2020.06.02.txt

==========New Papers==========
1, TITLE:       EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces
http://arxiv.org/abs/2006.00622
AUTHORS:        Thorir Mar Ingolfsson ; Michael Hersche ; Xiaying Wang ; Nobuaki Kobayashi ; Lukas Cavigelli ; Luca Benini
COMMENTS:       8 pages, 6 figures, 5 tables
HIGHLIGHT:      In this paper, we propose EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters.

2, TITLE:       In the Eye of the Beholder: Gaze and Actions in First Person Video
http://arxiv.org/abs/2006.00626
AUTHORS:        Yin Li ; Miao Liu ; James M. Rehg
COMMENTS:       Submitted to TPAMI
HIGHLIGHT:      Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV. To facilitate our research, we first introduce the EGTEA Gaze+ dataset.

3, TITLE:       Neural Networks with Small Weights and Depth-Separation Barriers
http://arxiv.org/abs/2006.00625
AUTHORS:        Gal Vardi ; Ohad Shamir
HIGHLIGHT:      In this paper, we focus on feedforward ReLU networks, and prove fundamental barriers to proving such results beyond depths $4$, by reduction to open problems and natural-proof barriers in circuit complexity.

4, TITLE:       Applying support vector data description for fraud detection
http://arxiv.org/abs/2006.00618
AUTHORS:        Mohamad Khedmati ; Masoud Erfani ; Mohammad GhasemiGol
HIGHLIGHT:      In order to deal with this challenge, we apply one-class classification methods such as SVDD which does not need the fraud samples for training.

5, TITLE:       Limited-angle CT reconstruction via the L1/L2 minimization
http://arxiv.org/abs/2006.00601
AUTHORS:        Chao Wang ; Min Tao ; James Nagy ; Yifei Lou
COMMENTS:       29 pages
HIGHLIGHT:      In this paper, we consider minimizing the L1/L2 term on the gradient for a limit-angle scanning problem in computed tomography (CT) reconstruction.

6, TITLE:       CNRL at SemEval-2020 Task 5: Modelling Causal Reasoning in Language with Multi-Head Self-Attention Weights based Counterfactual Detection
http://arxiv.org/abs/2006.00609
AUTHORS:        Rajaswa Patil ; Veeky Baths
COMMENTS:       Submitted at SemEval-2020 workshop
HIGHLIGHT:      In this paper, we describe an approach for modelling causal reasoning in natural language by detecting counterfactuals in text using multi-head self-attention weights.

7, TITLE:       LRG at SemEval-2020 Task 7: Assessing the Ability of BERT and Derivative Models to Perform Short-Edits based Humor Grading
http://arxiv.org/abs/2006.00607
AUTHORS:        Siddhant Mahurkar ; Rajaswa Patil
COMMENTS:       Submitted at SemEval-2020 workshop
HIGHLIGHT:      In this paper, we assess the ability of BERT and its derivative models (RoBERTa, DistilBERT, and ALBERT) for short-edits based humor grading.

8, TITLE:       Self-adaptive Re-weighted Adversarial Domain Adaptation
http://arxiv.org/abs/2006.00223
AUTHORS:        Shanshan Wang ; Lei Zhang
HIGHLIGHT:      To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution.

9, TITLE:       Web page classification with Google Image Search results
http://arxiv.org/abs/2006.00226
AUTHORS:        Fahri Aydos ; A. Murat Özbayoğlu ; Yahya Şirin ; M. Fatih Demirci
HIGHLIGHT:      In this paper, we introduce a novel method that combines multiple neural network results to decide the class of the input.

10, TITLE:      Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
http://arxiv.org/abs/2006.00697
AUTHORS:        Patricio Cerda-Mardini ; Vladimir Araujo ; Alvaro Soto
HIGHLIGHT:      We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation.

11, TITLE:      Complex Sequential Understanding through the Awareness of Spatial and Temporal Concepts
http://arxiv.org/abs/2006.00212
AUTHORS:        Bo Pang ; Kaiwen Zha ; Hanwen Cao ; Jiajun Tang ; Minghui Yu ; Cewu Lu
COMMENTS:       15 pages, 5 figures, 8 tables
HIGHLIGHT:      Here, we introduce a new modeling strategy called Semi-Coupled Structure (SCS), which consists of deep neural networks that decouple the complex spatial and temporal concepts learning.

12, TITLE:      A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
http://arxiv.org/abs/2006.00210
AUTHORS:        Bharathi Raja Chakravarthi ; Navya Jose ; Shardul Suryawanshi ; Elizabeth Sherly ; John P. McCrae
HIGHLIGHT:      This paper presents a new gold standard corpus for sentiment analysis of code-mixed text in Malayalam-English annotated by voluntary annotators.

13, TITLE:      Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance in Twitter Conversations
http://arxiv.org/abs/2006.00691
AUTHORS:        Ramon Villa-Cox ; Sumeet Kumar ; Matthew Babcock ; Kathleen M. Carley
HIGHLIGHT:      In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues.

14, TITLE:      Attention-Guided Discriminative Region Localization for Bone Age Assessment
http://arxiv.org/abs/2006.00202
AUTHORS:        Chao Chen ; Zhihong Chen1 ; Xinyu Jin ; Lanjuan Li ; William Speier ; Corey W. Arnold2
COMMENTS:       codes are available at \url{https://github.com/chenchao666/Bone-Age-Assessment}
HIGHLIGHT:      In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra annotations.

15, TITLE:      Symbol Spotting on Digital Architectural Floor Plans Using a Deep Learning-based Framework
http://arxiv.org/abs/2006.00684
AUTHORS:        Alireza Rezvanifar ; Melissa Cote ; Alexandra Branzan Albu
COMMENTS:       Accepted to CVPR2020 Workshop on Text and Documents in the Deep Learning Era
HIGHLIGHT:      In this paper, we address all of the above issues by leveraging recent advances in DL and adapting an object detection framework based on the You-Only-Look-Once (YOLO) architecture.

16, TITLE:      Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text
http://arxiv.org/abs/2006.00206
AUTHORS:        Bharathi Raja Chakravarthi ; Vigneshwaran Muralidaran ; Ruba Priyadharshini ; John P. McCrae
HIGHLIGHT:      In this paper, we describe the process of creating the corpus and assigning polarities. To overcome this, we created a gold standard Tamil-English code-switched, sentiment-annotated corpus containing 15,744 comment posts from YouTube.

17, TITLE:      Conversational Machine Comprehension: a Literature Review
http://arxiv.org/abs/2006.00671
AUTHORS:        Somil Gupta ; Bhanu Pratap Singh Rawat
HIGHLIGHT:      It focuses on synthesizing a generic framework for CMC models, rather than describing the models individually.

18, TITLE:      A multimodal approach for multi-label movie genre classification
http://arxiv.org/abs/2006.00654
AUTHORS:        Rafael B. Mangolin ; Rodolfo M. Pereira ; Alceu S. Britto Jr. ; Carlos N. Silla Jr. ; Valéria D. Feltrim ; Diego Bertolini ; Yandre M. G. Costa
COMMENTS:       21 pages and 4 figures
HIGHLIGHT:      In this paper, we addressed the multi-label classification of the movie genres in a multimodal way. For this purpose, we created a dataset composed of trailer video clips, subtitles, synopses, and movie posters taken from 152,622 movie titles from The Movie Database.

19, TITLE:      Automatic Building and Labeling of HD Maps with Deep Learning
http://arxiv.org/abs/2006.00644
AUTHORS:        Mahdi Elhousni ; Yecheng Lyu ; Ziming Zhang ; Xinming Huang
COMMENTS:       Accepted by IAAI2020
HIGHLIGHT:      In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data.

20, TITLE:      Review on 3D Lidar Localization for Autonomous Driving Cars
http://arxiv.org/abs/2006.00648
AUTHORS:        Mahdi Elhousni ; Xinming Huang
COMMENTS:       Accepted by IV2020
HIGHLIGHT:      In this paper, we review the latest finding in 3D LIDAR localization for autonomous driving cars, and analyze the results obtained by each method, in an effort to guide the research community towards the path that seems to be the most promising.

21, TITLE:      A Unified Feature Representation for Lexical Connotations
http://arxiv.org/abs/2006.00635
AUTHORS:        Emily Allaway ; Kathleen McKeown
HIGHLIGHT:      In this paper, we use distant labeling to create a new lexical resource representing connotation aspects for nouns and adjectives.

22, TITLE:      Neural Unsupervised Domain Adaptation in NLP---A Survey
http://arxiv.org/abs/2006.00632
AUTHORS:        Alan Ramponi ; Barbara Plank
HIGHLIGHT:      Motivated by the latest advances, in this survey we review neural unsupervised domain adaptation techniques which do not require labeled target domain data.

23, TITLE:      OPAL-Net: A Generative Model for Part-based Object Layout Generation
http://arxiv.org/abs/2006.00190
AUTHORS:        Rishabh Baghel ; Ravi Kiran Sarvadevabhatla
COMMENTS:       Code repository at https://github.com/atmacvit/opalnet
HIGHLIGHT:      We propose OPAL-Net, a novel hierarchical architecture for part-based layout generation of objects from multiple categories using a single unified model.

24, TITLE:      Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction
http://arxiv.org/abs/2006.00197
AUTHORS:        J. D. Bodapati ; N. Veeranjaneyulu ; S. N. Shareef ; S. Hakak ; M. Bilal ; P. K. R. Maddikunta ; O. Jo
COMMENTS:       18 pages, 8 figures, published in Electronics MDPI journal
HIGHLIGHT:      With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction.

25, TITLE:      MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement Learning
http://arxiv.org/abs/2006.00195
AUTHORS:        Parvin Malekzadeh ; Mohammad Salimibeni ; Arash Mohammadi ; Akbar Assa ; Konstantinos N. Plataniotis
HIGHLIGHT:      The main objective of this paper is to address this issue.

26, TITLE:      DocBank: A Benchmark Dataset for Document Layout Analysis
http://arxiv.org/abs/2006.01038
AUTHORS:        Minghao Li ; Yiheng Xu ; Lei Cui ; Shaohan Huang ; Furu Wei ; Zhoujun Li ; Ming Zhou
COMMENTS:       Work in progress
HIGHLIGHT:      In this paper, we present \textbf{DocBank}, a benchmark dataset with fine-grained token-level annotations for document layout analysis. We build several strong baselines and manually split train/dev/test sets for evaluation.

27, TITLE:      A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
http://arxiv.org/abs/2006.01031
AUTHORS:        Valentin Peretroukhin ; Matthew Giamou ; David M. Rosen ; W. Nicholas Greene ; Nicholas Roy ; Jonathan Kelly
COMMENTS:       To appear in the proceedings of Robotics: Science and Systems (RSS'20), Boston, Massachusetts, USA, Jul. 12-16, 2020
HIGHLIGHT:      In this work, we present a novel symmetric matrix representation of the 3D rotation group, SO(3), with two important properties that make it particularly suitable for learned models: (1) it satisfies a smoothness property that improves convergence and generalization when regressing large rotation targets, and (2) it encodes a symmetric Bingham belief over the space of unit quaternions, permitting the training of uncertainty-aware models.

28, TITLE:      An Efficient Planar Bundle Adjustment Algorithm
http://arxiv.org/abs/2006.00187
AUTHORS:        Lipu Zhou ; Daniel Koppel ; Hui Ju ; Frank Steinbruecker ; Michael Kaess
HIGHLIGHT:      This paper presents an efficient algorithm for the least-squares problem using the point-to-plane cost, which aims to jointly optimize depth sensor poses and plane parameters for 3D reconstruction.

29, TITLE:      Advanced Single Image Resolution Upsurging Using a Generative Adversarial Network
http://arxiv.org/abs/2006.00186
AUTHORS:        Md. Moshiur Rahman ; Samrat Kumar Dey ; Kabid Hassan Shibly
COMMENTS:       10 pages, 4 figures, 1 Table
HIGHLIGHT:      In this paper, we have proposed a technique of generating higher resolution images form lower resolution using Residual in Residual Dense Block network architecture with a deep network.

30, TITLE:      User Memory Reasoning for Conversational Recommendation
http://arxiv.org/abs/2006.00184
AUTHORS:        Hu Xu ; Seungwhan Moon ; Honglei Liu ; Bing Liu ; Pararth Shah ; Bing Liu ; Philip S. Yu
HIGHLIGHT:      We propose a simple yet expandable formulation for constructing and updating the MG, and a reasoning model that predicts optimal dialog policies and recommendation items in unconstrained graph space.

31, TITLE:      GoodPoint: unsupervised learning of keypoint detection and description
http://arxiv.org/abs/2006.01030
AUTHORS:        Anatoly Belikov ; Alexey Potapov
HIGHLIGHT:      This paper introduces a new algorithm for unsupervised learning of keypoint detectors and descriptors, which demonstrates fast convergence and good performance across different datasets.

32, TITLE:      MetaInv-Net: Meta Inversion Network for Sparse View CT Image Reconstruction
http://arxiv.org/abs/2006.00171
AUTHORS:        Haimiao Zhang ; Baodong Liu ; Hengyong Yu ; Bin Dong
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm.

33, TITLE:      A novel approach for multi-agent cooperative pursuit to capture grouped evaders
http://arxiv.org/abs/2006.01022
AUTHORS:        Muhammad Zuhair Qadir ; Songhao Piao ; Haiyang Jiang ; Mohammed El Habib Souidi
COMMENTS:       published paper's draft version
HIGHLIGHT:      An approach of mobile multi-agent pursuit based on application of self-organizing feature map (SOFM) and along with that reinforcement learning based on agent group role membership function (AGRMF) model is proposed.

34, TITLE:      Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)
http://arxiv.org/abs/2006.01023
AUTHORS:        Jie Sun ; Abd AlRahman AlMomani ; Erik Bollt
COMMENTS:       18 pages, 6 Figures
HIGHLIGHT:      In this paper we develop a new information theoretic methodology that we show to be significantly more efficient than previous approaches.

35, TITLE:      When2com: Multi-Agent Perception via Communication Graph Grouping
http://arxiv.org/abs/2006.00176
AUTHORS:        Yen-Cheng Liu ; Junjiao Tian ; Nathaniel Glaser ; Zsolt Kira
COMMENTS:       Accepted to ICRA 2020; for the project page, see https://ycliu93.github.io/projects/multi-agent-perception.html
HIGHLIGHT:      In this paper, we address the collaborative perception problem, where one agent is required to perform a perception task and can communicate and share information with other agents on the same task.

36, TITLE:      Retrieval of Family Members Using Siamese Neural Network
http://arxiv.org/abs/2006.00174
AUTHORS:        Jun Yu ; Guochen Xie ; Mengyan Li ; Xinlong Hao
COMMENTS:       5 pages, 3 figures
HIGHLIGHT:      To solve this problem, we propose our solution with deep Siamese neural network.

37, TITLE:      PlenoptiSign: an optical design tool for plenoptic imaging
http://arxiv.org/abs/2006.01015
AUTHORS:        Christopher Hahne ; Amar Aggoun
COMMENTS:       https://github.com/hahnec/plenoptisign/
HIGHLIGHT:      This paper presents PlenoptiSign, which implements these findings as a Python software package to help assist in an experimental or prototyping stage of a plenoptic system.

38, TITLE:      Probing Emergent Semantics in Predictive Agents via Question Answering
http://arxiv.org/abs/2006.01016
AUTHORS:        Abhishek Das ; Federico Carnevale ; Hamza Merzic ; Laura Rimell ; Rosalia Schneider ; Josh Abramson ; Alden Hung ; Arun Ahuja ; Stephen Clark ; Gregory Wayne ; Felix Hill
COMMENTS:       ICML 2020
HIGHLIGHT:      We propose question-answering as a general paradigm to decode and understand the representations that such agents develop, applying our method to two recent approaches to predictive modeling -action-conditional CPC (Guo et al., 2018) and SimCore (Gregor et al., 2019).

39, TITLE:      Semi-supervised deep learning for high-dimensional uncertainty quantification
http://arxiv.org/abs/2006.01010
AUTHORS:        Zequn Wang ; Mingyang Li
HIGHLIGHT:      This paper presents a semi-supervised learning framework for dimension reduction and reliability analysis.

40, TITLE:      Computing Plan-Length Bounds Using Lengths of Longest Paths
http://arxiv.org/abs/2006.01011
AUTHORS:        Mohammad Abdulaziz ; Dominik Berger
HIGHLIGHT:      We devise a method to exactly compute the length of the longest simple path in factored state spaces, like state spaces encountered in classical planning.

41, TITLE:      Joint Person Objectness and Repulsion for Person Search
http://arxiv.org/abs/2006.00155
AUTHORS:        Hantao Yao ; Changsheng Xu
HIGHLIGHT:      In this paper, we propose an OR similarity by jointly considering the objectness and repulsion information.

42, TITLE:      Challenge report: Recognizing Families In the Wild Data Challenge
http://arxiv.org/abs/2006.00154
AUTHORS:        Zhipeng Luo ; Zhiguang Zhang ; Zhenyu Xu ; Lixuan Che
COMMENTS:       RFIW,IEEE FG2020
HIGHLIGHT:      In this paper, we studied previous methods and proposed our method.

43, TITLE:      Topic Detection and Summarization of User Reviews
http://arxiv.org/abs/2006.00148
AUTHORS:        Pengyuan Li ; Lei Huang ; Guang-jie Ren
HIGHLIGHT:      As the sentiments are typically short, we combine sentiments talking about the same aspect into a single document and apply topic modeling method to identify hidden topics among customer reviews and summaries.

44, TITLE:      Deep Fusion Siamese Network for Automatic Kinship Verification
http://arxiv.org/abs/2006.00143
AUTHORS:        Jun Yu ; Mengyan Li ; Xinlong Hao ; Guochen Xie
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      In this work, the challenging problem is progressively addressed in two respects.

45, TITLE:      Classical and Quantum Data Interaction in Programming Languages: A Runtime Architecture
http://arxiv.org/abs/2006.00131
AUTHORS:        Evandro Chagas Ribeiro da Rosa ; Rafael de Santiago
HIGHLIGHT:      We propose a runtime architecture that can be used in the development of a quantum programming language and its programming environment.

46, TITLE:      Design and Implementation of a Virtual 3D Educational Environment to improve Deaf Education
http://arxiv.org/abs/2006.00114
AUTHORS:        Abdelaziz Lakhfif
COMMENTS:       Proceedings of the 7th International Symposium ISKO-Maghreb Knowledge Organization in the Perspective of Digital Humanities: Research & Applications November 25th & 26th, 2018, pp. 201-205, Bejaia, Algeria
HIGHLIGHT:      The goal of the project is the development of a machine translation system from Arabic to Algerian Sign Language that can be used as educational tool for Deaf children in algerian primary schools.

47, TITLE:      A frame semantics based approach to comparative study of digitized corpus
http://arxiv.org/abs/2006.00113
AUTHORS:        Abdelaziz Lakhfif ; Mohamed Tayeb Laskri
COMMENTS:       Proceedings of the 7th International Symposium ISKO-Maghreb Knowledge Organization in the Perspective of Digital Humanities: Research & Applications November 25th & 26th, 2018, pp. 217-223, Bejaia, Algeria
HIGHLIGHT:      in this paper, we present a corpus linguistics based approach applied to analyzing digitized classical multilingual novels and narrative texts, from a semantic point of view.

48, TITLE:      Approximating the Ideal Observer for joint signal detection and localization tasks by use of supervised learning methods
http://arxiv.org/abs/2006.00112
AUTHORS:        Weimin Zhou ; Hua Li ; Mark A. Anastasio
COMMENTS:       Submitted to IEEE Transactions on Medical Imaging
HIGHLIGHT:      In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored.

49, TITLE:      Overview of Scanner Invariant Representations
http://arxiv.org/abs/2006.00115
AUTHORS:        Daniel Moyer ; Greg Ver Steeg ; Paul M. Thompson
COMMENTS:       Accepted as a short paper in MIDL 2020. In accordance with the MIDL 2020 Call for Papers, this short paper is an overview of an already published work arXiv:1904.05375, and was submitted to MIDL in order to allow presentation and discussion at the meeting
HIGHLIGHT:      In the present abstract we provide an overview of this method.

50, TITLE:      ExplainIt: Explainable Review Summarization with Opinion Causality Graphs
http://arxiv.org/abs/2006.00119
AUTHORS:        Nofar Carmeli ; Xiaolan Wang ; Yoshihiko Suhara ; Stefanos Angelidis ; Yuliang Li ; Jinfeng Li ; Wang-Chiew Tan
HIGHLIGHT:      In this paper, we present the system's individual components and evaluate their effectiveness on their respective sub-tasks, where we report substantial improvements over baselines across two domains.

51, TITLE:      Emergence of Separable Manifolds in Deep Language Representations
http://arxiv.org/abs/2006.01095
AUTHORS:        Jonathan Mamou ; Hang Le ; Miguel Del Rio ; Cory Stephenson ; Hanlin Tang ; Yoon Kim ; SueYeon Chung
COMMENTS:       8 pages. 8 figures. Accepted to ICML 2020
HIGHLIGHT:      In this work, we utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience, to analyze the high dimensional geometry of language representations from large-scale contextual embedding models.

52, TITLE:      Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
http://arxiv.org/abs/2006.01096
AUTHORS:        Anoopkumar Sonar ; Vincent Pacelli ; Anirudha Majumdar
COMMENTS:       11 pages, 3 figures
HIGHLIGHT:      In this paper, we approach this challenge through the following invariance principle: an agent must find a representation such that there exists an action-predictor built on top of this representation that is simultaneously optimal across all training domains.

53, TITLE:      Is 42 the Answer to Everything in Subtitling-oriented Speech Translation?
http://arxiv.org/abs/2006.01080
AUTHORS:        Alina Karakanta ; Matteo Negri ; Marco Turchi
COMMENTS:       Accepted at IWSLT 2020
HIGHLIGHT:      In this work, we explore two methods for applying Speech Translation (ST) to subtitling: a) a direct end-to-end and b) a classical cascade approach.

54, TITLE:      Aligning Faithful Interpretations with their Social Attribution
http://arxiv.org/abs/2006.01067
AUTHORS:        Alon Jacovi ; Yoav Goldberg
COMMENTS:       12 pages
HIGHLIGHT:      We re-formulate faithfulness as an accurate attribution of causality to the model, and introduce the concept of "aligned faithfulness": faithful causal chains that are aligned with their expected social behavior.

55, TITLE:      DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera
http://arxiv.org/abs/2006.01053
AUTHORS:        David Fuentes-Jimenez ; Roberto Martin-Lopez ; Cristina Losada-Gutierrez ; David Casillas-Perez ; Javier Macias-Guarasa ; Daniel Pizarro ; Carlos A. Luna
HIGHLIGHT:      In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability.

56, TITLE:      Explanations of Black-Box Model Predictions by Contextual Importance and Utility
http://arxiv.org/abs/2006.00199
AUTHORS:        Sule Anjomshoae ; Kary Främling ; Amro Najjar
HIGHLIGHT:      In this work, we present the Contextual Importance (CI) and Contextual Utility (CU) concepts to extract explanations that are easily understandable by experts as well as novice users.

57, 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.

58, TITLE:      Quantum Accelerated Estimation of Algorithmic Information
http://arxiv.org/abs/2006.00987
AUTHORS:        Aritra Sarkar ; Zaid Al-Ars ; Koen Bertels
COMMENTS:       31 pages, pre-print
HIGHLIGHT:      In this research we present a quantum circuit for estimating algorithmic information metrics like the universal prior distribution.

59, TITLE:      An Online Platform for Automatic Skull Defect Restoration and Cranial Implant Design
http://arxiv.org/abs/2006.00980
AUTHORS:        Jianning Li ; Antonio Pepe ; Christina Gsaxner ; Jan Egger
COMMENTS:       5 pages
HIGHLIGHT:      We introduce a fully automatic system for cranial implant design, a common task in cranioplasty operations.

60, TITLE:      Attention Word Embedding
http://arxiv.org/abs/2006.00988
AUTHORS:        Shashank Sonkar ; Andrew E. Waters ; Richard G. Baraniuk
HIGHLIGHT:      We tackle this inefficiency by introducing the Attention Word Embedding (AWE) model, which integrates the attention mechanism into the CBOW model.

61, TITLE:      Acme: A Research Framework for Distributed Reinforcement Learning
http://arxiv.org/abs/2006.00979
AUTHORS:        Matt Hoffman ; Bobak Shahriari ; John Aslanides ; Gabriel Barth-Maron ; Feryal Behbahani ; Tamara Norman ; Abbas Abdolmaleki ; Albin Cassirer ; Fan Yang ; Kate Baumli ; Sarah Henderson ; Alex Novikov ; Sergio Gómez Colmenarejo ; Serkan Cabi ; Caglar Gulcehre ; Tom Le Paine ; Andrew Cowie ; Ziyu Wang ; Bilal Piot ; Nando de Freitas
HIGHLIGHT:      In this work we introduce the major design decisions behind Acme and show how these are used to construct these baselines.

62, TITLE:      One Versus all for deep Neural Network Incertitude (OVNNI) quantification
http://arxiv.org/abs/2006.00954
AUTHORS:        Gianni Franchi ; Andrei Bursuc ; Emanuel Aldea ; Severine Dubuisson ; Isabelle Bloch
HIGHLIGHT:      In this work, we propose a new technique to quantify the epistemic uncertainty of data easily.

63, TITLE:      Neural Architecture Search with Reinforce and Masked Attention Autoregressive Density Estimators
http://arxiv.org/abs/2006.00939
AUTHORS:        Chepuri Shri Krishna ; Ashish Gupta ; Himanshu Rai ; Swarnim Narayan
HIGHLIGHT:      In this paper, we present a reinforcement learning algorithm based on policy gradient that uses an attention-based autoregressive model to design the policy network.

64, TITLE:      Multimodal grid features and cell pointers for Scene Text Visual Question Answering
http://arxiv.org/abs/2006.00923
AUTHORS:        Lluís Gómez ; Ali Furkan Biten ; Rubèn Tito ; Andrés Mafla ; Dimosthenis Karatzas
HIGHLIGHT:      This paper presents a new model for the task of scene text visual question answering, in which questions about a given image can only be answered by reading and understanding scene text that is present in it.

65, TITLE:      BPGC at SemEval-2020 Task 11: Propaganda Detection in News Articles with Multi-Granularity Knowledge Sharing and Linguistic Features based Ensemble Learning
http://arxiv.org/abs/2006.00593
AUTHORS:        Rajaswa Patil ; Somesh Singh ; Swati Agarwal
COMMENTS:       Under review at SemEval-2020 workshop
HIGHLIGHT:      For sub-task 1, we use contextual embeddings extracted from pre-trained transformer models to represent the text data at various granularities and propose a multi-granularity knowledge sharing approach.

66, TITLE:      Predicting Engagement in Video Lectures
http://arxiv.org/abs/2006.00592
AUTHORS:        Sahan Bulathwela ; María Pérez-Ortiz ; Aldo Lipani ; Emine Yilmaz ; John Shawe-Taylor
COMMENTS:       In Proceedings of International Conference on Educational Data Mining 2020
HIGHLIGHT:      In this work, we explore the idea of building a predictive model for population-based engagement in education. We introduce a novel, large dataset of video lectures for predicting context-agnostic engagement and propose both cross-modal and modality-specific feature sets to achieve this task.

67, TITLE:      Efficient Deployment ofConversational 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.

68, TITLE:      Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning
http://arxiv.org/abs/2006.00587
AUTHORS:        Jianhao Wang ; Zhizhou Ren ; Beining Han ; Chongjie Zhang
HIGHLIGHT:      In the empirical study, our experiments also demonstrate that most deep multi-agent Q-learning algorithms using linear value decomposition structure cannot efficiently utilize off-policy samples.

69, TITLE:      Automated Neuron Shape Analysis from Electron Microscopy
http://arxiv.org/abs/2006.00100
AUTHORS:        Sharmishtaa Seshamani ; Leila Elabbady ; Casey Schneider-Mizell ; Gayathri Mahalingam ; Sven Dorkenwald ; Agnes Bodor ; Thomas Macrina ; Daniel Bumbarger ; JoAnn Buchanan ; Marc Takeno ; Wenjing Yin ; Derrick Brittain ; Russel Torres ; Daniel Kapner ; Kisuk lee ; Ran Lu ; Jinpeng Wu ; Nuno daCosta ; Clay Reid ; Forrest Collman
COMMENTS:       9 pages, 4 figures
HIGHLIGHT:      This paper proposes a fully automated framework for analysis of post-synaptic structure based neuron analysis from EM data.

70, TITLE:      Neural Entity Linking: A Survey of Models based on Deep Learning
http://arxiv.org/abs/2006.00575
AUTHORS:        Ozge Sevgili ; Artem Shelmanov ; Mikhail Arkhipov ; Alexander Panchenko ; Chris Biemann
HIGHLIGHT:      In this survey, we provide a comprehensive description of recent neural entity linking (EL) systems.

71, TITLE:      "Judge me by my size (noun), do you?'' YodaLib: A Demographic-Aware Humor Generation Framework
http://arxiv.org/abs/2006.00578
AUTHORS:        Aparna Garimella ; Carmen Banea ; Nabil Hossain ; Rada Mihalcea
HIGHLIGHT:      We propose an automatic humor generation framework for filling the blanks in Mad Libs stories, while accounting for the demographic backgrounds of the desired audience. We collect a dataset consisting of such stories, which are filled in and judged by carefully selected workers on Amazon Mechanical Turk.

72, TITLE:      Improve Document Embedding for Text Categorization Through Deep Siamese Neural Network
http://arxiv.org/abs/2006.00572
AUTHORS:        Erfaneh Gharavi ; Hadi Veisi
HIGHLIGHT:      To obtain representation for large text, we propose the utilization of deep Siamese neural networks.

73, TITLE:      A General-Purpose Dehazing Algorithm based on Local Contrast Enhancement Approaches
http://arxiv.org/abs/2006.00568
AUTHORS:        Bangyong Sun ; Vincent Whannou de Dravo ; Zhe Yu
COMMENTS:       We draw the attention of the reader that this is a work in progress
HIGHLIGHT:      To better understand this type of algorithm, we present in this document a dehazing method which is suitable for several local contrast adjustment algorithms.

74, TITLE:      Transferring Inductive Biases through Knowledge Distillation
http://arxiv.org/abs/2006.00555
AUTHORS:        Samira Abnar ; Mostafa Dehghani ; Willem Zuidema
HIGHLIGHT:      In this paper, we explore the power of knowledge distillation for transferring the effect of inductive biases from one model to another.

75, TITLE:      Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
http://arxiv.org/abs/2006.00545
AUTHORS:        Ajay Kumar Tanwani ; Pierre Sermanet ; Andy Yan ; Raghav Anand ; Mariano Phielipp ; Ken Goldberg
COMMENTS:       IEEE International Conference on Robotics and Automation (ICRA), 2020
HIGHLIGHT:      In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/sub-goals/options in a semi-supervised manner.

76, TITLE:      Benchmarking BioRelEx for Entity Tagging and Relation Extraction
http://arxiv.org/abs/2006.00533
AUTHORS:        Abhinav Bhatt ; Kaustubh D. Dhole
HIGHLIGHT:      In order to fill this gap, we compare multiple existing entity and relation extraction models over a recently introduced public dataset, BioRelEx of sentences annotated with biological entities and relations.

77, TITLE:      Maximum Voiced Frequency Estimation: Exploiting Amplitude and Phase Spectra
http://arxiv.org/abs/2006.00521
AUTHORS:        Thomas Drugman ; Yannis Stylianou
HIGHLIGHT:      This paper proposes a new approach for MVF estimation which exploits both amplitude and phase spectra.

78, TITLE:      Residual Excitation Skewness for Automatic Speech Polarity Detection
http://arxiv.org/abs/2006.00525
AUTHORS:        Thomas Drugman
HIGHLIGHT:      For this purpose, we here propose a very simple algorithm based on the skewness of two excitation signals.

79, TITLE:      When Bert Forgets How To POS: Amnesic Probing of Linguistic Properties and MLM Predictions
http://arxiv.org/abs/2006.00995
AUTHORS:        Yanai Elazar ; Shauli Ravfogel ; Alon Jacovi ; Yoav Goldberg
HIGHLIGHT:      In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method which is focused on how the information is being used, rather than on what information is encoded.

80, TITLE:      Temporal-Differential Learning in Continuous Environments
http://arxiv.org/abs/2006.00997
AUTHORS:        Tao Bian ; Zhong-Ping Jiang
HIGHLIGHT:      In this paper, a new reinforcement learning (RL) method known as the method of temporal differential is introduced.

81, TITLE:      Toxicity Detection: Does Context Really Matter?
http://arxiv.org/abs/2006.00998
AUTHORS:        John Pavlopoulos ; Jeffrey Sorensen ; Lucas Dixon ; Nithum Thain ; Ion Androutsopoulos
HIGHLIGHT:      We investigate this assumption by focusing on two questions: (a) does context affect the human judgement, and (b) does conditioning on context improve performance of toxicity detection systems?

82, TITLE:      Learning to Recognise Words using Visually Grounded Speech
http://arxiv.org/abs/2006.00512
AUTHORS:        Sebastiaan Scholten ; Danny Merkx ; Odette Scharenborg
HIGHLIGHT:      We investigated word recognition in a Visually Grounded Speech model.

83, TITLE:      Data-driven Detection and Analysis of the Patterns of Creaky Voice
http://arxiv.org/abs/2006.00518
AUTHORS:        Thomas Drugman ; John Kane ; Christer Gobl
HIGHLIGHT:      This paper investigates the temporal excitation patterns of creaky voice.

84, TITLE:      A Neural Network Model of Lexical Competition during Infant Spoken Word Recognition
http://arxiv.org/abs/2006.00999
AUTHORS:        Mihaela Duta ; Kim Plunkett
HIGHLIGHT:      We present a neural network model that processes dynamic unfolding phonological representations and maps them to static internal semantic and visual representations.

85, TITLE:      Automatic Diagnosis of Pulmonary Embolism Using an Attention-guided Framework: A Large-scale Study
http://arxiv.org/abs/2006.00074
AUTHORS:        Luyao Shi ; Deepta Rajan ; Shafiq Abedin ; Manikanta Srikar Yellapragada ; David Beymer ; Ehsan Dehghan
COMMENTS:       MIDL 2020 Full Paper
HIGHLIGHT:      We explored a deep learning model to detect PE on volumetric contrast-enhanced chest CT scans using a 2-stage training strategy.

86, TITLE:      Automated Measurements of Key Morphological Features of Human Embryos for IVF
http://arxiv.org/abs/2006.00067
AUTHORS:        B. D. Leahy ; W. -D. Jang ; H. Y. Yang ; R. Struyven ; D. Wei ; Z. Sun ; K. R. Lee ; C. Royston ; L. Cam ; Y. Kalma ; F. Azem ; D. Ben-Yosef ; H. Pfister ; D. Needleman
HIGHLIGHT:      Our approach greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.

87, TITLE:      Complexity of Maximum Cut on Interval Graphs
http://arxiv.org/abs/2006.00061
AUTHORS:        Ranendu Adhikary ; Kaustav Bose ; Satwik Mukherjee ; Bodhayan Roy
HIGHLIGHT:      In this paper, we show that the Max Cut problem is NP-complete on interval graphs.

88, TITLE:      Assessing the validity of saliency maps for abnormality localization in medical imaging
http://arxiv.org/abs/2006.00063
AUTHORS:        Nishanth Thumbavanam Arun ; Nathan Gaw ; Praveer Singh ; Ken Chang ; Katharina Viktoria Hoebel ; Jay Patel ; Mishka Gidwani ; Jayashree Kalpathy-Cramer
HIGHLIGHT:      In this work, we explored the credibility of the various existing saliency map methods on the RSNA Pneumonia dataset.

89, TITLE:      Towards a Human-Centred Cognitive Model of Visuospatial Complexity in Everyday Driving
http://arxiv.org/abs/2006.00059
AUTHORS:        Vasiliki Kondyli ; Mehul Bhatt ; Jakob Suchan
COMMENTS:       9th European Starting AI Researchers Symposium (STAIRS), at ECAI 2020, the 24th European Conference on Artificial Intelligence (ECAI)., Santiago de Compostela, Spain
HIGHLIGHT:      We develop a human-centred, cognitive model of visuospatial complexity in everyday, naturalistic driving conditions.

90, TITLE:      Applying the Decisiveness and Robustness Metrics to Convolutional Neural Networks
http://arxiv.org/abs/2006.00058
AUTHORS:        Christopher A. George ; Eduardo A. Barrera ; Kenric P. Nelson
HIGHLIGHT:      We review three recently-proposed classifier quality metrics and consider their suitability for large-scale classification challenges such as applying convolutional neural networks to the 1000-class ImageNet dataset.

91, TITLE:      Stance Prediction for Contemporary Issues: Data and Experiments
http://arxiv.org/abs/2006.00052
AUTHORS:        Marjan Hosseinia ; Eduard Dragut ; Arjun Mukherjee
HIGHLIGHT:      We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format.

92, TITLE:      Learning stochastic object models from medical imaging measurements using Progressively-Growing AmbientGANs
http://arxiv.org/abs/2006.00033
AUTHORS:        Weimin Zhou ; Sayantan Bhadra ; Frank J. Brooks ; Hua Li ; Mark A. Anastasio
COMMENTS:       Submitted to IEEE Transactions on Medical Imaging
HIGHLIGHT:      To circumvent this, in this work, a new Progressive Growing AmbientGAN (ProAmGAN) strategy is developed for establishing SOMs from medical imaging measurements.

93, TITLE:      A Comparative Study of Lexical Substitution Approaches based on Neural Language Models
http://arxiv.org/abs/2006.00031
AUTHORS:        Nikolay Arefyev ; Boris Sheludko ; Alexander Podolskiy ; Alexander Panchenko
HIGHLIGHT:      In this paper, we present a large-scale comparative study of popular neural language and masked language models (LMs and MLMs), such as context2vec, ELMo, BERT, XLNet, applied to the task of lexical substitution.

94, TITLE:      Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
http://arxiv.org/abs/2006.00027
AUTHORS:        Gabriel García ; Rocío del Amor ; Adrián Colomer ; Valery Naranjo
HIGHLIGHT:      We propose in this paper two different deep-learning-based approaches to address glaucoma detection just from raw circumpapillary OCT images.

95, TITLE:      Detecting Group Beliefs Related to 2018's Brazilian Elections in Tweets A Combined Study on Modeling Topics and Sentiment Analysis
http://arxiv.org/abs/2006.00490
AUTHORS:        Brenda Salenave Santana ; Aline Aver Vanin
HIGHLIGHT:      In this work, we perform an analysis covering politically motivated discourses related to the second round in Brazilian elections. In order to verify whether similar discourses reinforce group engagement to personal beliefs, we collected a set of tweets related to political hashtags at that moment.

96, TITLE:      BiERU: Bidirectional Emotional Recurrent Unit for Conversational Sentiment Analysis
http://arxiv.org/abs/2006.00492
AUTHORS:        Wei Li ; Wei Shao ; Shaoxiong Ji ; Erik Cambria
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis.

97, TITLE:      Real-World Scenario Mining for the Assessment of Automated Vehicles
http://arxiv.org/abs/2006.00483
AUTHORS:        Erwin de Gelder ; Jeroen Manders ; Corrado Grappiolo ; Jan-Pieter Paardekooper ; Olaf Op den Camp ; Bart De Schutter
COMMENTS:       8 pages, 8 figures, 4 tables, accepted for publication in the proceedings of the IEEE 2020 Intelligent Transportation Systems Conference (ITSC)
HIGHLIGHT:      In this paper, we propose a new method to capture scenarios from real-world data using a two-step approach.

98, TITLE:      Implementing AI-powered semantic character recognition in motor racing sports
http://arxiv.org/abs/2006.00904
AUTHORS:        Jose David Fernández Rodríguez ; David Daniel Albarracín Molina ; Jesús Hormigo Cebolla
COMMENTS:       8 pages, 7 figures, 2020 NAB Broadcast Engineering and Information Technology (BEIT) Conference
HIGHLIGHT:      This paper presents a system that largely automates these tasks and enables dynamic overlays using deep learning to track the drivers as they move on screen, without human intervention. We present the challenges faced during the implementation and discuss the implications.

99, TITLE:      PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
http://arxiv.org/abs/2006.00900
AUTHORS:        Henry Charlesworth ; Giovanni Montana
HIGHLIGHT:      In this work we propose PlanGAN, a model-based algorithm specifically designed for solving multi-goal tasks in environments with sparse rewards.

100, TITLE:     Anatomical Predictions using Subject-Specific Medical Data
http://arxiv.org/abs/2006.00090
AUTHORS:        Marianne Rakic ; John Guttag ; Adrian V. Dalca
COMMENTS:       Accepted as a short paper to MIDL2020. Keywords: Medical Imaging, Multi-Modal, Prediction
HIGHLIGHT:      We present a method that predicts how a brain MRI for an individual will change over time.

101, TITLE:     Explainable Artificial Intelligence: a Systematic Review
http://arxiv.org/abs/2006.00093
AUTHORS:        Giulia Vilone ; Luca Longo
COMMENTS:       78 pages, 18 figures, journal paper to be submitted to ACM Computing Surveys
HIGHLIGHT:      This systematic review contributes to the body of knowledge by clustering these methods with a hierarchical classification system with four main clusters: review articles, theories and notions, methods and their evaluation.

102, TITLE:     Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data
http://arxiv.org/abs/2006.00080
AUTHORS:        Qi Chang ; Hui Qu ; Yikai Zhang ; Mert Sabuncu ; Chao Chen ; Tong Zhang ; Dimitris Metaxas
COMMENTS:       to be published in Conference on Computer Vision and Pattern Recognition (CVPR)2020
HIGHLIGHT:      In this paper, we propose a data privacy-preserving and communication efficient distributed GAN learning framework named Distributed Asynchronized Discriminator GAN (AsynDGAN).

103, TITLE:     Automatic segmentation of the pulmonary lobes with a 3D u-net and optimized loss function
http://arxiv.org/abs/2006.00083
AUTHORS:        Bianca Lassen-Schmidt ; Alessa Hering ; Stefan Krass ; Hans Meine
COMMENTS:       MIDL2020 short paper
HIGHLIGHT:      We trained a 3D u-net for pulmonary lobe segmentation on 49 mainly publically available datasets and introduced a weighted Dice loss function to emphasize the lobar boundaries.

104, TITLE:     KGTK: A Toolkit for Large Knowledge Graph Manipulation and Analysis
http://arxiv.org/abs/2006.00088
AUTHORS:        Filip Ilievski ; Daniel Garijo ; Hans Chalupsky ; Naren Teja Divvala ; Yixiang Yao ; Craig Rogers ; Ronpeng Li ; Jun Liu ; Amandeep Singh ; Daniel Schwabe ; Pedro Szekely
COMMENTS:       16 pages
HIGHLIGHT:      In this paper, we present KGTK, a data science-centric toolkit to represent, create, transform, enhance and analyze KGs.

105, TITLE:     Synthesizing lesions using contextual GANs improves breast cancer classification on mammograms
http://arxiv.org/abs/2006.00086
AUTHORS:        Eric Wu ; Kevin Wu ; William Lotter
HIGHLIGHT:      Here, we present a novel generative adversarial network (GAN) model for data augmentation that can realistically synthesize and remove lesions on mammograms.

106, TITLE:     Sarcasm Detection using Context Separators in Online Discourse
http://arxiv.org/abs/2006.00850
AUTHORS:        Kartikey Pant ; Tanvi Dadu
COMMENTS:       Accepted at FigLang 2020 workshop to be held at ACL 2020
HIGHLIGHT:      In this work, we use RoBERTa_large to detect sarcasm in both the datasets.

107, TITLE:     3D Lidar Mapping Relative Accuracy Automatic Evaluation Algorithm
http://arxiv.org/abs/2006.00857
AUTHORS:        Guibin Chen ; Jiong Deng ; Dongze Huang ; Shuo Zhang
HIGHLIGHT:      In this paper, we proposed a relative accuracy evaluation algorithm that can automatically evaluate the accuracy of HD map built by 3D lidar mapping without ground truth.

108, TITLE:     LFTag: A Scalable Visual Fiducial System with Low Spatial Frequency
http://arxiv.org/abs/2006.00842
AUTHORS:        Ben Wang
HIGHLIGHT:      This paper presents LFTag, a visual fiducial system based on topological detection and relative position data encoding which optimizes data density within spatial frequency constraints.

109, TITLE:     Rhetoric, Logic, and Dialectic: Advancing Theory-based Argument Quality Assessment in Natural Language Processing
http://arxiv.org/abs/2006.00843
AUTHORS:        Anne Lauscher ; Lily Ng ; Courtney Napoles ; Joel Tetreault
HIGHLIGHT:      In this work, we advance theory-based argument quality research by conducting an extensive analysis covering three diverse domains of online argumentative writing: Q&A forums, debate forums, and review forums.

110, TITLE:     Distilling Neural Networks for Greener and Faster Dependency Parsing
http://arxiv.org/abs/2006.00844
AUTHORS:        Mark Anderson ; Carlos Gómez-Rodríguez
COMMENTS:       To be published in proceedings of the 16th International Conference on Parsing Technologies. Earlier versions were rejected at the 58th Annual Conference of the Association for Computational Linguistics and 8th International Conference on Learning Representations
HIGHLIGHT:      When distilling to 20\% of the original model's trainable parameters, we only observe an average decrease of $\sim$1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.30x (1.19x) faster than the baseline model on CPU (GPU) at inference time.

111, TITLE:     Temporal Aggregate Representations for Long Term Video Understanding
http://arxiv.org/abs/2006.00830
AUTHORS:        Fadime Sener ; Dipika Singhania ; Angela Yao
HIGHLIGHT:      We address all of these questions with a flexible multi-granular temporal aggregation framework.

112, TITLE:     Efficient EUD Parsing
http://arxiv.org/abs/2006.00838
AUTHORS:        Mathieu Dehouck ; Mark Anderson ; Carlos Gómez-Rodríguez
COMMENTS:       To published in the proceedings of the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies
HIGHLIGHT:      We present the system submission from the FASTPARSE team for the EUD Shared Task at IWPT 2020.

113, TITLE:     Multi-scale Cloud Detection in Remote Sensing Images using a Dual Convolutional Neural Network
http://arxiv.org/abs/2006.00836
AUTHORS:        Markku Luotamo ; Sari Metsämäki ; Arto Klami
HIGHLIGHT:      To support a wider scale of spatial features while simultaneously reducing computational requirements for large satellite images, we propose an architecture of two cascaded CNN model components successively processing undersampled and full resolution images.

114, TITLE:     Thermal Object Detection using Domain Adaptation through Style Consistency
http://arxiv.org/abs/2006.00821
AUTHORS:        Farzeen Munir ; Shoaib Azam ; Muhammad Aasim Rafique ; Ahmad Muqeem Sheri ; Moongu Jeon
HIGHLIGHT:      This paper presents a domain adaptation method for object detection in thermal images.

115, TITLE:     Real-Time Face and Landmark Localization for Eyeblink Detection
http://arxiv.org/abs/2006.00816
AUTHORS:        Paul Bakker ; Henk-Jan Boele ; Zaid Al-Ars ; Christos Strydis
COMMENTS:       13 pages, 10 figures
HIGHLIGHT:      In this work, a face- and landmark-detection algorithm have been carefully combined in order to provide fully automated eyelid tracking, and have further been accelerated to make the first crucial step towards online, closed-loop experiments.

116, TITLE:     Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English
http://arxiv.org/abs/2006.00814
AUTHORS:        Maha Elbayad ; Michael Ustaszewski ; Emmanuelle Esperança-Rodier ; Francis Brunet Manquat ; Laurent Besacier
HIGHLIGHT:      We conduct in this work an evaluation study comparing offline and online neural machine translation architectures.

117, TITLE:     Foreground-aware Semantic Representations for Image Harmonization
http://arxiv.org/abs/2006.00809
AUTHORS:        Konstantin Sofiiuk ; Polina Popenova ; Anton Konushin
HIGHLIGHT:      We propose a novel architecture to utilize the space of high-level features learned by a pre-trained classification network.

118, TITLE:     COVID-19: Social Media Sentiment Analysis on Reopening
http://arxiv.org/abs/2006.00804
AUTHORS:        Mohammed Emtiaz Ahmed ; Md Rafiqul Islam Rabin ; Farah Naz Chowdhury
COMMENTS:       8 pages, 4 figures, 1 table
HIGHLIGHT:      In this paper, we investigate the sentiment and emotion of peoples in the United States on the subject of reopening.

119, TITLE:     Face Authentication from Grayscale Coded Light Field
http://arxiv.org/abs/2006.00473
AUTHORS:        Dana Weitzner ; David Mendlovic ; Raja Giryes
COMMENTS:       To be published at ICIP 2020
HIGHLIGHT:      To mitigate this, we propose a novel authentication system, based on slim grayscale coded light field imaging.

120, TITLE:     Exemplar-based Generative Facial Editing
http://arxiv.org/abs/2006.00472
AUTHORS:        Jingtao Guo ; Yi Liu ; Zhenzhen Qian ; Zuowei Zhou
HIGHLIGHT:      This paper we propose a novel generative approach for exemplar based facial editing in the form of the region inpainting.

121, TITLE:     Modified Segmentation Algorithm for Recognition of Older Geez Scripts Written on Vellum
http://arxiv.org/abs/2006.00465
AUTHORS:        Girma Negashe ; Adane Mamuye
COMMENTS:       7 pages, 12 figures, AfricaNLP2020 Workshop
HIGHLIGHT:      In this study, we introduced a modified segmentation approach to recognize older Geez scripts.

122, TITLE:     Recognizing Chinese Judicial Named Entity using BiLSTM-CRF
http://arxiv.org/abs/2006.00464
AUTHORS:        Pin Tang ; Pinli Yang ; Yuang Shi ; Yi Zhou ; Feng Lin ; Yan Wang
HIGHLIGHT:      Thus, in this paper, we propose a deep learning-based method named BiLSTM-CRF which consists of bi-directional long short-term memory (BiLSTM) and conditional random fields (CRF).

123, TITLE:     End-to-End Change Detection for High Resolution Drone Images with GAN Architecture
http://arxiv.org/abs/2006.00467
AUTHORS:        Yura Zharkovsky ; Ovadya Menadeva
COMMENTS:       This paper will be presented at IMVC 2020 (https://www.imvc.co.il/Program/GeneralProgram.aspx)
HIGHLIGHT:      In this work we reveal for the first time, the potential of using a state-of-the-art change detection GAN based algorithm with high resolution drone images for infrastructure inspection.

124, TITLE:     SANA : Sentiment Analysis on Newspapers comments in Algeria
http://arxiv.org/abs/2006.00459
AUTHORS:        Hichem Rahab ; Abdelhafid Zitouni ; Mahieddine Djoudi
COMMENTS:       9 pages, 2 figures, 12 tables
HIGHLIGHT:      From this study we observe the importance of dedicated resources and methods the newspaper comments sentiment analysis which we look forward in future works.

125, TITLE:     Fast Enhancement for Non-Uniform Illumination Images using Light-weight CNNs
http://arxiv.org/abs/2006.00439
AUTHORS:        Feifan Lv ; Bo Liu ; Feng Lu
COMMENTS:       9 pages, 12 figures, 2 tables
HIGHLIGHT:      To train this network, we propose a semi-supervised retouching solution and construct a new dataset (82k images) contains various scenes and light conditions.

126, TITLE:     EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary Neuromorphic Vision Sensors
http://arxiv.org/abs/2006.00422
AUTHORS:        Deepak Singla ; Vivek Mohan ; Tarun Pulluri ; Andres Ussa ; Bharath Ramesh ; Arindam Basu
COMMENTS:       15 pages, 12 figures
HIGHLIGHT:      In this paper, we present a hybrid event-frame approach for detecting and tracking objects recorded by a stationary neuromorphic vision sensor (NVS) used in the application of traffic monitoring with a hardware efficient processing pipeline that optimizes memory and computational needs.

127, TITLE:     Pseudo-Representation Labeling Semi-Supervised Learning
http://arxiv.org/abs/2006.00429
AUTHORS:        Song-Bo Yang ; Tian-li Yu
HIGHLIGHT:      Therefore, this work proposes the pseudo-representation labeling, a simple and flexible framework that utilizes pseudo-labeling techniques to iteratively label a small amount of unlabeled data and use them as training data.

128, TITLE:     DC-UNet: Rethinking the U-Net Architecture with Dual Channel Efficient CNN for Medical Images Segmentation
http://arxiv.org/abs/2006.00414
AUTHORS:        Ange Lou ; Shuyue Guan ; Murray Loew
HIGHLIGHT:      We have evaluated our model on three datasets with tough cases and have obtained a relative improvement in performance of 2.90%, 1.49% and 11.42% respectively compared with classical U-Net.

129, TITLE:     Attribute-Induced Bias Eliminating for Transductive Zero-Shot Learning
http://arxiv.org/abs/2006.00412
AUTHORS:        Hantao Yao ; Shaobo Min ; Yongdong Zhang ; Changsheng Xu
HIGHLIGHT:      To solve the above problem, we propose a novel Attribute-Induced Bias Eliminating (AIBE) module for Transductive ZSL.

130, TITLE:     Learning to refer informatively by amortizing pragmatic reasoning
http://arxiv.org/abs/2006.00418
AUTHORS:        Julia White ; Jesse Mu ; Noah D. Goodman
COMMENTS:       Accepted to CogSci 2020
HIGHLIGHT:      One theory for how humans reason about language is presented in the Rational Speech Acts (RSA) framework, which captures pragmatic phenomena via a process of recursive social reasoning (Goodman & Frank, 2016).

131, TITLE:     Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog
http://arxiv.org/abs/2006.00417
AUTHORS:        Jeiyoon Park ; Chanhee Lee ; Kuekyeng Kim ; Heuiseok Lim
HIGHLIGHT:      We proposes the Variational Reward estimator Bottleneck (VRB), which is an effective regularization method that aims to constrain unproductive information flows between inputs and the reward estimator.

132, 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
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.

133, TITLE:     Centralized and Decentralized Non-Cooperative Load-Balancing Games among Competing Cloudlets
http://arxiv.org/abs/2006.00390
AUTHORS:        Sourav Mondal ; Goutam Das ; Elaine Wong
HIGHLIGHT:      Thus, in this paper, we propose an economic and non-cooperative load balancing game for low-latency applications among neighboring cloudlets, from same as well as different service providers.

134, TITLE:     Linguistic Features for Readability Assessment
http://arxiv.org/abs/2006.00377
AUTHORS:        Tovly Deutsch ; Masoud Jasbi ; Stuart Shieber
COMMENTS:       To be published in ACL BEA workshop (15th Workshop on Innovative Use of NLP for Building Educational Applications)
HIGHLIGHT:      This paper combines these two approaches with the goal of improving overall model performance and addressing this question.

135, TITLE:     Entropy Decision Fusion for Smartphone Sensor based Human Activity Recognition
http://arxiv.org/abs/2006.00367
AUTHORS:        Olasimbo Ayodeji Arigbabu
HIGHLIGHT:      We present an approach for fusing convolutional neural network, recurrent convolutional network, and support vector machine by computing and fusing the relative weighted scores from each classifier based on Tsallis entropy to improve human activity recognition performance.

136, TITLE:     Critical Assessment of Transfer Learning for Medical Image Segmentation with Fully Convolutional Neural Networks
http://arxiv.org/abs/2006.00356
AUTHORS:        Davood Karimi ; Simon K. Warfield ; Ali Gholipour
HIGHLIGHT:      Transfer learning is widely used for training machine learning models.

137, TITLE:     Provable guarantees for decision tree induction: the agnostic setting
http://arxiv.org/abs/2006.00743
AUTHORS:        Guy Blanc ; Jane Lange ; Li-Yang Tan
COMMENTS:       20 pages, to appear in ICML 2020
HIGHLIGHT:      We give strengthened provable guarantees on the performance of widely employed and empirically successful {\sl top-down decision tree learning heuristics}.

138, TITLE:     Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods in a small dataset
http://arxiv.org/abs/2006.00730
AUTHORS:        Mizuho Nishio ; Shunjiro Noguchi ; Hidetoshi Matsuo ; Takamichi Murakami
HIGHLIGHT:      Automatic classification between COVID-19 pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray image: combination of data augmentation methods in a small dataset

139, TITLE:     Using Generative Models for Pediatric wbMRI
http://arxiv.org/abs/2006.00727
AUTHORS:        Alex Chang ; Vinith M. Suriyakumar ; Abhishek Moturu ; Nipaporn Tewattanarat ; Andrea Doria ; Anna Goldenberg
HIGHLIGHT:      We use the Frchet Inception Distance (FID) metric, Domain Frchet Distance (DFD), and blind tests with a radiology fellow for evaluation.

140, TITLE:     DeepMark++: CenterNet-based Clothing Detection
http://arxiv.org/abs/2006.00710
AUTHORS:        Alexey Sidnev ; Alexander Krapivin ; Alexey Trushkov ; Ekaterina Krasikova ; Maxim Kazakov
HIGHLIGHT:      We introduce several powerful post-processing techniques that may be applied to increase the quality of keypoint localization tasks.

141, TITLE:     Leveraging TSP Solver Complementarity via Deep Learning
http://arxiv.org/abs/2006.00715
AUTHORS:        Kangfei Zhao ; Shengcai Liu ; Yu Rong ; Jeffrey Xu Yu
HIGHLIGHT:      In this paper, for the first time, we propose a deep learning framework, \CTAS, for TSP solver selection in an end-to-end manner. Moreover, to support large scale TSP solver selection, we construct a challenging TSP benchmark dataset with 6,000 instances, which is known as the largest TSP benchmark.

142, TITLE:     Influence via Ethos: On the Persuasive Power of Reputation in Deliberation Online
http://arxiv.org/abs/2006.00707
AUTHORS:        Emaad Manzoor ; George H. Chen ; Dokyun Lee ; Michael D. Smith
HIGHLIGHT:      Our research examines the persuasive power of $\textit{ethos}$ -- an individual's "reputation" -- using a 7-year panel of over a million debates from an argumentation platform containing explicit indicators of successful persuasion.

143, TITLE:     Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses
http://arxiv.org/abs/2006.00703
AUTHORS:        Chander Chandak ; Zeynab Raeesy ; Ariya Rastrow ; Yuzong Liu ; Xiangyang Huang ; Siyu Wang ; Dong Kwon Joo ; Roland Maas
COMMENTS:       5 pages, 2 figures
HIGHLIGHT:      This paper presents our modeling and architecture approaches for building a highly accurate low-latency language identification system to support multilingual spoken queries for voice assistants.

144, TITLE:     Semi-Supervised Fine-Tuning for Deep Learning Models in Remote Sensing Applications
http://arxiv.org/abs/2006.00345
AUTHORS:        Eftychios Protopapadakis ; Anastasios Doulamis ; Nikolaos Doulamis ; Evangelos Maltezos
HIGHLIGHT:      The proposed methodology demonstrates the impact on the performance of deep learning models, when SSL approaches are used as performance functions during training.

145, TITLE:     Probabilistic self-learning framework for Low-dose CT Denoising
http://arxiv.org/abs/2006.00327
AUTHORS:        Ti Bai ; Dan Nguyen ; Biling Wang ; Steve Jiang
HIGHLIGHT:      To alleviate this problem, in this paper, a shift-invariant property based neural network was devised to learn the inherent pixel correlations and also the noise distribution by only using the LDCT images, shaping into our probabilistic self-learning framework.

146, TITLE:     Learning to Recognize Code-switched Speech Without Forgetting Monolingual Speech Recognition
http://arxiv.org/abs/2006.00782
AUTHORS:        Sanket Shah ; Basil Abraham ; Gurunath Reddy M ; Sunayana Sitaram ; Vikas Joshi
COMMENTS:       5 pages (4 pages + 1 page references), 5 tables, 1 figure, 1 algorithm, 16 references
HIGHLIGHT:      In this work, we show that fine-tuning ASR models on code-switched speech harms performance on monolingual speech.

147, TITLE:     SDCT-AuxNet$^θ$: DCT Augmented Stain Deconvolutional CNN with Auxiliary Classifier for Cancer Diagnosis
http://arxiv.org/abs/2006.00304
AUTHORS:        Shiv Gehlot ; Anubha Gupta ; Ritu Gupta
COMMENTS:       The final version of this preprint has been published in Medical Image Analysis
HIGHLIGHT:      This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer.

148, TITLE:     Super-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation
http://arxiv.org/abs/2006.00303
AUTHORS:        Jianqiang Wan ; Yang Liu ; Donglai Wei ; Xiang Bai ; Yongchao Xu
COMMENTS:       Accepted to CVPR 2020. 10 pages, 9 figures. Code available at https: //github.com/JianqiangWan/Super-BPD
HIGHLIGHT:      In this paper, we propose a fast image segmentation method based on a novel super boundary-to-pixel direction (super-BPD) and a customized segmentation algorithm with super-BPD.

149, TITLE:     Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos
http://arxiv.org/abs/2006.00785
AUTHORS:        Benet Oriol ; Jordi Luque ; Ferran Diego ; Xavier Giro-i-Nieto
COMMENTS:       Accepted for presentation at EPIC@CVPR2020 workshop
HIGHLIGHT:      In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives.

150, TITLE:     Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation
http://arxiv.org/abs/2006.00781
AUTHORS:        Xiao-Lei Yin ; Dong-Xue Liang ; Lu Wang ; Jing Qiu ; Zhi-Yun Yang ; Jun-Hui Xing ; Jian-Zeng Dong ; Zhao-Yuan Ma
HIGHLIGHT:      In this work, we innovatively utilize a deep-learning based video interpolation algorithm to interpolate coronary angiography videos.

151, TITLE:     Variational Bayesian Inference for Crowdsourcing Predictions
http://arxiv.org/abs/2006.00778
AUTHORS:        Desmond Cai ; Duc Chien Nguyen ; Shiau Hong Lim ; Laura Wynter
COMMENTS:       7 pages
HIGHLIGHT:      To do so, we propose a Bayesian approach aimed specifically at alleviating overfitting, a typical impediment to accurate prediction models in practice.

152, TITLE:     Structured Multimodal Attentions for TextVQA
http://arxiv.org/abs/2006.00753
AUTHORS:        Chenyu Gao ; Qi Zhu ; Peng Wang ; Hui Li ; Yuliang Liu ; Anton van den Hengel ; Qi Wu
COMMENTS:       19 pages, winner of TextVQA Challenge 2020
HIGHLIGHT:      In this paper, we propose a structured multimodal attention (SMA) neural network to solve the above issues.

153, TITLE:     Global Distance-distributions Separation for Unsupervised Person Re-identification
http://arxiv.org/abs/2006.00752
AUTHORS:        Xin Jin ; Cuiling Lan ; Wenjun Zeng ; Zhibo Chen
HIGHLIGHT:      To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.

154, TITLE:     Residual Squeeze-and-Excitation Network for Fast Image Deraining
http://arxiv.org/abs/2006.00757
AUTHORS:        Jun Fu ; Jianfeng Xu ; Kazuyuki Tasaka ; Zhibo Chen
COMMENTS:       7 pages, 5 figures
HIGHLIGHT:      In this paper, we propose a residual squeeze-and-excitation network called RSEN for fast image deraining as well as superior deraining performance compared with state-of-the-art approaches.

155, TITLE:     Statistical Guarantees for Regularized Neural Networks
http://arxiv.org/abs/2006.00294
AUTHORS:        Mahsa Taheri ; Fang Xie ; Johannes Lederer
HIGHLIGHT:      In this paper, we develop a general statistical guarantee for estimators that consist of a least-squares term and a regularizer.

156, TITLE:     Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration
http://arxiv.org/abs/2006.00283
AUTHORS:        Dennis J. N. J. Soemers ; Éric Piette ; Matthew Stephenson ; Cameron Browne
COMMENTS:       Accepted at the IEEE Conference on Games (CoG) 2020
HIGHLIGHT:      This paper outlines three different approaches for manipulating the distribution of data collected from self-play, and the procedure that samples batches for learning updates from the collected data.

157, TITLE:     Positron Emission Tomography (PET) image enhancement using a gradient vector orientation based nonlinear diffusion filter (GVOF) for accurate quantitation of radioactivity concentration
http://arxiv.org/abs/2006.00273
AUTHORS:        Mahbubunnabi Tamal
HIGHLIGHT:      A novel parameter free gradient vector orientation based nonlinear diffusion filter (GVOF) is proposed in this paper that is insensitive to statistical fluctuations (e. g., SNR, contrast, size etc.).

158, TITLE:     NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg
http://arxiv.org/abs/2006.01117
AUTHORS:        Joshua Bambrick ; Minjie Xu ; Andy Almonte ; Igor Malioutov ; Guim Perarnau ; Vittorio Selo ; Iat Chong Chan
COMMENTS:       To be presented at ACL 2020 (System Demonstration track)
HIGHLIGHT:      At ACL 2020, we will present a demo of NSTM.

159, TITLE:     Cascaded Text Generation with Markov Transformers
http://arxiv.org/abs/2006.01112
AUTHORS:        Yuntian Deng ; Alexander M. Rush
HIGHLIGHT:      This work proposes an autoregressive model with sub-linear parallel time generation.

160, TITLE:     Is Depth Really Necessary for Salient Object Detection?
http://arxiv.org/abs/2006.00269
AUTHORS:        Shengwei Zhao ; Yifan Zhao ; Jia Li ; Xiaowu Chen
HIGHLIGHT:      Taking the advantages of RGB and RGBD methods, we propose a novel depth-aware salient object detection framework, which has following superior designs: 1) It only takes the depth information as training data while only relies on RGB information in the testing phase.

161, TITLE:     Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas
http://arxiv.org/abs/2006.01110
AUTHORS:        Yen-Ling Kuo ; Boris Katz ; Andrei Barbu
HIGHLIGHT:      We demonstrate a reinforcement learning agent which uses a compositional recurrent neural network that takes as input an LTL formula and determines satisfying actions.

162, TITLE:     Data Augmentation for Learning Bilingual Word Embeddings with Unsupervised Machine Translation
http://arxiv.org/abs/2006.00262
AUTHORS:        Sosuke Nishikawa ; Ryokan Ri ; Yoshimasa Tsuruoka
HIGHLIGHT:      In this paper, we propose using a pseudo-parallel corpus generated by an unsupervised machine translation model to facilitate structural similarity of the two embedding spaces and improve the quality of BWEs in the mapping method.

163, TITLE:     Reconstructing undersampled photoacoustic microscopy images using deep learning
http://arxiv.org/abs/2006.00251
AUTHORS:        Anthony DiSpirito III ; Daiwei Li ; Tri Vu ; Maomao Chen ; Dong Zhang ; Jianwen Luo ; Roarke Horstmeyer ; Junjie Yao
COMMENTS:       12 pages, 7 main figures, 3 supplemental figures (see last 2 pages)
HIGHLIGHT:      In this study, we propose a novel application of deep learning principles to reconstruct undersampled PAM images and transcend the trade-off between spatial resolution and imaging speed.

164, TITLE:     Dynamic Masking for Improved Stability in Spoken Language Translation
http://arxiv.org/abs/2006.00249
AUTHORS:        Yuekun Yao ; Barry Haddow
HIGHLIGHT:      We show how this mask can be set dynamically, improving the latency-flicker trade-off without sacrificing translation quality.

165, TITLE:     Hyperspectral Image Denoising via Global Spatial-Spectral Total Variation Regularized Nonconvex Local Low-Rank Tensor Approximation
http://arxiv.org/abs/2006.00235
AUTHORS:        Haijin Zeng ; Xiaozhen Xie ; Jifeng Ning
HIGHLIGHT:      In this paper, we propose a novel spatial-spectral total variation (SSTV) regularized nonconvex local low-rank (LR) tensor approximation method to remove mixed noise in HSIs.

166, TITLE:     The global information for land cover classification by dual-branch deep learning
http://arxiv.org/abs/2006.00234
AUTHORS:        Fan Zhang ; MinChao Yan ; Chen Hu ; Jun Ni ; Fei Ma
HIGHLIGHT:      This paper proposed a novel method to take into the information of remote sensing image, i.e. geographic latitude-longitude information.



==========Updates to Previous Papers==========
1, 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:      This paper introduces a new info intervention to solve these two problems, and provides causal diagrams for communication and theoretical focus based on this info intervention.

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 type of generalization error induced by initialization in deep neural networks
http://arxiv.org/abs/1905.07777
AUTHORS:        Yaoyu Zhang ; Zhi-Qin John Xu ; Tao Luo ; Zheng Ma
COMMENTS:       Accepted by MSML Revised the proof of Lemma 2
HIGHLIGHT:      In this work, by exploiting the linearity of DNN training dynamics in the NTK regime \citep{jacot2018neural,lee2019wide}, we provide an explicit and quantitative answer to this problem.

4, TITLE:       A Batch Normalized Inference Network Keeps the KL Vanishing Away
http://arxiv.org/abs/2004.12585
AUTHORS:        Qile Zhu ; Jianlin Su ; Wei Bi ; Xiaojiang Liu ; Xiyao Ma ; Xiaolin Li ; Dapeng Wu
COMMENTS:       An extension for the original ACL 2020 paper
HIGHLIGHT:      We propose to let the KL follow a distribution across the whole dataset, and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL's distribution positive.

5, TITLE:       Consciousness and Automated Reasoning
http://arxiv.org/abs/2001.09442
AUTHORS:        Ulrike Barthelmeß ; Ulrich Furbach ; Claudia Schon
HIGHLIGHT:      This paper aims at demonstrating how a first-order logic reasoning system in combination with a large knowledge base can be understood as an artificial consciousness system.

6, TITLE:       Proq: Projection-based Runtime Assertions for Debugging on a Quantum Computer
http://arxiv.org/abs/1911.12855
AUTHORS:        Gushu Li ; Li Zhou ; Nengkun Yu ; Yufei Ding ; Mingsheng Ying ; Yuan Xie
COMMENTS:       A major revision, in submission
HIGHLIGHT:      In this paper, we propose Proq, a runtime assertion scheme for testing and debugging quantum programs on a quantum computer.

7, TITLE:       Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI
http://arxiv.org/abs/2004.09788
AUTHORS:        Jinyoung Kim ; Remi Patriat ; Jordan Kaplan ; Oren Solomon ; Noam Harel
COMMENTS:       56 pages (one column), 13 figures, 5 tables, supplementary materials, Accepted for publication in IEEE Access
HIGHLIGHT:      In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI.

8, TITLE:       JPAD-SE: High-Level Semantics for Joint Perception-Accuracy-Distortion Enhancement in Image Compression
http://arxiv.org/abs/2005.12810
AUTHORS:        Shiyu Duan ; Huaijin Chen ; Jinwei Gu
HIGHLIGHT:      In this paper, we (1) present a generic framework that can enable any image codec to leverage high-level semantics, and (2) study the joint optimization of perception quality, accuracy of downstream computer vision task, and distortion.

9, TITLE:       Pyramid Attention Networks for Image Restoration
http://arxiv.org/abs/2004.13824
AUTHORS:        Yiqun Mei ; Yuchen Fan ; Yulun Zhang ; Jiahui Yu ; Yuqian Zhou ; Ding Liu ; Yun Fu ; Thomas S. Huang ; Honghui Shi
HIGHLIGHT:      To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid.

10, TITLE:      A Framework for Building Closed-Domain Chat Dialogue Systems
http://arxiv.org/abs/1910.13826
AUTHORS:        Mikio Nakano ; Kazunori Komatani
COMMENTS:       24 pages
HIGHLIGHT:      This paper presents HRIChat, a framework for developing closed-domain chat dialogue systems.

11, TITLE:      Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI
http://arxiv.org/abs/2005.11524
AUTHORS:        Anas Tahir ; Yazan Qiblawey ; Amith Khandakar ; Tawsifur Rahman ; Uzair Khurshid ; Farayi Musharavati ; Serkan Kiranyaz ; Muhammad E. H. Chowdhury
COMMENTS:       10 Figures, 4 Tables
HIGHLIGHT:      In this work, authors used deep machine learning algorithms along with innovative image pre-processing techniques to distinguish COVID-19 images from SARS and MERS images.

12, TITLE:      Direct Sparse Mapping
http://arxiv.org/abs/1904.06577
AUTHORS:        Jon Zubizarreta ; Iker Aguinaga ; J. M. M. Montiel
COMMENTS:       Accepted for publication in IEEE Transactions on Robotics
HIGHLIGHT:      We present, DSM, a full monocular visual SLAM based on PBA.

13, TITLE:      End-to-End Pixel-Based Deep Active Inference for Body Perception and Action
http://arxiv.org/abs/2001.05847
AUTHORS:        Cansu Sancaktar ; Marcel van Gerven ; Pablo Lanillos
HIGHLIGHT:      We present a pixel-based deep active inference algorithm (PixelAI) inspired by human body perception and action.

14, TITLE:      Few-shot Learning for Domain-specific Fine-grained Image Classification
http://arxiv.org/abs/1907.09647
AUTHORS:        Xin Sun ; Hongwei Xv ; Junyu Dong ; Qiong Li ; Changrui Chen
COMMENTS:       11 pages, 11 figures
HIGHLIGHT:      We propose a feature fusion model to explore discriminative features by focusing on key regions.

15, TITLE:      TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
http://arxiv.org/abs/1903.11239
AUTHORS:        Andy Zeng ; Shuran Song ; Johnny Lee ; Alberto Rodriguez ; Thomas Funkhouser
COMMENTS:       Summary Video: https://youtu.be/f5Zn2Up2RjQ Project webpage: https://tossingbot.cs.princeton.edu
HIGHLIGHT:      In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error.

16, TITLE:      Characterizations and approximability of hard counting classes below #P
http://arxiv.org/abs/2003.02524
AUTHORS:        Eleni Bakali ; Aggeliki Chalki ; Aris Pagourtzis
COMMENTS:       18 pages, 5 figures, to be published in the proceedings of TAMC2020
HIGHLIGHT:      An important objective of research in counting complexity is to understand which counting problems are approximable.

17, TITLE:      Sealing Pointer-Based Optimizations Behind Pure Functions
http://arxiv.org/abs/2003.01685
AUTHORS:        Daniel Selsam ; Simon Hudon ; Leonardo de Moura
HIGHLIGHT:      We have implemented our approach for the upcoming version (v4) of Lean, and our approach could be adopted by other languages based on dependent type theory as well.

18, TITLE:      AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery
http://arxiv.org/abs/2003.04769
AUTHORS:        Mobarakol Islam ; Vibashan VS ; Hongliang Ren
COMMENTS:       Accepted in the conference of ICRA 2020
HIGHLIGHT:      Thus, we propose an asynchronous task-aware optimization (ATO) technique to calculate task-oriented gradients and train the decoders independently.

19, TITLE:      The information bottleneck and geometric clustering
http://arxiv.org/abs/1712.09657
AUTHORS:        DJ Strouse ; David J Schwab
COMMENTS:       Updated to final published version with more detailed relationship to GMMs/k-means
HIGHLIGHT:      We apply this approach to a variety of simple clustering problems, showing that DIB with our model selection procedure recovers the generative cluster labels.

20, TITLE:      Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics
http://arxiv.org/abs/1912.09978
AUTHORS:        Ylenia Giarratano ; Eleonora Bianchi ; Calum Gray ; Andrew Morris ; Tom MacGillivray ; Baljean Dhillon ; Miguel O. Bernabeu
HIGHLIGHT:      In this study, we generate the first open dataset of retinal parafoveal OCTA images with associated ground truth manual segmentations.

21, TITLE:      Face Verification Using 60~GHz 802.11 waveforms
http://arxiv.org/abs/2002.11965
AUTHORS:        Eran Hof ; Amichai Sanderovich ; Evyatar Hemo
HIGHLIGHT:      Our preliminary study shows promising results for the application of autoencoder for the setup at hand.

22, TITLE:      Bi-direction Context Propagation Network for Real-time Semantic Segmentation
http://arxiv.org/abs/2005.11034
AUTHORS:        Shijie Hao ; Yuan Zhou ; Yanrong Guo
COMMENTS:       9 pages, 6 figures
HIGHLIGHT:      To address this problem, we propose the Bi-direction Context Propagation Network (BCPNet) by leveraging both spatial and context information.

23, TITLE:      Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT
http://arxiv.org/abs/2005.13690
AUTHORS:        Hyemin Um ; Jue Jiang ; Maria Thor ; Andreas Rimner ; Leo Luo ; Joseph O. Deasy ; Harini Veeraraghavan
COMMENTS:       MIDL 2020 short paper
HIGHLIGHT:      We trained our approach using 206 thoracic CT scans of lung cancer patients with 35 scans held out for validation to segment the left and right lungs, heart, esophagus, and spinal cord.

24, TITLE:      Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with CNN-based Re-identification
http://arxiv.org/abs/1912.05949
AUTHORS:        Nathanael L. Baisa
COMMENTS:       arXiv admin note: text overlap with arXiv:1908.03945
HIGHLIGHT:      We propose a novel online multi-object visual tracking algorithm via a tracking-by-detection paradigm using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep Convolutional Neural Network (CNN) appearance representations learning.

25, TITLE:      Adversarial Attacks and Defense on Texts: A Survey
http://arxiv.org/abs/2005.14108
AUTHORS:        Aminul Huq ; Mst. Tasnim Pervin
HIGHLIGHT:      In this manuscript we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea.

26, TITLE:      Visual-Inertial Mapping with Non-Linear Factor Recovery
http://arxiv.org/abs/1904.06504
AUTHORS:        Vladyslav Usenko ; Nikolaus Demmel ; David Schubert ; Jörg Stückler ; Daniel Cremers
HIGHLIGHT:      In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery.

27, TITLE:      CRVOS: Clue Refining Network for Video Object Segmentation
http://arxiv.org/abs/2002.03651
AUTHORS:        Suhwan Cho ; MyeongAh Cho ; Tae-young Chung ; Heansung Lee ; Sangyoun Lee
COMMENTS:       ICIP 2020. Code: https://github.com/suhwan-cho/CRVOS
HIGHLIGHT:      In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame's coarse mask and coordinates information.

28, TITLE:      Efficient Derivative Computation for Cumulative B-Splines on Lie Groups
http://arxiv.org/abs/1911.08860
AUTHORS:        Christiane Sommer ; Vladyslav Usenko ; David Schubert ; Nikolaus Demmel ; Daniel Cremers
COMMENTS:       First two authors contributed equally
HIGHLIGHT:      In this work we present an alternative derivation of time derivatives based on recurrence relations that needs $\mathcal{O}(k)$ instead of $\mathcal{O}(k^2)$ matrix operations (for a spline of order $k$) and results in simple and elegant expressions.

29, TITLE:      Surrogate Scoring Rules
http://arxiv.org/abs/1802.09158
AUTHORS:        Yang Liu ; Juntao Wang ; Yiling Chen
HIGHLIGHT:      In this paper, we extend such scoring rules to settings where a principal elicits private probabilistic beliefs but only has access to agents' reports.

30, TITLE:      Quantifying Attention Flow in Transformers
http://arxiv.org/abs/2005.00928
AUTHORS:        Samira Abnar ; Willem Zuidema
HIGHLIGHT:      In this paper, we consider the problem of quantifying this flow of information through self-attention.

31, TITLE:      Attention! A Lightweight 2D Hand Pose Estimation Approach
http://arxiv.org/abs/2001.08047
AUTHORS:        Nicholas Santavas ; Ioannis Kansizoglou ; Loukas Bampis ; Evangelos Karakasis ; Antonios Gasteratos
COMMENTS:       updated version with ablation studies
HIGHLIGHT:      In this letter, we present a novel Convolutional Neural Network architecture, reinforced with a Self-Attention module that it can be deployed on an embedded system, due to its lightweight nature, with just 1.9 Million parameters.

32, TITLE:      From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
http://arxiv.org/abs/2001.01886
AUTHORS:        Haipeng Xiong ; Hao Lu ; Chengxin Liu ; Liang Liu ; Chunhua Shen ; Zhiguo Cao
COMMENTS:       Extended version of arXiv:1908.06473
HIGHLIGHT:      We therefore introduce the idea of spatial divide-and-conquer (S-DC) that transforms open-set counting into a closed-set problem.

33, TITLE:      Pathological speech detection using x-vector embeddings
http://arxiv.org/abs/2003.00864
AUTHORS:        Catarina Botelho ; Francisco Teixeira ; Thomas Rolland ; Alberto Abad ; Isabel Trancoso
COMMENTS:       Rejected for publication by peer review
HIGHLIGHT:      In this work, we focus on the latter, to assess their applicability as a general feature extraction method to the detection of Parkinson's disease (PD) and obstructive sleep apnea (OSA).

34, TITLE:      Distributed Embodied Evolution in Networks of Agents
http://arxiv.org/abs/2003.12848
AUTHORS:        Anil Yaman ; Giovanni Iacca
HIGHLIGHT:      In this work we propose a distributed embodied evolutionary approach to optimize spatially distributed, locally interacting agents by allowing them to exchange their behavior parameters and learn from each other to adapt to a certain task within a given environment.

35, TITLE:      Can Giraffes Become Birds? An Evaluation of Image-to-image Translation for Data Generation
http://arxiv.org/abs/2001.03637
AUTHORS:        Daniel V. Ruiz ; Gabriel Salomon ; Eduardo Todt
COMMENTS:       Accepted for presentation at the Computer on the Beach (COTB'20) 2020
HIGHLIGHT:      In the present work, we investigate image-to-image translation using Generative Adversarial Networks (GANs) for generating new data, taking as a case study the morphing of giraffes images into bird images.

36, TITLE:      Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction
http://arxiv.org/abs/2005.00987
AUTHORS:        Masahiro Kaneko ; Masato Mita ; Shun Kiyono ; Jun Suzuki ; Kentaro Inui
COMMENTS:       Accepted as a short paper to the 58th Annual Conference of the Association for Computational Linguistics (ACL-2020)
HIGHLIGHT:      This paper investigates how to effectively incorporate a pre-trained masked language model (MLM), such as BERT, into an encoder-decoder (EncDec) model for grammatical error correction (GEC).

37, TITLE:      4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation
http://arxiv.org/abs/2004.09216
AUTHORS:        Nils Gessert ; Marcel Bengs ; Julia Krüger ; Roland Opfer ; Ann-Christin Ostwaldt ; Praveena Manogaran ; Sven Schippling ; Alexander Schlaefer
COMMENTS:       Accepted at MIDL 2020
HIGHLIGHT:      In this work, we investigate whether extending this problem to full 4D deep learning using a history of MRI volumes and thus an extended baseline can improve performance.

38, TITLE:      Robust Object Detection under Occlusion with Context-Aware CompositionalNets
http://arxiv.org/abs/2005.11643
AUTHORS:        Angtian Wang ; Yihong Sun ; Adam Kortylewski ; Alan Yuille
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.

39, TITLE:      On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
http://arxiv.org/abs/2003.07064
AUTHORS:        Osman Semih Kayhan ; Jan C. van Gemert
COMMENTS:       CVPR 2020. (Minor revision on Figure 4, arguments unchanged.)
HIGHLIGHT:      In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant.

40, TITLE:      Learning Local Features with Context Aggregation for Visual Localization
http://arxiv.org/abs/2005.12880
AUTHORS:        Siyu Hong ; Kunhong Li ; Yongcong Zhang ; Zhiheng Fu ; Mengyi Liu ; Yulan Guo
COMMENTS:       4 pages, 2 figures
HIGHLIGHT:      In this paper, we focus on the fusion of low-level textual information and high-level semantic context information to improve the discrimitiveness of local features.

41, TITLE:      Backward Feature Correction: How Deep Learning Performs Deep Learning
http://arxiv.org/abs/2001.04413
AUTHORS:        Zeyuan Allen-Zhu ; Yuanzhi Li
HIGHLIGHT:      We present a theory towards explaining this in terms of hierarchical learning.

42, TITLE:      Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
http://arxiv.org/abs/2005.10242
AUTHORS:        Tongzhou Wang ; Phillip Isola
COMMENTS:       International Conference on Machine Learning (ICML), 2020
HIGHLIGHT:      In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere.

43, TITLE:      From Prediction to Prescription: AI-Based Optimization of Non-Pharmaceutical Interventions for the COVID-19 Pandemic
http://arxiv.org/abs/2005.13766
AUTHORS:        Risto Miikkulainen ; Olivier Francon ; Elliot Meyerson ; Xin Qiu ; Elisa Canzani ; Babak Hodjat
HIGHLIGHT:      Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures.

44, TITLE:      JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search
http://arxiv.org/abs/2005.13783
AUTHORS:        Ali Ahmadvand ; Surya Kallumadi ; Faizan Javed ; Eugene Agichtein
COMMENTS:       SIGIR 2020
HIGHLIGHT:      In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.

45, TITLE:      Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium
http://arxiv.org/abs/1808.08210
AUTHORS:        Xiran Wang ; Jason Juang ; Stanley H. Chan
HIGHLIGHT:      We propose an automatic foreground extraction method in applications where a static but imperfect background is available.

46, TITLE:      privGAN: Protecting GANs from membership inference attacks at low cost
http://arxiv.org/abs/2001.00071
AUTHORS:        Sumit Mukherjee ; Yixi Xu ; Anusua Trivedi ; Juan Lavista Ferres
HIGHLIGHT:      The main contributions of this paper are: i) we propose a novel GAN architecture that can generate synthetic data in a privacy preserving manner without additional hyperparameter tuning and architecture selection, ii) we provide a theoretical understanding of the optimal solution of the privGAN loss function, iii) we demonstrate the effectiveness of our model against several white and black--box attacks on several benchmark datasets, iv) we demonstrate on three common benchmark datasets that synthetic images generated by privGAN lead to negligible loss in downstream performance when compared against non--private GANs.

47, TITLE:      Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition
http://arxiv.org/abs/2005.04862
AUTHORS:        Ye Bai ; Jiangyan Yi ; Jianhua Tao ; Zhengkun Tian ; Zhengqi Wen ; Shuai Zhang
COMMENTS:       submitted to INTERSPEECH2020
HIGHLIGHT:      To address this issue, we propose a non-autoregressive end-to-end speech recognition system called LASO (listen attentively, and spell once).

48, TITLE:      SBERT-WK: A Sentence Embedding Method by Dissecting BERT-based Word Models
http://arxiv.org/abs/2002.06652
AUTHORS:        Bin Wang ; C. -C. Jay Kuo
HIGHLIGHT:      In this work, we study the layer-wise pattern of the word representation of deep contextualized models.

49, TITLE:      Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts
http://arxiv.org/abs/1909.12299
AUTHORS:        Subba Reddy Oota ; Naresh Manwani ; Raju S. Bapi
COMMENTS:       15 pages
HIGHLIGHT:      In this paper, we achieve this by clustering similar regions together and for every cluster we learn a different linear regression model using a mixture of linear experts model.

50, TITLE:      TutorialVQA: Question Answering Dataset for Tutorial Videos
http://arxiv.org/abs/1912.01046
AUTHORS:        Anthony Colas ; Seokhwan Kim ; Franck Dernoncourt ; Siddhesh Gupte ; Daisy Zhe Wang ; Doo Soon Kim
COMMENTS:       Accepted at LREC 2020
HIGHLIGHT:      To adequately address this, We propose a new question answering task on instructional videos, because of their verbose and narrative nature.

51, TITLE:      Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples
http://arxiv.org/abs/1902.02067
AUTHORS:        Derui Wang ; Chaoran Li ; Sheng Wen ; Qing-Long Han ; Surya Nepal ; Xiangyu Zhang ; Yang Xiang
HIGHLIGHT:      In this paper, an adversarial example attack which triggers malfunctioning of NMS in end-to-end OD models is proposed.

52, TITLE:      A Deep Learning Approach for Motion Forecasting Using 4D OCT Data
http://arxiv.org/abs/2004.10121
AUTHORS:        Marcel Bengs ; Nils Gessert ; Alexander Schlaefer
COMMENTS:       Accepted for publication at MIDL 2020: https://openreview.net/forum?id=WVd56kgRV
HIGHLIGHT:      We propose 4D spatio-temporal deep learning for end-to-end motion forecasting and estimation using a stream of OCT volumes.

53, TITLE:      Insignificant Choice Polynomial Time
http://arxiv.org/abs/2005.04598
AUTHORS:        Klaus-Dieter Schewe
COMMENTS:       48 pages
HIGHLIGHT:      We use this result for our second contribution showing that PTIME differs from NP.

54, TITLE:      HOnnotate: A method for 3D Annotation of Hand and Object Poses
http://arxiv.org/abs/1907.01481
AUTHORS:        Shreyas Hampali ; Mahdi Rad ; Markus Oberweger ; Vincent Lepetit
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      We propose a method for annotating images of a hand manipulating an object with the 3D poses of both the hand and the object, together with a dataset created using this method.

55, TITLE:      Nonexistence Certificates for Ovals in a Projective Plane of Order Ten
http://arxiv.org/abs/2001.11974
AUTHORS:        Curtis Bright ; Kevin K. H. Cheung ; Brett Stevens ; Ilias Kotsireas ; Vijay Ganesh
COMMENTS:       Appears in the Proceedings of the 31st International Workshop on Combinatorial Algorithms (IWOCA 2020)
HIGHLIGHT:      In this paper, we rerun the search for ovals in a projective plane of order ten and produce a collection of nonexistence certificates that, when taken together, imply that such ovals do not exist.

56, TITLE:      Generalizing meanings from partners to populations: Hierarchical inference supports convention formation on networks
http://arxiv.org/abs/2002.01510
AUTHORS:        Robert D. Hawkins ; Noah D. Goodman ; Adele E. Goldberg ; Thomas L. Griffiths
COMMENTS:       CogSci 2020
HIGHLIGHT:      We propose a hierarchical Bayesian model to explain how speakers and listeners solve this inductive problem.

57, TITLE:      DenseNet Models for Tiny ImageNet Classification
http://arxiv.org/abs/1904.10429
AUTHORS:        Zoheb Abai ; Nishad Rajmalwar
COMMENTS:       7 pages, 8 figures, 2 tables
HIGHLIGHT:      In this paper, we present two image classification models on the Tiny ImageNet dataset.

58, TITLE:      Normal Assisted Stereo Depth Estimation
http://arxiv.org/abs/1911.10444
AUTHORS:        Uday Kusupati ; Shuo Cheng ; Rui Chen ; Hao Su
HIGHLIGHT:      In this paper, we study how to leverage a normal estimation model and the predicted normal maps to improve the depth quality.

59, TITLE:      Speaker-change Aware CRF for Dialogue Act Classification
http://arxiv.org/abs/2004.02913
AUTHORS:        Guokan Shang ; Antoine Jean-Pierre Tixier ; Michalis Vazirgiannis ; Jean-Pierre Lorré
HIGHLIGHT:      To address this limitation, this paper proposes a simple modification of the CRF layer that takes speaker-change into account.

60, TITLE:      Towards End-to-End Text Spotting in Natural Scenes
http://arxiv.org/abs/1906.06013
AUTHORS:        Peng Wang ; Hui Li ; Chunhua Shen
COMMENTS:       An extension of the work "Towards End-to-end Text Spotting with Convolutional Recurrent Neural Networks", Proc. Int. Conf. Comp. Vision 2017
HIGHLIGHT:      In this work, we propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes such as image cropping and feature re-calculation, word separation, and character grouping.

61, TITLE:      Bi3D: Stereo Depth Estimation via Binary Classifications
http://arxiv.org/abs/2005.07274
AUTHORS:        Abhishek Badki ; Alejandro Troccoli ; Kihwan Kim ; Jan Kautz ; Pradeep Sen ; Orazio Gallo
COMMENTS:       To be presented at CVPR 2020
HIGHLIGHT:      We present Bi3D, a method that estimates depth via a series of binary classifications.

62, TITLE:      What Can ResNet Learn Efficiently, Going Beyond Kernels?
http://arxiv.org/abs/1905.10337
AUTHORS:        Zeyuan Allen-Zhu ; Yuanzhi Li
COMMENTS:       V2 slightly improves lower bound, V3 strengthens experiments and adds citation to "backward feature correction" which is an even stronger form of hierarchical learning [2]
HIGHLIGHT:      Can we more provide theoretical justifications for this gap?

63, TITLE:      Review on Computer Vision in Gastric Cancer: Potential Efficient Tools for Diagnosis
http://arxiv.org/abs/2005.09459
AUTHORS:        Yihua Sun
HIGHLIGHT:      Different methods for data generation and augmentation are presented, and various approaches to extract discriminative features compared and evaluated.

64, TITLE:      The Worrisome Impact of an Inter-rater Bias on Neural Network Training
http://arxiv.org/abs/1906.11872
AUTHORS:        Or Shwartzman ; Harel Gazit ; Ilan Shelef ; Tammy Riklin-Raviv
HIGHLIGHT:      In this paper, we highlight the issue of inter-rater bias as opposed to random inter-observer variability and demonstrate its influence on DNN training, leading to different segmentation results for the same input images.

65, TITLE:      A Personalized Affective Memory Neural Model for Improving Emotion Recognition
http://arxiv.org/abs/1904.12632
AUTHORS:        Pablo Barros ; German I. Parisi ; Stefan Wermter
COMMENTS:       Accepted by the International Conference on Machine Learning 2019 (ICML2019)
HIGHLIGHT:      In this paper, we present a neural model based on a conditional adversarial autoencoder to learn how to represent and edit general emotion expressions.

66, TITLE:      ParsBERT: Transformer-based Model for Persian Language Understanding
http://arxiv.org/abs/2005.12515
AUTHORS:        Mehrdad Farahani ; Mohammad Gharachorloo ; Marzieh Farahani ; Mohammad Manthouri
COMMENTS:       10 pages, 5 figures, 7 tables, table 7 corrected and some refs related to table 7
HIGHLIGHT:      This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-of-the-art performance compared to other architectures and multilingual models.

67, TITLE:      Learning Resilient Behaviors for Navigation Under Uncertainty
http://arxiv.org/abs/1910.09998
AUTHORS:        Tingxiang Fan ; Pinxin Long ; Wenxi Liu ; Jia Pan ; Ruigang Yang ; Dinesh Manocha
COMMENTS:       accepted to ICRA 2020
HIGHLIGHT:      In this paper, we consider the problem that a mobile robot learns adaptive and resilient behaviors for navigating in unseen uncertain environments while avoiding collisions.

68, TITLE:      Optimal Error Pseudodistributions for Read-Once Branching Programs
http://arxiv.org/abs/2002.07208
AUTHORS:        Eshan Chattopadhyay ; Jyun-Jie Liao
HIGHLIGHT:      In this work, we construct a PRPD with seed length $$O(\log n\cdot \log (nw)\cdot \log\log(nw)+\log(1/\varepsilon)).

69, TITLE:      Fast Portrait Segmentation with Highly Light-weight Network
http://arxiv.org/abs/1910.08695
AUTHORS:        Yuezun Li ; Ao Luo ; Siwei Lyu
HIGHLIGHT:      In this paper, we describe a fast and light-weight portrait segmentation method based on a new highly light-weight backbone (HLB) architecture.

70, TITLE:      Kinematic Single Vehicle Trajectory Prediction Baselines and Applications with the NGSIM Dataset
http://arxiv.org/abs/1908.11472
AUTHORS:        Jean Mercat ; Nicole El Zoghby ; Guillaume Sandou ; Dominique Beauvois ; Guillermo Pita Gil
HIGHLIGHT:      In this article we produce reproducible vehicle prediction results from simple models.

71, TITLE:      Elephant in the Room: An Evaluation Framework for Assessing Adversarial Examples in NLP
http://arxiv.org/abs/2001.07820
AUTHORS:        Ying Xu ; Xu Zhong ; Antonio Jose Jimeno Yepes ; Jey Han Lau
HIGHLIGHT:      In this paper, we propose an evaluation framework consisting of a set of automatic evaluation metrics and human evaluation guidelines, to rigorously assess the quality of adversarial examples based on the aforementioned properties.

72, TITLE:      Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
http://arxiv.org/abs/1811.04918
AUTHORS:        Zeyuan Allen-Zhu ; Yuanzhi Li ; Yingyu Liang
COMMENTS:       V1/V2/V3/V4 polish writing, V5 adds experiments, V6 reflects our camera ready version
HIGHLIGHT:      In this work, we prove that overparameterized neural networks can learn some notable concept classes, including two and three-layer networks with fewer parameters and smooth activations.

73, TITLE:      Anytime Integrated Task and Motion Policies for Stochastic Environments
http://arxiv.org/abs/1904.13006
AUTHORS:        Naman Shah ; Deepak Kala Vasudevan ; Kislay Kumar ; Pranav Kamojjhala ; Siddharth Srivastava
HIGHLIGHT:      We present a new approach for integrated task and motion planning in stochastic settings.

74, TITLE:      Dueling Posterior Sampling for Preference-Based Reinforcement Learning
http://arxiv.org/abs/1908.01289
AUTHORS:        Ellen R. Novoseller ; Yibing Wei ; Yanan Sui ; Yisong Yue ; Joel W. Burdick
COMMENTS:       9 pages before references and Appendix; 51 pages total; 7 figures; 4 tables. This replacement corrects some notational conflicts in the previous version, and adds additional details to the mathematical analysis
HIGHLIGHT:      Building upon ideas from preference-based bandit learning and posterior sampling in RL, we present DUELING POSTERIOR SAMPLING (DPS), which employs preference-based posterior sampling to learn both the system dynamics and the underlying utility function that governs the preference feedback.

75, TITLE:      Amora: Black-box Adversarial Morphing Attack
http://arxiv.org/abs/1912.03829
AUTHORS:        Run Wang ; Felix Juefei-Xu ; Qing Guo ; Yihao Huang ; Xiaofei Xie ; Lei Ma ; Yang Liu
HIGHLIGHT:      In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora).

76, TITLE:      A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability
http://arxiv.org/abs/1812.08342
AUTHORS:        Xiaowei Huang ; Daniel Kroening ; Wenjie Ruan ; James Sharp ; Youcheng Sun ; Emese Thamo ; Min Wu ; Xinping Yi
COMMENTS:       To appear in the journal of Computer Science Review
HIGHLIGHT:      This survey paper conducts a review of the current research effort into making DNNs safe and trustworthy, by focusing on four aspects: verification, testing, adversarial attack and defence, and interpretability.

77, TITLE:      C3VQG: Category Consistent Cyclic Visual Question Generation
http://arxiv.org/abs/2005.07771
AUTHORS:        Shagun Uppal ; Anish Madan ; Sarthak Bhagat ; Yi Yu ; Rajiv Ratn Shah
HIGHLIGHT:      In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder (VAE) without ground-truth answers.

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

79, TITLE:      City-Scale Road Extraction from Satellite Imagery v2: Road Speeds and Travel Times
http://arxiv.org/abs/1908.09715
AUTHORS:        Adam Van Etten
COMMENTS:       In Proceedings WACV 2020. 8 pages, 11 figures, 5 appendices. arXiv admin note: text overlap with arXiv:1904.09901
HIGHLIGHT:      We evaluate our method using two sources of labels (OpenStreetMap, and those from the SpaceNet dataset), and find that models both trained and tested on SpaceNet labels outperform OpenStreetMap labels by greater than 60%.

80, TITLE:      The Landscape of Matrix Factorization Revisited
http://arxiv.org/abs/2002.12795
AUTHORS:        Hossein Valavi ; Sulin Liu ; Peter J. Ramadge
COMMENTS:       19 pages
HIGHLIGHT:      We revisit the landscape of the simple matrix factorization problem.

81, TITLE:      Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN
http://arxiv.org/abs/1811.09245
AUTHORS:        Masaki Saito ; Shunta Saito ; Masanori Koyama ; Sosuke Kobayashi
COMMENTS:       Accepted at International Journal of Computer Vision. The source code is available at https://github.com/pfnet-research/tgan2
HIGHLIGHT:      In this study, we present a novel memory efficient method of unsupervised learning of high-resolution video dataset whose computational cost scales only linearly with the resolution.

82, TITLE:      Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
http://arxiv.org/abs/2003.13586
AUTHORS:        Heitor Felix ; Walber M. Rodrigues ; David Macêdo ; Francisco Simões ; Adriano L. I. Oliveira ; Veronica Teichrieb ; Cleber Zanchettin
COMMENTS:       This paper was accepted by 2020 International Joint Conference on Neural Networks (IJCNN)
HIGHLIGHT:      In this paper, we propose an approach to reduce the complexity of 6DoF detection networks while maintaining accuracy.

83, TITLE:      Improving Slot Filling by Utilizing Contextual Information
http://arxiv.org/abs/1911.01680
AUTHORS:        Amir Pouran Ben Veyseh ; Franck Dernoncourt ; Thien Huu Nguyen
COMMENTS:       Accepted at NLP4ConvAI Workshop at ACL2020
HIGHLIGHT:      To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i.e., representation level and task-specific (i.e., label) level.

84, TITLE:      Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
http://arxiv.org/abs/2005.14405
AUTHORS:        Komal Chugh ; Parul Gupta ; Abhinav Dhall ; Ramanathan Subramanian
HIGHLIGHT:      We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS).

85, TITLE:      Efficacy of Modern Neuro-Evolutionary Strategies for Continuous Control Optimization
http://arxiv.org/abs/1912.05239
AUTHORS:        Paolo Pagliuca ; Nicola Milano ; Stefano Nolfi
COMMENTS:       17 pages, 5 Figures, 4 Tables
HIGHLIGHT:      We analyze the efficacy of modern neuro-evolutionary strategies for continuous control optimization.

86, TITLE:      Practical Sized Typing for Coq
http://arxiv.org/abs/1912.05601
AUTHORS:        Jonathan Chan ; William J. Bowman
HIGHLIGHT:      In this paper, we present a design and implementation of sized type checking and inference for Coq.

87, TITLE:      On Value Functions and the Agent-Environment Boundary
http://arxiv.org/abs/1905.13341
AUTHORS:        Nan Jiang
COMMENTS:       16 pages
HIGHLIGHT:      We address this issue via a simple and novel boundary-invariant analysis of Fitted Q-Iteration, a representative RL algorithm, where the assumptions and the guarantees are invariant to the choice of boundary.

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

89, TITLE:      The State and Fate of Linguistic Diversity and Inclusion in the NLP World
http://arxiv.org/abs/2004.09095
AUTHORS:        Pratik Joshi ; Sebastin Santy ; Amar Budhiraja ; Kalika Bali ; Monojit Choudhury
COMMENTS:       Accepted at ACL 2020 (10 pages + 2 pages Appendix). P.J., S.S. and A.B. contributed equally
HIGHLIGHT:      In this paper we look at the relation between the types of languages, resources, and their representation in NLP conferences to understand the trajectory that different languages have followed over time.

90, 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.

91, TITLE:      Inexpensive Domain Adaptation of Pretrained Language Models: Case Studies on Biomedical NER and Covid-19 QA
http://arxiv.org/abs/2004.03354
AUTHORS:        Nina Poerner ; Ulli Waltinger ; Hinrich Schütze
HIGHLIGHT:      We cover over 50% of the BioBERT-BERT F1 delta, at 5% of BioBERT's CO_2 footprint and 2% of its cloud compute cost.

92, TITLE:      Learning Representative Temporal Features for Action Recognition
http://arxiv.org/abs/1802.06724
AUTHORS:        Ali Javidani ; Ahmad Mahmoudi-Aznaveh
COMMENTS:       14 pages
HIGHLIGHT:      In this paper, a novel video classification methodology is presented that aims to recognize different categories of third-person videos efficiently.