; Tong Xiao ; Jingbo Zhu ; Tongran Liu ; Changliang Li
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we extend the search space of NAS.

27, TITLE:      Learning Bayesian Networks that enable full propagation of evidence
http://arxiv.org/abs/2004.04571
AUTHORS:        Anthony Constantinou
HIGHLIGHT:      The paper presents a novel hybrid structure learning algorithm, called SaiyanH, that addresses this issue.

28, TITLE:      Trainable Activation Function in Image Classification
http://arxiv.org/abs/2004.13271
AUTHORS:        Zhaohe Liao
HIGHLIGHT:      In the current research of neural networks, the activation function is manually specified by human and not able to change themselves during training.

29, TITLE:      A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
http://arxiv.org/abs/1806.03517
AUTHORS:        Hanan Hindy ; David Brosset ; Ethan Bayne ; Amar Seeam ; Christos Tachtatzis ; Robert Atkinson ; Xavier Bellekens
COMMENTS:       28 Pages, 6 Figures
HIGHLIGHT:      The unique combination of the taxonomy and the analysis of the datasets provided in this manuscript aims to improve the creation of datasets and the collection of real-world data.

30, TITLE:      Affinity and Diversity: Quantifying Mechanisms of Data Augmentation
http://arxiv.org/abs/2002.08973
AUTHORS:        Raphael Gontijo-Lopes ; Sylvia J. Smullin ; Ekin D. Cubuk ; Ethan Dyer
COMMENTS:       10 pages, 7 figures
HIGHLIGHT:      To this end, we introduce interpretable and easy-to-compute measures: Affinity and Diversity.

31, TITLE:      Deep Generation of Face Images from Sketches
http://arxiv.org/abs/2006.01047
AUTHORS:        Shu-Yu Chen ; Wanchao Su ; Lin Gao ; Shihong Xia ; Hongbo Fu
COMMENTS:       Accepted to Siggraph 2020
HIGHLIGHT:      To address this issue, our key idea is to implicitly model the shape space of plausible face images and synthesize a face image in this space to approximate an input sketch.

32, TITLE:      CNN Denoisers as Non-Local Filters: The Neural Tangent Denoiser
http://arxiv.org/abs/2006.02379
AUTHORS:        Julián Tachella ; Junqi Tang ; Mike Davies
HIGHLIGHT:      We introduce a novel interpretation of denoising networks with no clean training data in the context of the neural tangent kernel (NTK), elucidating the strong links with well-known non-local filtering techniques, such as non-local means or BM3D.

33, TITLE:      Deep backward schemes for high-dimensional nonlinear PDEs
http://arxiv.org/abs/1902.01599
AUTHORS:        Côme Huré ; Huyên Pham ; Xavier Warin
COMMENTS:       34 pages
HIGHLIGHT:      We propose new machine learning schemes for solving high dimensional nonlinear partial differential equations (PDEs).

34, TITLE:      Forecasting with time series imaging
http://arxiv.org/abs/1904.08064
AUTHORS:        Xixi Li ; Yanfei Kang ; Feng Li
HIGHLIGHT:      In this paper, we introduce an automated approach to extract time series features based on time series imaging.

35, TITLE:      Outlier Exposure with Confidence Control for Out-of-Distribution Detection
http://arxiv.org/abs/1906.03509
AUTHORS:        Aristotelis-Angelos Papadopoulos ; Mohammad Reza Rajati ; Nazim Shaikh ; Jiamian Wang
COMMENTS:       PyTorch code available at https://github.com/nazim1021/OOD-detection-using-OECC. This paper supersedes arXiv:1912.03133
HIGHLIGHT:      In this work, we propose a methodology for training a neural network that allows it to efficiently detect out-of-distribution (OOD) examples without compromising much of its classification accuracy on the test examples from known classes.

36, TITLE:      Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
http://arxiv.org/abs/1912.05170
AUTHORS:        Görkem Algan ; Ilkay Ulusoy
HIGHLIGHT:      This paper aims to present these algorithms while categorizing them into one of the two subgroups: noise model based and noise model free methods.

37, TITLE:      Neural Power Units
http://arxiv.org/abs/2006.01681
AUTHORS:        Niklas Heim ; Tomáš Pevný ; Václav Šmídl
HIGHLIGHT:      We introduce the Neural Power Unit (NPU) that operates on the full domain of real numbers and is capable of learning arbitrary power functions in a single layer.

38, TITLE:      Efficient Deployment of Conversational Natural Language Interfaces over Databases
http://arxiv.org/abs/2006.00591
AUTHORS:        Anthony Colas ; Trung Bui ; Franck Dernoncourt ; Moumita Sinha ; Doo Soon Kim
COMMENTS:       Accepted at ACL-NLI 2020
HIGHLIGHT:      In this work, we propose a novel method for accelerating the training dataset collection for developing the natural language-to-query-language machine learning models.

39, TITLE:      On the Weisfeiler-Leman Dimension of Fractional Packing
http://arxiv.org/abs/1910.11325
AUTHORS:        V. Arvind ; Frank Fuhlbrück ; Johannes Köbler ; Oleg Verbitsky
COMMENTS:       26 pages, 1 figure, major revision of the previous version
HIGHLIGHT:      The $k$-dimensional Weisfeiler-Leman procedure ($k$-WL), which colors $k$-tuples of vertices in rounds based on the neighborhood structure in the graph, has proven to be immensely fruitful in the algorithmic study of Graph Isomorphism.

40, TITLE:      ISL: A novel approach for deep exploration
http://arxiv.org/abs/1909.06293
AUTHORS:        Lucas Cassano ; Ali H. Sayed
HIGHLIGHT:      In this article we explore an alternative approach to address deep exploration and we introduce the ISL algorithm, which is efficient at performing deep exploration.

41, TITLE:      Preliminary Forensics Analysis of DeepFake Images
http://arxiv.org/abs/2004.12626
AUTHORS:        Luca Guarnera ; Oliver Giudice ; Cristina Nastasi ; Sebastiano Battiato
HIGHLIGHT:      This paper will present a brief overview of technologies able to produce DeepFake images of faces.

42, TITLE:      Bidirectional Generative Modeling Using Adversarial Gradient Estimation
http://arxiv.org/abs/2002.09161
AUTHORS:        Xinwei Shen ; Tong Zhang ; Kani Chen
HIGHLIGHT:      We present a new optimization method for this formulation, where the gradient is computed using an adversarially learned discriminator.

43, TITLE:      Problems of dataset creation for light source estimation
http://arxiv.org/abs/2006.02692
AUTHORS:        E. I. Ershov ; A. V. Belokopytov ; A. V. Savchik
HIGHLIGHT:      The paper describes our experience collecting a new dataset for the light source estimation problem in a single image.

44, TITLE:      INSET: Sentence Infilling with INter-SEntential Transformer
http://arxiv.org/abs/1911.03892
AUTHORS:        Yichen Huang ; Yizhe Zhang ; Oussama Elachqar ; Yu Cheng
COMMENTS:       Y.H. and Y.Z. contributed equally to this work. v2: published version with updated results and references
HIGHLIGHT:      In this paper, we propose a framework to decouple the challenge and address these three aspects respectively, leveraging the power of existing large-scale pre-trained models such as BERT and GPT-2.

45, TITLE:      Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
http://arxiv.org/abs/1911.06904
AUTHORS:        Maxwell Crouse ; Ibrahim Abdelaziz ; Cristina Cornelio ; Veronika Thost ; Lingfei Wu ; Kenneth Forbus ; Achille Fokoue
HIGHLIGHT:      In this work we propose a novel approach for embedding logical formulae that is designed to overcome the representational limitations of prior approaches.

46, TITLE:      Learning Local Neighboring Structure for Robust 3D Shape Representation
http://arxiv.org/abs/2004.09995
AUTHORS:        Zhongpai Gao ; Guangtao Zhai ; Juyong Zhang ; Junchi Yan ; Yiyan Yang ; Xiaokang Yang
HIGHLIGHT:      In this paper, we propose a local structure-aware anisotropic convolutional operation (LSA-Conv) that learns adaptive weighting matrices for each node according to the local neighboring structure and performs shared anisotropic filters.

47, TITLE:      Mapping Natural Language Instructions to Mobile UI Action Sequences
http://arxiv.org/abs/2005.03776
AUTHORS:        Yang Li ; Jiacong He ; Xin Zhou ; Yuan Zhang ; Jason Baldridge
COMMENTS:       Annual Conference of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it.

48, TITLE:      Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
http://arxiv.org/abs/2005.14684
AUTHORS:        Fei Shen ; Jianqing Zhu ; Xiaobin Zhu ; Yi Xie ; Jingchang Huang
HIGHLIGHT:      In this paper, firstly, an innovative spatial graph network (SGN) is proposed to elaborately explore the spatial significance of feature maps.

49, TITLE:      Cascaded Refinement Network for Point Cloud Completion
http://arxiv.org/abs/2004.03327
AUTHORS:        Xiaogang Wang ; Marcelo H Ang Jr ; Gim Hee Lee
COMMENTS:       CVPR2020
HIGHLIGHT:      To this end, we propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes.

50, TITLE:      Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
http://arxiv.org/abs/2005.14464
AUTHORS:        Xiaoya Li ; Mingxin Zhou ; Jiawei Wu ; Arianna Yuan ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we perform a comprehensive analysis on the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020.

51, TITLE:      Permutation Matters: Anisotropic Convolutional Layer for Learning on Point Clouds
http://arxiv.org/abs/2005.13135
AUTHORS:        Zhongpai Gao ; Guangtao Zhai ; Junchi Yan ; Xiaokang Yang
HIGHLIGHT:      In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that calculates soft-permutation matrices for each point using dot-product attention according to a set of evenly distributed kernel points on a sphere's surface and performs shared anisotropic filters.

52, TITLE:      On Mutual Information in Contrastive Learning for Visual Representations
http://arxiv.org/abs/2005.13149
AUTHORS:        Mike Wu ; Chengxu Zhuang ; Milan Mosse ; Daniel Yamins ; Noah Goodman
COMMENTS:       8 pages content; 15 pages supplement with proofs
HIGHLIGHT:      In practice, our new objectives yield representations that outperform those learned with previous approaches for transfer to classification, bounding box detection, instance segmentation, and keypoint detection.

53, TITLE:      BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
http://arxiv.org/abs/2006.01174
AUTHORS:        Maria de la Iglesia Vayá ; Jose Manuel Saborit ; Joaquim Angel Montell ; Antonio Pertusa ; Aurelia Bustos ; Miguel Cazorla ; Joaquin Galant ; Xavier Barber ; Domingo Orozco-Beltrán ; Francisco García-García ; Marisa Caparrós ; Germán González ; Jose María Salinas
HIGHLIGHT:      This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests.