2020.05.07.txt

==========New Papers==========
1, TITLE:       Unsupervised Real-world Low-light Image Enhancement with Decoupled Networks
http://arxiv.org/abs/2005.02818
AUTHORS:        Wei Xiong ; Ding Liu ; Xiaohui Shen ; Chen Fang ; Jiebo Luo
COMMENTS:       17 pages
HIGHLIGHT:      In this paper, we address the real-world low-light image enhancement problem by decoupling this task into two sub-tasks: illumination enhancement and noise suppression.

2, TITLE:       Prediction of Human Empathy based on EEG Cortical Asymmetry
http://arxiv.org/abs/2005.02824
AUTHORS:        Andrea Kuijt ; Maryam Alimardani
COMMENTS:       Accepted in the 1st IEEE International Conference on Human-Machine Systems
HIGHLIGHT:      More specifically, we investigated the proposition of predicting self-reported human empathy based on EEG cortical asymmetry in different areas of the brain.

3, TITLE:       Manipulated Face Detector: Joint Spatial and Frequency Domain Attention Network
http://arxiv.org/abs/2005.02958
AUTHORS:        Zehao Chen ; Hua Yang
HIGHLIGHT:      In this paper, we propose a novel manipulated face detector, which is based on spatial and frequency domain combination and attention mechanism.

4, TITLE:       Multitask Models for Supervised Protests Detection in Texts
http://arxiv.org/abs/2005.02954
AUTHORS:        Benjamin J. Radford
HIGHLIGHT:      I apply multitask neural networks capable of producing predictions for two and three of these tasks simultaneously.

5, TITLE:       Generating Memorable Images Based on Human Visual Memory Schemas
http://arxiv.org/abs/2005.02969
AUTHORS:        Cameron Kyle-Davidson ; Adrian G. Bors ; Karla K. Evans
HIGHLIGHT:      This research study proposes using Generative Adversarial Networks (GAN) that incorporate a two-dimensional measure of human memorability to generate memorable or non-memorable images of scenes.

6, TITLE:       Seeing the Forest and the Trees: Detection and Cross-Document Coreference Resolution of Militarized Interstate Disputes
http://arxiv.org/abs/2005.02966
AUTHORS:        Benjamin J. Radford
HIGHLIGHT:      Previous efforts to automate the detection of social and political events in text have primarily focused on identifying events described within single sentences or documents. I provide a data set for evaluating methods to identify certain political events in text and to link related texts to one another based on shared events.

7, TITLE:       Towards the Role of Theory of Mind in Explanation
http://arxiv.org/abs/2005.02963
AUTHORS:        Maayan Shvo ; Toryn Q. Klassen ; Sheila A. McIlraith
HIGHLIGHT:      In this paper, we build and expand upon previous work by providing an account of explanation in terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations.

8, TITLE:       Learning to Understand Child-directed and Adult-directed Speech
http://arxiv.org/abs/2005.02721
AUTHORS:        Lieke Gelderloos ; Grzegorz Chrupała ; Afra Alishahi
COMMENTS:       ACL 2020
HIGHLIGHT:      This study explores the effect of child-directed speech when learning to extract semantic information from speech directly.

9, TITLE:       A Survey of Algorithms for Black-Box Safety Validation
http://arxiv.org/abs/2005.02979
AUTHORS:        Anthony Corso ; Robert J. Moss ; Mark Koren ; Ritchie Lee ; Mykel J. Kochenderfer
HIGHLIGHT:      We present and discuss algorithms in the domains of optimization, path planning, reinforcement learning, and importance sampling.

10, TITLE:      Probabilistic Color Constancy
http://arxiv.org/abs/2005.02730
AUTHORS:        Firas Laakom ; Jenni Raitoharju ; Alexandros Iosifidis ; Uygar Tuna ; Jarno Nikkanen ; Moncef Gabbouj
COMMENTS:       5 pages, 1 figure
HIGHLIGHT:      In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).

11, TITLE:      What are the Goals of Distributional Semantics?
http://arxiv.org/abs/2005.02982
AUTHORS:        Guy Emerson
COMMENTS:       To be published in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)
HIGHLIGHT:      In this paper, I take a broad linguistic perspective, looking at how well current models can deal with various semantic challenges.

12, TITLE:      MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models
http://arxiv.org/abs/2005.02507
AUTHORS:        Mandy Guo ; Yinfei Yang ; Daniel Cer ; Qinlan Shen ; Noah Constant
HIGHLIGHT:      This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets.

13, TITLE:      DenoiSeg: Joint Denoising and Segmentation
http://arxiv.org/abs/2005.02987
AUTHORS:        Tim-Oliver Buchholz ; Mangal Prakash ; Alexander Krull ; Florian Jug
COMMENTS:       10 pages, 4 figures, 2 pages supplement (4 figures)
HIGHLIGHT:      Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations.

14, TITLE:      Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning
http://arxiv.org/abs/2005.02503
AUTHORS:        Semih Yagli ; Alex Dytso ; H. Vincent Poor
COMMENTS:       Accepted for publication in Proceedings of 21st IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020. arXiv version is 10pt font, 6 Pages. This is the same document as the SPAWC version, except that the conference version is written with 9pt font to meet the strict page margin requirements
HIGHLIGHT:      The main objective of this work is to provide an information-theoretic framework for all of the aforementioned learning paradigms.

15, TITLE:      The More the Merrier?! Evaluating the Effect of Landmark Extraction Algorithms on Landmark-Based Goal Recognition
http://arxiv.org/abs/2005.02986
AUTHORS:        Kin Max Piamolini Gusmão ; Ramon Fraga Pereira ; Felipe Meneguzzi
COMMENTS:       This paper has been published at the AAAI 2020 workshop on Plan, Activity, and Intent Recognition (PAIR)
HIGHLIGHT:      In this paper, we investigate the impact and effect of using various landmark extraction algorithms capable of extracting a larger proportion of the landmarks for each given planning problem, up to exhaustive landmark extraction.

16, TITLE:      PeTra: A Sparsely Supervised Memory Model for People Tracking
http://arxiv.org/abs/2005.02990
AUTHORS:        Shubham Toshniwal ; Allyson Ettinger ; Kevin Gimpel ; Karen Livescu
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose PeTra, a memory-augmented neural network designed to track entities in its memory slots.

17, TITLE:      Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics
http://arxiv.org/abs/2005.02991
AUTHORS:        Guy Emerson
COMMENTS:       To be published in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)
HIGHLIGHT:      In this paper, I introduce the Pixie Autoencoder, which augments the generative model of Functional Distributional Semantics with a graph-convolutional neural network to perform amortised variational inference.

18, TITLE:      Phonetic and Visual Priors for Decipherment of Informal Romanization
http://arxiv.org/abs/2005.02517
AUTHORS:        Maria Ryskina ; Matthew R. Gormley ; Taylor Berg-Kirkpatrick
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion. Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments.

19, TITLE:      Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images
http://arxiv.org/abs/2005.02760
AUTHORS:        Alexander Prutsch ; Antonio Pepe ; Jan Egger
COMMENTS:       9 figures, 14 references
HIGHLIGHT:      By designing the tool as a web application, we simplify the usage of the neural network and reduce the initial learning curve.

20, TITLE:      ESG2Risk: A Deep Learning Framework from ESG News to Stock Volatility Prediction
http://arxiv.org/abs/2005.02527
AUTHORS:        Tian Guo ; Nicolas Jamet ; Valentin Betrix ; Louis-Alexandre Piquet ; Emmanuel Hauptmann
HIGHLIGHT:      In this paper, we focus on the ESG events in financial news flow and exploring the predictive power of ESG related financial news on stock volatility.

21, TITLE:      Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases
http://arxiv.org/abs/2005.02525
AUTHORS:        Henrique Lemos ; Pedro Avelar ; Marcelo Prates ; Luís Lamb ; Artur Garcez
COMMENTS:       Under review: ICANN 2020
HIGHLIGHT:      We propose a neural-symbolic graph neural network which applies learning over all the paths by feeding the model with the embedding of the minimal subset of the knowledge graph containing such paths.

22, TITLE:      Partly Supervised Multitask Learning
http://arxiv.org/abs/2005.02523
AUTHORS:        Abdullah-Al-Zubaer Imran ; Chao Huang ; Hui Tang ; Wei Fan ; Yuan Xiao ; Dingjun Hao ; Zhen Qian ; Demetri Terzopoulos
COMMENTS:       10 pages, 8 figures, 3 tables
HIGHLIGHT:      Leveraging self-supervision and adversarial training, we propose a novel general purpose semi-supervised, multiple-task model---namely, self-supervised, semi-supervised, multitask learning (S$^4$MTL)---for accomplishing two important tasks in medical imaging, segmentation and diagnostic classification.

23, TITLE:      Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency
http://arxiv.org/abs/2005.02530
AUTHORS:        Peyman Afshani ; Mark De Berg ; Kevin Buchin ; Jie Gao ; Maarten Loffler ; Amir Nayyeri ; Benjamin Raichel ; Rik Sarkar ; Haotian Wang ; Hao-Tsung Yang
COMMENTS:       Proceedings of the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR 20)
HIGHLIGHT:      We present a polynomial-time algorithm with an approximation factor of $O(k \log \frac{w_{max}}{w_{min}})$ to the optimal solution, where $w_{max}$ and $w_{min}$ are the maximum and minimum weight of the sites respectively.

24, TITLE:      Unsupervised Neural Aspect Search with Related Terms Extraction
http://arxiv.org/abs/2005.02771
AUTHORS:        Timur Sokhin ; Maria Khodorchenko ; Nikolay Butakov
HIGHLIGHT:      In this work, we present a novel unsupervised neural network with convolutional multi-attention mechanism, that allows extracting pairs (aspect, term) simultaneously, and demonstrate the effectiveness on the real-world dataset.

25, TITLE:      Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
http://arxiv.org/abs/2005.02539
AUTHORS:        Ahmed Elgohary ; Saghar Hosseini ; Ahmed Hassan Awadallah
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance.

26, TITLE:      The Cascade Transformer: an Application for Efficient Answer Sentence Selection
http://arxiv.org/abs/2005.02534
AUTHORS:        Luca Soldaini ; Alessandro Moschitti
COMMENTS:       Accepted to ACL 2020 (long)
HIGHLIGHT:      In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers.

27, TITLE:      Multi-Head Attention with Joint Agent-Map Representation for Trajectory Prediction in Autonomous Driving
http://arxiv.org/abs/2005.02545
AUTHORS:        Kaouther Messaoud ; Nachiket Deo ; Mohan M. Trivedi ; Fawzi Nashashibi
HIGHLIGHT:      Results on the publicly available nuScenes dataset prove that our model achieves the performance of existing methods and generates diverse possible future trajectories compliant with scene structure.

28, TITLE:      Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
http://arxiv.org/abs/2005.02552
AUTHORS:        Guanlin Li ; Shuya Ding ; Jun Luo ; Chang Liu
HIGHLIGHT:      In this paper, we propose an attack-agnostic defence framework to enhance the intrinsic robustness of neural networks, without jeopardizing the ability of generalizing clean samples.

29, TITLE:      Token Manipulation Generative Adversarial Network for Text Generation
http://arxiv.org/abs/2005.02794
AUTHORS:        DaeJin Jo
COMMENTS:       5 pages, 2 figures
HIGHLIGHT:      In this paper, we focus on addressing the limitations caused by having to specify blanks to be filled.

30, TITLE:      CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement
http://arxiv.org/abs/2005.02551
AUTHORS:        Ho Kei Cheng ; Jihoon Chung ; Yu-Wing Tai ; Chi-Keung Tang
COMMENTS:       Accepted to CVPR2020. Project page: https://github.com/hkchengrex/CascadePSP
HIGHLIGHT:      In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data.

31, TITLE:      UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving
http://arxiv.org/abs/2005.02790
AUTHORS:        Hao He ; Hengchen Dai ; Naiyan Wang
HIGHLIGHT:      We show that the proposed method substantially outperforms the previous state-of-the-art methods while maintaining its simplicity.

32, TITLE:      Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder
http://arxiv.org/abs/2005.02558
AUTHORS:        Desheng Wang ; Jiawei Liu ; Xiang Qi ; Baolin Sun ; Peng Zhang
HIGHLIGHT:      This work focuses on automatically generating regular expressions and proposes a novel genetic algorithm to deal with this problem.

33, TITLE:      An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Mining
http://arxiv.org/abs/2005.02799
AUTHORS:        Yifan Peng ; Qingyu Chen ; Zhiyong Lu
COMMENTS:       Accepted by BioNLP 2020
HIGHLIGHT:      In this work, we study a multi-task learning model with multiple decoders on varieties of biomedical and clinical natural language processing tasks such as text similarity, relation extraction, named entity recognition, and text inference.

34, TITLE:      Crossing Variational Autoencoders for Answer Retrieval
http://arxiv.org/abs/2005.02557
AUTHORS:        Wenhao Yu ; Lingfei Wu ; Qingkai Zeng ; Yu Deng ; Shu Tao ; Meng Jiang
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this work, we propose to cross variational auto-encoders by generating questions with aligned answers and generating answers with aligned questions.

35, TITLE:      A Model for Image Segmentation in Retina
http://arxiv.org/abs/2005.02567
AUTHORS:        Christopher Warner ; Friedrich T. Sommer
COMMENTS:       39 pages, 20 figures
HIGHLIGHT:      We propose a computational model for image segmentation consisting of a Kuramoto model of coupled oscillators whose phases model the timing of individual retinal spikes.

36, TITLE:      Active Preference-Based Gaussian Process Regression for Reward Learning
http://arxiv.org/abs/2005.02575
AUTHORS:        Erdem Bıyık ; Nicolas Huynh ; Mykel J. Kochenderfer ; Dorsa Sadigh
COMMENTS:       Proceedings of Robotics: Science and Systems (RSS), July 2020
HIGHLIGHT:      To address these challenges, we present a preference-based learning approach, where as an alternative, the human feedback is only of the form of comparisons between trajectories.

37, TITLE:      Probing the Natural Language Inference Task with Automated Reasoning Tools
http://arxiv.org/abs/2005.02573
AUTHORS:        Zaid Marji ; Animesh Nighojkar ; John Licato
COMMENTS:       Accepted to Proceedings of The 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-33, 2020)
HIGHLIGHT:      To improve performance, we develop a set of syntactic and semantic transformation rules.

38, TITLE:      Towards Concise, Machine-discovered Proofs of Gödel's Two Incompleteness Theorems
http://arxiv.org/abs/2005.02576
AUTHORS:        Elijah Malaby ; Bradley Dragun ; John Licato
HIGHLIGHT:      To facilitate this research, we present MATR, a new framework for automated theorem proving explicitly designed to easily adapt to unusual logics or integrate new reasoning processes.

39, TITLE:      Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns
http://arxiv.org/abs/2005.02589
AUTHORS:        Anirudh Som ; Narayanan Krishnamurthi ; Matthew Buman ; Pavan Turaga
COMMENTS:       Accepted in the 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)
HIGHLIGHT:      In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions.

40, TITLE:      Online Parameter Estimation for Human Driver Behavior Prediction
http://arxiv.org/abs/2005.02597
AUTHORS:        Raunak Bhattacharyya ; Ransalu Senanayake ; Kyle Brown ; Mykel Kochenderfer
COMMENTS:       Accepted to the 2020 American Control Conference (ACC). 6 pages, 6 figures
HIGHLIGHT:      In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories.

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

42, TITLE:      Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders
http://arxiv.org/abs/2005.02590
AUTHORS:        Terra Blevins ; Luke Zettlemoyer
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense.

43, TITLE:      Exploiting Inter-Frame Regional Correlation for Efficient Action Recognition
http://arxiv.org/abs/2005.02591
AUTHORS:        Yuecong Xu ; Jianfei Yang ; Kezhi Mao ; Jianxiong Yin ; Simon See
COMMENTS:       24 pages (exclude reference), 7 figures, 4 tables
HIGHLIGHT:      In this paper, we propose a novel temporal feature extraction method, named Attentive Correlated Temporal Feature (ACTF), by exploring inter-frame correlation within a certain region.

44, TITLE:      Automatic Detection and Recognition of Individuals in Patterned Species
http://arxiv.org/abs/2005.02905
AUTHORS:        Gullal Singh Cheema ; Saket Anand
COMMENTS:       12 pages, ECML-PKDD 2017
HIGHLIGHT:      In this work, we develop a framework for automatic detection and recognition of individuals in different patterned species like tigers, zebras and jaguars.

45, TITLE:      High-Contrast Limited-Angle Reflection Tomography
http://arxiv.org/abs/2005.02903
AUTHORS:        Ajinkya Kadu ; Hassan Mansour ; Petros T. Boufounos
HIGHLIGHT:      In this paper, we consider a limited-angle reflection tomography of high contrast objects that commonly occurs in ground-penetrating radar, exploration geophysics, terahertz imaging, ultrasound, and electron microscopy.

46, TITLE:      Review of text style transfer based on deep learning
http://arxiv.org/abs/2005.02914
AUTHORS:        Xiangyang Li ; Guo Pu ; Keyu Ming ; Pu Li ; Jie Wang ; Yuxuan Wang ; Sujian Li
HIGHLIGHT:      This article summarizes the research on the text style transfer model based on deep learning in recent years, and summarizes, analyzes and compares the main research directions and progress. In addition, the article also introduces public data sets and evaluation indicators commonly used for text style transfer.

47, TITLE:      Harvesting and Refining Question-Answer Pairs for Unsupervised QA
http://arxiv.org/abs/2005.02925
AUTHORS:        Zhongli Li ; Wenhui Wang ; Li Dong ; Furu Wei ; Ke Xu
COMMENTS:       Accepted by ACL-20
HIGHLIGHT:      In this work, we introduce two approaches to improve unsupervised QA.

48, TITLE:      GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
http://arxiv.org/abs/2005.02936
AUTHORS:        Ankita Shukla ; Pavan Turaga ; Saket Anand
COMMENTS:       16 pages
HIGHLIGHT:      In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation.

49, TITLE:      Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization
http://arxiv.org/abs/2005.02934
AUTHORS:        Pierre-Alexandre Kamienny ; Matteo Pirotta ; Alessandro Lazaric ; Thibault Lavril ; Nicolas Usunier ; Ludovic Denoyer
COMMENTS:       18 pages
HIGHLIGHT:      We propose a novel algorithm that regularizes the training of an RNN-based policy using informed policies trained to maximize the reward in each task.

50, TITLE:      Groupwise Multimodal Image Registration using Joint Total Variation
http://arxiv.org/abs/2005.02933
AUTHORS:        Mikael Brudfors ; Yaël Balbastre ; John Ashburner
HIGHLIGHT:      In this paper, we introduce a cost function based on joint total variation for such multimodal image registration.

51, TITLE:      Knee Injury Detection using MRI with Efficiently-Layered Network (ELNet)
http://arxiv.org/abs/2005.02706
AUTHORS:        Chen-Han Tsai ; Nahum Kiryati ; Eli Konen ; Iris Eshed ; Arnaldo Mayer
COMMENTS:       To be published in the Proceedings of Machine Learning Research (PMLR) 2020
HIGHLIGHT:      In this work, we present the Efficiently-Layered Network (ELNet), a convolutional neural network (CNN) architecture optimized for the task of initial knee MRI diagnosis for triage.

52, TITLE:      Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data
http://arxiv.org/abs/2005.02704
AUTHORS:        Aritra Mukherjee ; Sourya Dipta Das ; Jasorsi Ghosh ; Ananda S. Chowdhury ; Sanjoy Kumar Saha
COMMENTS:       Accepted to PReMI 2019( Pattern Recognition and Machine Intelligence 2019 )
HIGHLIGHT:      In this paper, a methodology is presented to generate the segmented surfaces in real time and these can be used in modeling the 3D objects.

53, TITLE:      Optimal Covid-19 Pool Testing with a priori Information
http://arxiv.org/abs/2005.02940
AUTHORS:        Marc Beunardeau ; Éric Brier ; Noémie Cartier ; Aisling Connolly ; Nathanaël Courant ; Rémi Géraud-Stewart ; David Naccache ; Ofer Yifrach-Stav
HIGHLIGHT:      This paper describes how to optimally detect infected patients in pools, i.e. using a minimal number of tests to precisely identify them, given the a priori probabilities that each of the patients is healthy.

54, TITLE:      TAG : Type Auxiliary Guiding for Code Comment Generation
http://arxiv.org/abs/2005.02835
AUTHORS:        Ruichu Cai ; Zhihao Liang ; Boyan Xu ; Zijian Li ; Yuexing Hao ; Yao Chen
COMMENTS:       ACL 2020, Accepted
HIGHLIGHT:      In order to address the issues above, we propose a Type Auxiliary Guiding encoder-decoder framework for the code comment generation task which considers the source code as an N-ary tree with type information associated with each node.

55, TITLE:      Towards quantum advantage for topological data analysis
http://arxiv.org/abs/2005.02607
AUTHORS:        Casper Gyurik ; Chris Cade ; Vedran Dunjko
COMMENTS:       21 pages, 1 figure
HIGHLIGHT:      In this paper, we study the quantum algorithm for topological data analysis by Lloyd, Garnerone and Zanardi (LGZ).

56, TITLE:      Gradual Relation Network: Decoding Intuitive Upper Extremity Movement Imaginations Based on Few-Shot EEG Learning
http://arxiv.org/abs/2005.02602
AUTHORS:        Kyung-Hwan Shim ; Ji-Hoon Jeong ; Seong-Whan Lee
HIGHLIGHT:      To avoid this problem, we adopt the metric based few-shot learning approach for decoding intuitive upper-extremity movement imagination (MI) using a gradual relation network (GRN) that can gradually consider the combination of temporal and spectral groups.

57, TITLE:      Graph-Embedding Empowered Entity Retrieval
http://arxiv.org/abs/2005.02843
AUTHORS:        Emma J. Gerritse ; Faegheh Hasibi ; Arjen P. de Vries
HIGHLIGHT:      In this research, we improve upon the current state of the art in entity retrieval by re-ranking the result list using graph embeddings.

58, TITLE:      Vehicle Routing and Scheduling for Regular Mobile Healthcare Services
http://arxiv.org/abs/2005.02618
AUTHORS:        Cosmin Pascaru ; Paul Diac
COMMENTS:       International Conference on Tools with Artificial Intelligence (ICTAI) 8 pages 1 figure
HIGHLIGHT:      We propose our solution to a particular practical problem in the domain of vehicle routing and scheduling.

59, TITLE:      Building A User-Centric and Content-Driven Socialbot
http://arxiv.org/abs/2005.02623
AUTHORS:        Hao Fang
COMMENTS:       PhD thesis
HIGHLIGHT:      To build Sounding Board, we develop a system architecture that is capable of accommodating dialog strategies that we designed for socialbot conversations.

60, TITLE:      Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction
http://arxiv.org/abs/2005.02870
AUTHORS:        Toshiaki Koike-Akino ; Ye Wang
COMMENTS:       14 pages, 12 figures, ISIT 2020 accepted
HIGHLIGHT:      We propose a new concept of rateless auto-encoders (RL-AEs) that enable a flexible latent dimensionality, which can be seamlessly adjusted for varying distortion and dimensionality requirements.

61, TITLE:      TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
http://arxiv.org/abs/2005.02877
AUTHORS:        Michael Heck ; Carel van Niekerk ; Nurul Lubis ; Christian Geishauser ; Hsien-Chin Lin ; Marco Moresi ; Milica Gašić
COMMENTS:       10 pages, 6 figures, to be published in Proceedings of the 21st Annual SIGdial Meeting on Discourse and Dialogue
HIGHLIGHT:      In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values.

62, TITLE:      Multi-Resolution POMDP Planning for Multi-Object Search in 3D
http://arxiv.org/abs/2005.02878
AUTHORS:        Kaiyu Zheng ; Yoonchang Sung ; George Konidaris ; Stefanie Tellex
COMMENTS:       13 pages, 5 figures, 4 tables
HIGHLIGHT:      We propose a new approach that enables the robot to efficiently search for objects in 3D, taking occlusions into account.

63, TITLE:      Dependency Aware Filter Pruning
http://arxiv.org/abs/2005.02634
AUTHORS:        Kai Zhao ; Xin-Yu Zhang ; Qi Han ; Ming-Ming Cheng
HIGHLIGHT:      Besides, we propose a novel mechanism to dynamically control the sparsity-inducing regularization so as to achieve the desired sparsity.

64, TITLE:      Robotic Arm Control and Task Training through Deep Reinforcement Learning
http://arxiv.org/abs/2005.02632
AUTHORS:        Andrea Franceschetti ; Elisa Tosello ; Nicola Castaman ; Stefano Ghidoni
COMMENTS:       Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2018
HIGHLIGHT:      This paper proposes a detailed and extensive comparison of the Trust Region Policy Optimization and DeepQ-Network with Normalized Advantage Functions with respect to other state of the art algorithms, namely Deep Deterministic Policy Gradient and Vanilla Policy Gradient.

65, TITLE:      Low-shot Object Detection via Classification Refinement
http://arxiv.org/abs/2005.02641
AUTHORS:        Yiting Li ; Yu Cheng ; Lu Liu ; Sichao Tian ; Haiyue Zhu ; Cheng Xiang ; Prahlad Vadakkepat ; Cheksing Teo ; Tongheng Lee
COMMENTS:       Submitted to NIPS2020
HIGHLIGHT:      In this paper, we propose a novel low-shot classification correction network (LSCN) which can be adopted into any anchor-based detector to directly enhance the detection accuracy on data-rare categories, without sacrificing the performance on base categories.

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

67, TITLE:      Search for developments of a box having multiple ways of folding by SAT solver
http://arxiv.org/abs/2005.02645
AUTHORS:        Riona Tadaki ; Kazuyuki Amano
COMMENTS:       12 pages
HIGHLIGHT:      In this work, we conducted a computer search for finding such developments by using a SAT solver.

68, TITLE:      Deep Recurrent Disease Progression Model for Conversion-Time Prediction of Alzheimer's Disease
http://arxiv.org/abs/2005.02643
AUTHORS:        Wonsik Jung ; Eunji Jun ; Heung-Il Suk
COMMENTS:       30 pages, 12 figures
HIGHLIGHT:      Under the same problem settings, in this work, we propose a novel computational framework that forecasts the phenotypic measurements of MRI biomarkers and predicts the clinical statuses at multiple future time points.

69, TITLE:      Towards Building Knowledge by Merging Multiple Ontologies with CoMerger: A Partitioning-based Approach
http://arxiv.org/abs/2005.02659
AUTHORS:        Samira Babalou ; Birgitta König-Ries
COMMENTS:       A further improved version of this paper will be submitted to the International Semantic Web Conference (ISWC) 2020 conference. The paper has 23 pages including appendix and 7 figures
HIGHLIGHT:      We present CoMerger, a scalable multiple ontologies merging method.

70, TITLE:      Spoofing Linear Cross-Entropy Benchmarking in Shallow Quantum Circuits
http://arxiv.org/abs/2005.02421
AUTHORS:        Boaz Barak ; Chi-Ning Chou ; Xun Gao
HIGHLIGHT:      In this work we give a classical randomized algorithm that for a given circuit $C$ of depth $d$ with Haar random 2-qubit gates achieves in expectation a fidelity value of $\Omega(\tfrac{n}{L} \cdot 15^{-d})$ in running time $\textsf{poly}(n,2^L)$.

71, TITLE:      Automated Transcription for Pre-Modern Japanese Kuzushiji Documents by Random Lines Erasure and Curriculum Learning
http://arxiv.org/abs/2005.02669
AUTHORS:        Anh Duc Le
HIGHLIGHT:      For the lack of training data, we propose a random text line erasure approach that randomly erases text lines and distorts documents.

72, TITLE:      Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
http://arxiv.org/abs/2005.02666
AUTHORS:        Tim Cofala ; Lars Elend ; Philip Mirbach ; Jonas Prellberg ; Thomas Teusch ; Oliver Kramer
COMMENTS:       15 pages, 7 figures, submitted to PPSN 2020
HIGHLIGHT:      We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease.

73, TITLE:      Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System
http://arxiv.org/abs/2005.02431
AUTHORS:        Ekaterina Kochmar ; Dung Do Vu ; Robert Belfer ; Varun Gupta ; Iulian Vlad Serban ; Joelle Pineau
COMMENTS:       Artificial Intelligence for Education 2020 (AIED)
HIGHLIGHT:      We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account.

74, TITLE:      CONFIG: Controllable Neural Face Image Generation
http://arxiv.org/abs/2005.02671
AUTHORS:        Marek Kowalski ; Stephan J. Garbin ; Virginia Estellers ; Tadas Baltrušaitis ; Matthew Johnson ; Jamie Shotton
COMMENTS:       includes supplementary materials
HIGHLIGHT:      To this end we propose ConfigNet, a neural face model that allows for controlling individual aspects of output images in semantically meaningful ways and that is a significant step on the path towards finely-controllable neural rendering.

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

76, TITLE:      Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery
http://arxiv.org/abs/2005.02436
AUTHORS:        Hiroshi Sasaki ; Chris G. Willcocks ; Toby P. Breckon
COMMENTS:       9 pages, 9 figures
HIGHLIGHT:      To address this problem, this paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery via a generative domain transfer model.

77, TITLE:      A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
http://arxiv.org/abs/2005.02680
AUTHORS:        Longyin Zhang ; Yuqing Xing ; Fang Kong ; Peifeng Li ; Guodong Zhou
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS parsing.

78, TITLE:      Drosophila-Inspired 3D Moving Object Detection Based on Point Clouds
http://arxiv.org/abs/2005.02696
AUTHORS:        Li Wang ; Dawei Zhao ; Tao Wu ; Hao Fu ; Zhiyu Wang ; Liang Xiao ; Xin Xu ; Bin Dai
HIGHLIGHT:      In this paper, we propose a novel Drosophila-inspired 3D moving object detection method using Lidar sensors.

79, TITLE:      Shape of synth to come: Why we should use synthetic data for English surface realization
http://arxiv.org/abs/2005.02693
AUTHORS:        Henry Elder ; Robert Burke ; Alexander O'Connor ; Jennifer Foster
HIGHLIGHT:      The Surface Realization Shared Tasks of 2018 and 2019 were Natural Language Generation shared tasks with the goal of exploring approaches to surface realization from Universal-Dependency-like trees to surface strings for several languages.

80, TITLE:      Iris segmentation techniques to recognize the behavior of a vigilant driver
http://arxiv.org/abs/2005.02450
AUTHORS:        Abdullatif Baba
HIGHLIGHT:      In this paper, we clarify how to recognize different levels of vigilance for vehicle drivers.

81, TITLE:      Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia
http://arxiv.org/abs/2005.02690
AUTHORS:        Xi Ouyang ; Jiayu Huo ; Liming Xia ; Fei Shan ; Jun Liu ; Zhanhao Mo ; Fuhua Yan ; Zhongxiang Ding ; Qi Yang ; Bin Song ; Feng Shi ; Huan Yuan ; Ying Wei ; Xiaohuan Cao ; Yaozong Gao ; Dijia Wu ; Qian Wang ; Dinggang Shen
HIGHLIGHT:      In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses.

82, TITLE:      ProbaNet: Proposal-balanced Network for Object Detection
http://arxiv.org/abs/2005.02699
AUTHORS:        Xiang Zhang ; Jing Wu ; Mingyi Zhou
HIGHLIGHT:      In this study, we propose a Proposal-balanced Network (ProbaNet) for alleviating the imbalance problem.

83, TITLE:      Temporal Event Segmentation using Attention-based Perceptual Prediction Model for Continual Learning
http://arxiv.org/abs/2005.02463
AUTHORS:        Ramy Mounir ; Roman Gula ; Jörn Theuerkauf ; Sudeep Sarkar
HIGHLIGHT:      In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames.

84, TITLE:      A new design of a flying robot, with advanced computer vision techniques to perform self-maintenance of smart grids
http://arxiv.org/abs/2005.02460
AUTHORS:        Abdullatif Baba
HIGHLIGHT:      In this paper, we present a full design of a flying robot to investigate the state of power grid components and to perform the appropriate maintenance procedures according to each fail or defect that could be recognized.

85, TITLE:      Efficient strategies for hierarchical text classification: External knowledge and auxiliary tasks
http://arxiv.org/abs/2005.02473
AUTHORS:        Kervy Rivas Rojas ; Gina Bustamante ; Marco A. Sobrevilla Cabezudo ; Arturo Oncevay
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy.

86, TITLE:      Cross-media Structured Common Space for Multimedia Event Extraction
http://arxiv.org/abs/2005.02472
AUTHORS:        Manling Li ; Alireza Zareian ; Qi Zeng ; Spencer Whitehead ; Di Lu ; Heng Ji ; Shih-Fu Chang
COMMENTS:       Accepted as an oral paper at ACL 2020
HIGHLIGHT:      We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments.

87, TITLE:      Russian Natural Language Generation: Creation of a Language Modelling Dataset and Evaluation with Modern Neural Architectures
http://arxiv.org/abs/2005.02470
AUTHORS:        Zein Shaheen ; Gerhard Wohlgenannt ; Bassel Zaity ; Dmitry Mouromtsev ; Vadim Pak
HIGHLIGHT:      In this work, we i) provide a novel reference dataset for Russian language modeling, ii) experiment with popular modern methods for text generation, namely variational autoencoders, and generative adversarial networks, which we trained on the new dataset.

88, TITLE:      A Ladder of Causal Distances
http://arxiv.org/abs/2005.02480
AUTHORS:        Maxime Peyrard ; Robert West
COMMENTS:       13 pages, 8 figures
HIGHLIGHT:      Following this organization, we introduce a hierarchy of three distances, one for each rung of the ladder.

89, TITLE:      Mimicry: Towards the Reproducibility of GAN Research
http://arxiv.org/abs/2005.02494
AUTHORS:        Kwot Sin Lee ; Christopher Town
COMMENTS:       Accepted to the AI for Content Creation Workshop at CVPR 2020
HIGHLIGHT:      To mitigate these issues, we introduce Mimicry, a lightweight PyTorch library that provides implementations of popular state-of-the-art GANs and evaluation metrics to closely reproduce reported scores in the literature.



==========Updates to Previous Papers==========
1, TITLE:       Improving Semantic Segmentation via Self-Training
http://arxiv.org/abs/2004.14960
AUTHORS:        Yi Zhu ; Zhongyue Zhang ; Chongruo Wu ; Zhi Zhang ; Tong He ; Hang Zhang ; R. Manmatha ; Mu Li ; Alexander Smola
HIGHLIGHT:      In this paper, we show that we can obtain state-of-the-art results using a semi-supervised approach, specifically a self-training paradigm.

2, TITLE:       A new Taxonomy of Continuous Global Optimization Algorithms
http://arxiv.org/abs/1808.08818
AUTHORS:        Jörg Stork ; A. E. Eiben ; Thomas Bartz-Beielstein
COMMENTS:       35 pages total, 28 written pages, 4 figures, 2019 Reworked Version
HIGHLIGHT:      This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies.

3, TITLE:       Big Transfer (BiT): General Visual Representation Learning
http://arxiv.org/abs/1912.11370
AUTHORS:        Alexander Kolesnikov ; Lucas Beyer ; Xiaohua Zhai ; Joan Puigcerver ; Jessica Yung ; Sylvain Gelly ; Neil Houlsby
COMMENTS:       The first three authors contributed equally. Results on ObjectNet are reported in v3
HIGHLIGHT:      We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT).

4, TITLE:       Forecasting future action sequences with attention: a new approach to weakly supervised action forecasting
http://arxiv.org/abs/1912.04608
AUTHORS:        Yan Bin Ng ; Basura Fernando
HIGHLIGHT:      We present a method to forecast actions for the unseen future of the video using a neural machine translation technique that uses encoder-decoder architecture.

5, TITLE:       Complexity of Stability
http://arxiv.org/abs/1910.00305
AUTHORS:        Fabian Frei ; Edith Hemaspaandra ; Jörg Rothe
HIGHLIGHT:      We initiate the study of stability of graphs for such parameters in terms of their computational complexity.

6, TITLE:       Quantum Distributed Complexity of Set Disjointness on a Line
http://arxiv.org/abs/2002.11795
AUTHORS:        Frederic Magniez ; Ashwin Nayak
COMMENTS:       20 pages, 2 figures. More details in an appendix
HIGHLIGHT:      In this work, we prove an unconditional lower bound of $\widetilde{\Omega}(\sqrt[3]{n d^2}+\sqrt{n} )$ rounds for Set Disjointness on a Line.

7, TITLE:       Encoders Help You Disambiguate Word Senses in Neural Machine Translation
http://arxiv.org/abs/1908.11771
AUTHORS:        Gongbo Tang ; Rico Sennrich ; Joakim Nivre
COMMENTS:       Update with corrections. Here is the link to the erratum: https://www.aclweb.org/anthology/D19-1149e1.pdf
HIGHLIGHT:      In this paper, we explore the ability of NMT encoders and decoders to disambiguate word senses by evaluating hidden states and investigating the distributions of self-attention.

8, TITLE:       Neural translation and automated recognition of ICD10 medical entities from natural language
http://arxiv.org/abs/2004.13839
AUTHORS:        Louis Falissard ; Claire Morgand ; Sylvie Roussel ; Claire Imbaud ; Walid Ghosn ; Karim Bounebache ; Grégoire Rey
HIGHLIGHT:      This article investigates the applications of deep neural sequence models to the medical entity recognition from natural language problem.

9, TITLE:       Generative Memorize-Then-Recall framework for low bit-rate Surveillance Video Compression
http://arxiv.org/abs/1912.12847
AUTHORS:        Yaojun Wu ; Tianyu He ; Zhibo Chen
COMMENTS:       11 pages, 8 figures
HIGHLIGHT:      In this paper, we figure out this issue by disentangling surveillance video into the structure of a global spatio-temporal feature (memory) for Group of Picture (GoP) and skeleton for each frame (clue).

10, TITLE:      Differentiable Adaptive Computation Time for Visual Reasoning
http://arxiv.org/abs/2004.12770
AUTHORS:        Cristobal Eyzaguirre ; Alvaro Soto
COMMENTS:       CVPR 2020
HIGHLIGHT:      This paper presents a novel attention-based algorithm for achieving adaptive computation called DACT, which, unlike existing ones, is end-to-end differentiable.

11, TITLE:      Modified swarm-based metaheuristics enhance Gradient Descent initialization performance: Application for EEG spatial filtering
http://arxiv.org/abs/1907.08220
AUTHORS:        Mojtaba Moattari ; Mohammad Hassan Moradi ; Reza Boostani
COMMENTS:       10 tables, 32 references, 11 formulas. arXiv admin note: text overlap with arXiv:1209.4115 by other authors
HIGHLIGHT:      In this paper, Swarm-based optimizers like ICA and PSO are modified by a new optimization framework to improve GD optimization performance.

12, TITLE:      Efficient Bird Eye View Proposals for 3D Siamese Tracking
http://arxiv.org/abs/1903.10168
AUTHORS:        Jesus Zarzar ; Silvio Giancola ; Bernard Ghanem
HIGHLIGHT:      In this paper, we leverage the dense and structured Bird Eye View (BEV) representation of LIDAR point clouds to efficiently search for objects of interest.

13, TITLE:      Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
http://arxiv.org/abs/2005.01632
AUTHORS:        Jun Hayakawa ; Behzad Dariush
HIGHLIGHT:      In this paper, we propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera.

14, TITLE:      Calibrating Structured Output Predictors for Natural Language Processing
http://arxiv.org/abs/2004.04361
AUTHORS:        Abhyuday Jagannatha ; Hong Yu
COMMENTS:       ACL 2020; 9 pages + 4 page appendix
HIGHLIGHT:      In this study, we propose a general calibration scheme for output entities of interest in neural-network based structured prediction models.

15, TITLE:      A detailed comparative study of open source deep learning frameworks
http://arxiv.org/abs/1903.00102
AUTHORS:        Ghadeer Al-Bdour ; Raffi Al-Qurran ; Mahmoud Al-Ayyoub ; Ali Shatnawi
COMMENTS:       26 pages, 25 figures, 4 tables
HIGHLIGHT:      The purpose of this work is to provide a qualitative and quantitative comparison among three of the most popular and most comprehensive DL frameworks (namely Google's TensorFlow, University of Montreal's Theano and Microsoft's CNTK).

16, TITLE:      Fine-grained Financial Opinion Mining: A Survey and Research Agenda
http://arxiv.org/abs/2005.01897
AUTHORS:        Chung-Chi Chen ; Hen-Hsen Huang ; Hsin-Hsi Chen
HIGHLIGHT:      Fine-grained Financial Opinion Mining: A Survey and Research Agenda

17, TITLE:      Tensor completion using enhanced multiple modes low-rank prior and total variation
http://arxiv.org/abs/2004.08747
AUTHORS:        Haijin Zeng ; Xiaozhen Xie ; Jifeng Ning
HIGHLIGHT:      In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor.

18, TITLE:      Color Constancy by Reweighting Image Feature Maps
http://arxiv.org/abs/1806.09248
AUTHORS:        Jueqin Qiu ; Haisong Xu ; Zhengnan Ye
HIGHLIGHT:      In this study, a novel illuminant color estimation framework is proposed for computational color constancy, which incorporates the high representational capacity of deep-learning-based models and the great interpretability of assumption-based models.

19, TITLE:      Seeing voices and hearing voices: learning discriminative embeddings using cross-modal self-supervision
http://arxiv.org/abs/2004.14326
AUTHORS:        Soo-Whan Chung ; Hong Goo Kang ; Joon Son Chung
COMMENTS:       Under submission as a conference paper
HIGHLIGHT:      The goal of this work is to train discriminative cross-modal embeddings without access to manually annotated data.

20, TITLE:      Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training
http://arxiv.org/abs/2004.14565
AUTHORS:        Chenguang Zhu
COMMENTS:       SIGDial 2020
HIGHLIGHT:      We propose to integrate adversarial training to produce more human-like responses.

21, TITLE:      WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis
http://arxiv.org/abs/1911.07925
AUTHORS:        Tianfu Li ; Zhibin Zhao ; Chuang Sun ; Li Cheng ; Xuefeng Chen ; Ruqiang Yan ; Robert X. Gao
HIGHLIGHT:      In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN.

22, TITLE:      Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
http://arxiv.org/abs/2005.00165
AUTHORS:        Forrest Davis ; Marten van Schijndel
COMMENTS:       Proceedings of 58th Annual Meeting of the Association for Computational Linguistics; v2 updated references and added additional corpus stats in discussion
HIGHLIGHT:      A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence).

23, TITLE:      SLEDGE: A Simple Yet Effective Baseline for Coronavirus Scientific Knowledge Search
http://arxiv.org/abs/2005.02365
AUTHORS:        Sean MacAvaney ; Arman Cohan ; Nazli Goharian
HIGHLIGHT:      In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles.

24, TITLE:      CODA-19: Reliably Annotating Research Aspects on 10,000+ CORD-19 Abstracts Using Non-Expert Crowd
http://arxiv.org/abs/2005.02367
AUTHORS:        Ting-Hao 'Kenneth' Huang ; Chieh-Yang Huang ; Chien-Kuang Cornelia Ding ; Yen-Chia Hsu ; C. Lee Giles
COMMENTS:       CODA-19: COVID-19 Research Aspect Dataset: https://github.com/windx0303/CODA-19
HIGHLIGHT:      This paper introduces CODA-19, a human-annotated dataset that denotes the Background, Purpose, Method, Finding/Contribution, and Other for 10,966 English abstracts in the COVID-19 Open Research Dataset.

25, TITLE:      Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models
http://arxiv.org/abs/1908.06725
AUTHORS:        Zhi-Xiu Ye ; Qian Chen ; Wen Wang ; Zhen-Hua Ling
HIGHLIGHT:      We propose a pre-training approach for incorporating commonsense knowledge into language representation models.

26, TITLE:      Vehicle Re-identification: exploring feature fusion using multi-stream convolutional networks
http://arxiv.org/abs/1911.05541
AUTHORS:        Icaro O. de Oliveira ; Rayson Laroca ; David Menotti ; Keiko V. O. Fonseca ; Rodrigo Minetto
HIGHLIGHT:      As our main contribution, we propose a novel two-stream convolutional neural network (CNN) that simultaneously uses two of the most distinctive and persistent features available: the vehicle appearance and its license plate. As part of this work, we created an important dataset for vehicle re-identification with more than three hours of videos spanning almost 3,000 vehicles.

27, TITLE:      Unity: A General Platform for Intelligent Agents
http://arxiv.org/abs/1809.02627
AUTHORS:        Arthur Juliani ; Vincent-Pierre Berges ; Ervin Teng ; Andrew Cohen ; Jonathan Harper ; Chris Elion ; Chris Goy ; Yuan Gao ; Hunter Henry ; Marwan Mattar ; Danny Lange
HIGHLIGHT:      In this work, we propose a novel taxonomy of existing simulation platforms and discuss the highest level class of general platforms which enable the development of learning environments that are rich in visual, physical, task, and social complexity.

28, TITLE:      MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten Compound Characters
http://arxiv.org/abs/2005.02155
AUTHORS:        Jannatul Ferdous ; Suvrajit Karmaker ; A K M Shahariar Azad Rabby ; Syed Akhter Hossain
COMMENTS:       19 fig, 2 table
HIGHLIGHT:      It is intended to frame the acknowledgment technique for handwritten Bangla compound character.

29, TITLE:      NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
http://arxiv.org/abs/2005.02291
AUTHORS:        Dario Fuoli ; Zhiwu Huang ; Martin Danelljan ; Radu Timofte ; Hua Wang ; Longcun Jin ; Dewei Su ; Jing Liu ; Jaehoon Lee ; Michal Kudelski ; Lukasz Bala ; Dmitry Hrybov ; Marcin Mozejko ; Muchen Li ; Siyao Li ; Bo Pang ; Cewu Lu ; Chao Li ; Dongliang He ; Fu Li ; Shilei Wen
COMMENTS:       The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
HIGHLIGHT:      This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain.

30, TITLE:      Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimation
http://arxiv.org/abs/2004.09048
AUTHORS:        Oladapo Afolabi ; Allen Y. Yang ; S. Shankar Sastry
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we present a formulation that overcomes this issue by jointly estimating the shape and similarity transformation parameters.

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

32, TITLE:      Introducing a framework to assess newly created questions with Natural Language Processing
http://arxiv.org/abs/2004.13530
AUTHORS:        Luca Benedetto ; Andrea Cappelli ; Roberto Turrin ; Paolo Cremonesi
COMMENTS:       Accepted at the International Conference of Artificial Intelligence in Education
HIGHLIGHT:      In this paper, we propose a framework to train and evaluate models for estimating the difficulty and discrimination of newly created Multiple Choice Questions by extracting meaningful features from the text of the question and of the possible choices.

33, TITLE:      Mask Encoding for Single Shot Instance Segmentation
http://arxiv.org/abs/2003.11712
AUTHORS:        Rufeng Zhang ; Zhi Tian ; Chunhua Shen ; Mingyu You ; Youliang Yan
COMMENTS:       Accepted to Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020
HIGHLIGHT:      In this work, we propose a simple singleshot instance segmentation framework, termed mask encoding based instance segmentation (MEInst).

34, TITLE:      Pedestrian Detection: The Elephant In The Room
http://arxiv.org/abs/2003.08799
AUTHORS:        Irtiza Hasan ; Shengcai Liao ; Jinpeng Li ; Saad Ullah Akram ; Ling Shao
COMMENTS:       17 pages, 1 figure
HIGHLIGHT:      To this end, we conduct a comprehensive study in this paper, using a general principle of direct cross-dataset evaluation.

35, TITLE:      Bayesian Entailment Hypothesis: How Brains Implement Monotonic and Non-monotonic Reasoning
http://arxiv.org/abs/2005.00961
AUTHORS:        Hiroyuki Kido
COMMENTS:       7 pages, 3 figures
HIGHLIGHT:      In this paper, we give a Bayesian account of entailment and characterize its abstract inferential properties.

36, TITLE:      Entity Type Prediction in Knowledge Graphs using Embeddings
http://arxiv.org/abs/2004.13702
AUTHORS:        Russa Biswas ; Radina Sofronova ; Mehwish Alam ; Harald Sack
HIGHLIGHT:      To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings.

37, TITLE:      Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning
http://arxiv.org/abs/1910.01723
AUTHORS:        Kolby Nottingham ; Anand Balakrishnan ; Jyotirmoy Deshmukh ; Connor Christopherson ; Joshua Greaves ; David Wingate
HIGHLIGHT:      In this work, we explore the use of a language based on propositional logic with quantitative semantics--in place of weight vectors--for specifying non-linear behaviors in an interpretable way.

38, TITLE:      Knowledge Federation: A Unified and Hierarchical Privacy-Preserving AI Framework
http://arxiv.org/abs/2002.01647
AUTHORS:        Hongyu Li ; Dan Meng ; Hong Wang ; Xiaolin Li
HIGHLIGHT:      To advance AI theories and applications, we propose a comprehensive framework (called Knowledge Federation - KF) to address these challenges by enabling AI while preserving data privacy and ownership.

39, TITLE:      Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs
http://arxiv.org/abs/1911.02707
AUTHORS:        Houyu Zhang ; Zhenghao Liu ; Chenyan Xiong ; Zhiyuan Liu
HIGHLIGHT:      This paper presents a new conversation generation model, ConceptFlow, which leverages commonsense knowledge graphs to explicitly model conversation flows.

40, TITLE:      Designing Data Augmentation for Simulating Interventions
http://arxiv.org/abs/2005.01856
AUTHORS:        Maximilian Ilse ; Jakub M. Tomczak ; Patrick Forré
HIGHLIGHT:      In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels.

41, TITLE:      PointPainting: Sequential Fusion for 3D Object Detection
http://arxiv.org/abs/1911.10150
AUTHORS:        Sourabh Vora ; Alex H. Lang ; Bassam Helou ; Oscar Beijbom
COMMENTS:       11 pages, 6 figures, 8 tables. v1 is initial submission to CVPR 2020. v2 is final version accepted for publication at CVPR 2020
HIGHLIGHT:      In this work, we propose PointPainting: a sequential fusion method to fill this gap.

42, TITLE:      Entity Abstraction in Visual Model-Based Reinforcement Learning
http://arxiv.org/abs/1910.12827
AUTHORS:        Rishi Veerapaneni ; John D. Co-Reyes ; Michael Chang ; Michael Janner ; Chelsea Finn ; Jiajun Wu ; Joshua B. Tenenbaum ; Sergey Levine
COMMENTS:       Accepted at CoRL 2019
HIGHLIGHT:      We present object-centric perception, prediction, and planning (OP3), which to the best of our knowledge is the first fully probabilistic entity-centric dynamic latent variable framework for model-based reinforcement learning that acquires entity representations from raw visual observations without supervision and uses them to predict and plan.

43, TITLE:      INTEL-TAU: A Color Constancy Dataset
http://arxiv.org/abs/1910.10404
AUTHORS:        Firas Laakom ; Jenni Raitoharju ; Alexandros Iosifidis ; Jarno Nikkanen ; Moncef Gabbouj
COMMENTS:       7 pages, 3 figures
HIGHLIGHT:      In this paper, we describe a new large dataset for illumination estimation.

44, TITLE:      Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
http://arxiv.org/abs/2004.10964
AUTHORS:        Suchin Gururangan ; Ana Marasović ; Swabha Swayamdipta ; Kyle Lo ; Iz Beltagy ; Doug Downey ; Noah A. Smith
COMMENTS:       ACL 2020
HIGHLIGHT:      We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings.

45, TITLE:      Self-Paced Deep Reinforcement Learning
http://arxiv.org/abs/2004.11812
AUTHORS:        Pascal Klink ; Carlo D'Eramo ; Jan Peters ; Joni Pajarinen
HIGHLIGHT:      In this paper, we consider recently established results in Curriculum Learning for episodic RL, proposing an extension that is easily integrated with well-known RL algorithms and providing a theoretical formulation from an RL-as-Inference perspective.

46, TITLE:      Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
http://arxiv.org/abs/1911.03860
AUTHORS:        Margaret Li ; Stephen Roller ; Ilia Kulikov ; Sean Welleck ; Y-Lan Boureau ; Kyunghyun Cho ; Jason Weston
HIGHLIGHT:      In this work we show how all of these problems can be addressed by extending the recently introduced unlikelihood loss (Welleck et al., 2019) to these cases.

47, TITLE:      Mimicking Evolution with Reinforcement Learning
http://arxiv.org/abs/2004.00048
AUTHORS:        João P. Abrantes ; Arnaldo J. Abrantes ; Frans A. Oliehoek
COMMENTS:       18 pages, 7 figures
HIGHLIGHT:      This work proposes Evolution via Evolutionary Reward (EvER) that allows learning to single-handedly drive the search for policies with increasingly evolutionary fitness by ensuring the alignment of the reward function with the fitness function.

48, TITLE:      Self-supervised Learning of Visual Speech Features with Audiovisual Speech Enhancement
http://arxiv.org/abs/2004.12031
AUTHORS:        Zakaria Aldeneh ; Anushree Prasanna Kumar ; Barry-John Theobald ; Erik Marchi ; Sachin Kajarekar ; Devang Naik ; Ahmed Hussen Abdelaziz
COMMENTS:       Submitted to INTERSPEECH
HIGHLIGHT:      We present an introspection of an audiovisual speech enhancement model.

49, TITLE:      Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
http://arxiv.org/abs/2001.03152
AUTHORS:        Krishna Kumar Singh ; Dhruv Mahajan ; Kristen Grauman ; Yong Jae Lee ; Matt Feiszli ; Deepti Ghadiyaram
COMMENTS:       CVPR 2020
HIGHLIGHT:      Our goal is to accurately recognize a category in the absence of its context, without compromising on performance when it co-occurs with context.

50, TITLE:      Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques
http://arxiv.org/abs/2005.01385
AUTHORS:        Narinder Singh Punn ; Sanjay Kumar Sonbhadra ; Sonali Agarwal
HIGHLIGHT:      Motivated by this notion, this article proposes a deep learning based framework for automating the task of monitoring social distancing using surveillance video.

51, TITLE:      Multi-modal Dense Video Captioning
http://arxiv.org/abs/2003.07758
AUTHORS:        Vladimir Iashin ; Esa Rahtu
COMMENTS:       To appear in the proceedings of CVPR Workshops 2020; Code: https://github.com/v-iashin/MDVC; Project Page: https://v-iashin.github.io/mdvc
HIGHLIGHT:      In this paper, we present a new dense video captioning approach that is able to utilize any number of modalities for event description.

52, TITLE:      Singing Synthesis: with a little help from my attention
http://arxiv.org/abs/1912.05881
AUTHORS:        Orazio Angelini ; Alexis Moinet ; Kayoko Yanagisawa ; Thomas Drugman
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      We present UTACO, a singing synthesis model based on an attention-based sequence-to-sequence mechanism and a vocoder based on dilated causal convolutions.

53, TITLE:      Adversarial NLI: A New Benchmark for Natural Language Understanding
http://arxiv.org/abs/1910.14599
AUTHORS:        Yixin Nie ; Adina Williams ; Emily Dinan ; Mohit Bansal ; Jason Weston ; Douwe Kiela
COMMENTS:       ACL 2020
HIGHLIGHT:      We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure.

54, TITLE:      A New Approach for Optimizing Highly Nonlinear Problems Based on the Observer Effect Concept
http://arxiv.org/abs/1906.05516
AUTHORS:        Mojtaba Moattari ; Emad Roshandel ; Shima Kamyab ; Zohreh Azimifar
COMMENTS:       29 pages, 10 figures, 9 pages, 11 formulas
HIGHLIGHT:      To find global optima, a new meta-heuristic algorithm is proposed based on Observer Effect concepts for controlling memory usage per localities without pursuing Tabu-like cut-off approaches.