2020.04.22.txt

==========New Papers==========
1, TITLE:       Disaster Feature Classification on Aerial Photography to Explain Typhoon Damaged Region using Grad-CAM
http://arxiv.org/abs/2004.10130
AUTHORS:        Yasuno Takato
COMMENTS:       12pages, 7 figures
HIGHLIGHT:      This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography.

2, TITLE:       Have you forgotten? A method to assess if machine learning models have forgotten data
http://arxiv.org/abs/2004.10129
AUTHORS:        Xiao Liu ; Sotirios A Tsaftaris
COMMENTS:       11 pages, 2 figures and 2 tables
HIGHLIGHT:      In this paper, for the first time, we want to address the challenging question of whether data have been forgotten by a model.

3, 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.

4, TITLE:       COVID-19 and Company Knowledge Graphs: Assessing Golden Powers and Economic Impact of Selective Lockdown via AI Reasoning
http://arxiv.org/abs/2004.10119
AUTHORS:        Luigi Bellomarini ; Marco Benedetti ; Andrea Gentili ; Rosario Laurendi ; Davide Magnanimi ; Antonio Muci ; Emanuel Sallinger
HIGHLIGHT:      In this work, we present a visionary opinion and report on ongoing work about the application of Automated Reasoning and Knowledge Graph technology to address the impact of the COVID-19 outbreak on the network of Italian companies and support the application of legal instruments for the protection of strategic companies from takeovers.

5, TITLE:       Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data
http://arxiv.org/abs/2004.10114
AUTHORS:        Marcel Bengs ; Nils Gessert ; Matthias Schlüter ; Alexander Schlaefer
COMMENTS:       Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery (IJCARS)
HIGHLIGHT:      For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach.

6, TITLE:       Experience Grounds Language
http://arxiv.org/abs/2004.10151
AUTHORS:        Yonatan Bisk ; Ari Holtzman ; Jesse Thomason ; Jacob Andreas ; Yoshua Bengio ; Joyce Chai ; Mirella Lapata ; Angeliki Lazaridou ; Jonathan May ; Aleksandr Nisnevich ; Nicolas Pinto ; Joseph Turian
HIGHLIGHT:      In this article, we consider work on the contextual foundations of language: grounding, embodiment, and social interaction.

7, TITLE:       Unsupervised Opinion Summarization with Noising and Denoising
http://arxiv.org/abs/2004.10150
AUTHORS:        Reinald Kim Amplayo ; Mirella Lapata
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper we enable the use of supervised learning for the setting where there are only documents available (e.g.,~product or business reviews) without ground truth summaries. We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input.

8, TITLE:       TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition
http://arxiv.org/abs/2004.10141
AUTHORS:        Rami Ben-Ari ; Mor Shpigel ; Ophir Azulai ; Udi Barzelay ; Daniel Rotman
HIGHLIGHT:      In this paper, we present a Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between sub-actions.

9, TITLE:       Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
http://arxiv.org/abs/2004.10171
AUTHORS:        Haipeng Sun ; Rui Wang ; Kehai Chen ; Masao Utiyama ; Eiichiro Sumita ; Tiejun Zhao
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this paper, we empirically introduce a simple method to translate between thirteen languages using a single encoder and a single decoder, making use of multilingual data to improve UNMT for all language pairs.

10, TITLE:      4D Spatio-Temporal Deep Learning with 4D fMRI Data for Autism Spectrum Disorder Classification
http://arxiv.org/abs/2004.10165
AUTHORS:        Marcel Bengs ; Nils Gessert ; Alexander Schlaefer
COMMENTS:       Accepted at MIDL 2019
HIGHLIGHT:      Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data.

11, TITLE:      EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks
http://arxiv.org/abs/2004.10162
AUTHORS:        Sanchari Sen ; Balaraman Ravindran ; Anand Raghunathan
COMMENTS:       Published as a conference paper at ICLR 2020
HIGHLIGHT:      In this work, we propose EMPIR, ensembles of quantized DNN models with different numerical precisions, as a new approach to increase robustness against adversarial attacks.

12, TITLE:      Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
http://arxiv.org/abs/2004.10157
AUTHORS:        Akari Asai ; Hannaneh Hajishirzi
COMMENTS:       Published as a conference paper at ACL 2020
HIGHLIGHT:      This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models.

13, TITLE:      Spatio-spectral deep learning methods for in-vivo hyperspectral laryngeal cancer detection
http://arxiv.org/abs/2004.10159
AUTHORS:        Marcel Bengs ; Stephan Westermann ; Nils Gessert ; Dennis Eggert ; Andreas O. H. Gerstner ; Nina A. Mueller ; Christian Betz ; Wiebke Laffers ; Alexander Schlaefer
COMMENTS:       Accepted at SPIE Medical Imaging 2020
HIGHLIGHT:      In this work, we analyze deep learning techniques for in-vivo hyperspectral laryngeal cancer detection.

14, TITLE:      Semantics, Specification, and Bounded Verification of Concurrent Libraries in Replicated Systems
http://arxiv.org/abs/2004.10158
AUTHORS:        Kartik Nagar ; Prasita Mukherjee ; Suresh Jagannathan
COMMENTS:       Extended Version of CAV20 Paper
HIGHLIGHT:      In this paper, we tackle these issues by proposing appropriate semantics and specifications for highly-concurrent libraries in a weakly-consistent, replicated setting.

15, TITLE:      Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation
http://arxiv.org/abs/2004.10190
AUTHORS:        Ryan Julian ; Benjamin Swanson ; Gaurav S. Sukhatme ; Sergey Levine ; Chelsea Finn ; Karol Hausman
COMMENTS:       8.5 pages, 9 figures. See video overview and experiments at https://youtu.be/pPDVewcSpdc and project website at https://ryanjulian.me/continual-fine-tuning
HIGHLIGHT:      In this paper, we present a method and empirical evidence towards a robot learning framework that facilitates continuous adaption.

16, 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:       55 pages (one column), 13 figures, 5 tables, supplementary materials, Under review
HIGHLIGHT:      In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for the segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI.

17, TITLE:      Grounding Conversations with Improvised Dialogues
http://arxiv.org/abs/2004.09544
AUTHORS:        Hyundong Cho ; Jonathan May
COMMENTS:       ACL2020; 9 pages + 1 page appendix
HIGHLIGHT:      We collect a corpus of more than 26,000 yes-and turns, transcribing them from improv dialogues and extracting them from larger, but more sparsely populated movie script dialogue corpora, via a bootstrapped classifier.

18, TITLE:      AANet: Adaptive Aggregation Network for Efficient Stereo Matching
http://arxiv.org/abs/2004.09548
AUTHORS:        Haofei Xu ; Juyong Zhang
COMMENTS:       CVPR 2020. The improved version AANet+ is also included. Code: https://github.com/haofeixu/aanet
HIGHLIGHT:      In this paper, we aim at completely replacing the commonly used 3D convolutions to achieve fast inference speed while maintaining comparable accuracy.

19, TITLE:      Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings
http://arxiv.org/abs/2004.09776
AUTHORS:        Moritz Einfalt ; Rainer Lienhart
COMMENTS:       10 pages, 4 figures
HIGHLIGHT:      In this paper we address the problem of motion event detection in athlete recordings from individual sports.

20, TITLE:      A CNN Framenwork Based on Line Annotations for Detecting Nematodes in Microscopic Images
http://arxiv.org/abs/2004.09795
AUTHORS:        Long Chen ; Martin Strauch ; Matthias Daub ; Xiaochen Jiang ; Marcus Jansen ; Hans-Georg Luigs ; Susanne Schultz-Kuhlmann ; Stefan Krüssel ; Dorif Merhof
COMMENTS:       ISBI 2020
HIGHLIGHT:      Here, we propose a framework for detecting worm-shaped objects in microscopic images that is based on convolutional neural networks (CNNs).

21, TITLE:      Games for Fairness and Interpretability
http://arxiv.org/abs/2004.09551
AUTHORS:        Eric Chu ; Nabeel Gillani ; Sneha Priscilla Makini
HIGHLIGHT:      We propose a new class of games -- ``games for fairness and interpretability'' -- as one example of an incentive-aligned approach for producing fairer and more equitable algorithms.

22, TITLE:      LSQ+: Improving low-bit quantization through learnable offsets and better initialization
http://arxiv.org/abs/2004.09576
AUTHORS:        Yash Bhalgat ; Jinwon Lee ; Markus Nagel ; Tijmen Blankevoort ; Nojun Kwak
COMMENTS:       Camera-ready for Joint Workshop on Efficient Deep Learning in Computer Vision, CVPR 2020
HIGHLIGHT:      To solve this problem, we propose LSQ+, a natural extension of LSQ, wherein we introduce a general asymmetric quantization scheme with trainable scale and offset parameters that can learn to accommodate the negative activations.

23, TITLE:      Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis
http://arxiv.org/abs/2004.09573
AUTHORS:        Marie-Sophie von Braun ; Patrick Frenzel ; Christian Käding ; Mirco Fuchs
COMMENTS:       (Accepted / In press) 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRW)
HIGHLIGHT:      Here, we propose an approach for the automated waterline detection.

24, TITLE:      Attention Module is Not Only a Weight: Analyzing Transformers with Vector Norms
http://arxiv.org/abs/2004.10102
AUTHORS:        Goro Kobayashi ; Tatsuki Kuribayashi ; Sho Yokoi ; Kentaro Inui
HIGHLIGHT:      In this study, we point out that attention weights alone are only one of the two factors determining the output of self-attention modules, and we propose to incorporate the other factor as well, namely, the transformed input vectors into the analysis.

25, TITLE:      Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
http://arxiv.org/abs/2004.09821
AUTHORS:        Long Chen ; Martin Strauch ; Dorit Merhof
COMMENTS:       MICCAI 2019
HIGHLIGHT:      In this work, we assign an embedding vector to each pixel through a deep neural network.

26, TITLE:      Commutative automata networks
http://arxiv.org/abs/2004.09806
AUTHORS:        Florian Bridoux ; Maximilien Gadouleau ; Guillaume Theyssier
HIGHLIGHT:      In this paper, we study automata networks that are invariant under many different update schedules.

27, TITLE:      AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification
http://arxiv.org/abs/2004.09805
AUTHORS:        Hongjun Choi ; Anirudh Som ; Pavan Turaga
HIGHLIGHT:      In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry.

28, TITLE:      Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation
http://arxiv.org/abs/2004.09813
AUTHORS:        Nils Reimers ; Iryna Gurevych
HIGHLIGHT:      We present an easy and efficient method to extend existing sentence embedding models to new languages.

29, TITLE:      Fast and Robust Registration of Aerial Images and LiDAR data Based on Structrual Features and 3D Phase Correlation
http://arxiv.org/abs/2004.09811
AUTHORS:        Bai Zhu ; Yuanxin Ye ; Chao Yang ; Liang Zhou ; Huiyu Liu ; Yungang Cao
HIGHLIGHT:      To tackle the problem, this paper proposes an automatic registration method based on structural features and three-dimension (3D) phase correlation.

30, TITLE:      A Decomposition-based Large-scale Multi-modal Multi-objective Optimization Algorithm
http://arxiv.org/abs/2004.09838
AUTHORS:        Yiming Peng ; Hisao Ishibuchi
COMMENTS:       8 pages, 8 figures, 3 tables. Accepted by the 2020 IEEE Congress on Evolutionary Computation (IEEE CEC)
HIGHLIGHT:      In this paper, we propose an efficient multi-modal multi-objective optimization algorithm based on the widely used MOEA/D algorithm.

31, TITLE:      An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com
http://arxiv.org/abs/2004.09601
AUTHORS:        Shadi Shahsavari ; Ehsan Ebrahimzadeh ; Behnam Shahbazi ; Misagh Falahi ; Pavan Holur ; Roja Bandari ; Timothy R. Tangherlini ; Vwani Roychowdhury
HIGHLIGHT:      Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem.

32, TITLE:      Egel -- Graph Rewriting with a Twist
http://arxiv.org/abs/2004.09843
AUTHORS:        M. C. A. ;  Devillers
COMMENTS:       3 pages, 5 figures
HIGHLIGHT:      Egel -- Graph Rewriting with a Twist

33, TITLE:      SIBRE: Self Improvement Based REwards for Reinforcement Learning
http://arxiv.org/abs/2004.09846
AUTHORS:        Somjit Nath ; Richa Verma ; Abhik Ray ; Harshad Khadilkar
COMMENTS:       7 pages, 10 figures
HIGHLIGHT:      We propose a generic reward shaping approach for improving rate of convergence in reinforcement learning (RL), called \textbf{S}elf \textbf{I}mprovement \textbf{B}ased \textbf{RE}wards, or \textbf{SIBRE}.

34, TITLE:      Robust Motion Averaging under MaximumCorrentropy Criterion
http://arxiv.org/abs/2004.09829
AUTHORS:        Jihua Zhu ; Jie Hu ; Zhongyu Li ; Badong Chen
HIGHLIGHT:      Accordingly, this paper proposes a novel robust motion averaging method based on the maximum correntropy criterion (MCC).

35, TITLE:      MixNet: Multi-modality Mix Network for Brain Segmentation
http://arxiv.org/abs/2004.09832
AUTHORS:        Long Chen ; Dorit Merhof
COMMENTS:       BrainLes, MICCAI 2018
HIGHLIGHT:      In this work, we introduce MixNet, a 2D semantic-wise deep convolutional neural network to segment brain structure in multi-modality MRI images.

36, TITLE:      TAL EmotioNet Challenge 2020 Rethinking the Model Chosen Problem in Multi-Task Learning
http://arxiv.org/abs/2004.09862
AUTHORS:        Pengcheng Wang ; Zihao Wang ; Zhilong Ji ; Xiao Liu ; Songfan Yang ; Zhongqin Wu
COMMENTS:       4 pages, 2 figures, 3 tables, CVPRW2020
HIGHLIGHT:      This paper introduces our approach to the EmotioNet Challenge 2020. We pose the AU recognition problem as a multi-task learning problem, where the non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head motion (the last 6 AUs) are modeled separately.

37, TITLE:      Knowledge-Driven Distractor Generation for Cloze-style Multiple Choice Questions
http://arxiv.org/abs/2004.09853
AUTHORS:        Siyu Ren ; Kenny Q. Zhu
COMMENTS:       11 pages, 4 figures, 6 tables
HIGHLIGHT:      In this paper, we propose a novel configurable framework to automatically generate distractive choices for open-domain cloze-style multiple-choice questions, which incorporates a general-purpose knowledge base to effectively create a small distractor candidate set, and a feature-rich learning-to-rank model to select distractors that are both plausible and reliable.

38, TITLE:      Deep variational network for rapid 4D flow MRI reconstruction
http://arxiv.org/abs/2004.09610
AUTHORS:        Valery Vishnevskiy ; Jonas Walheim ; Sebastian Kozerke
COMMENTS:       15 pages, 6 figures
HIGHLIGHT:      We propose an efficient model-based deep neural reconstruction network and evaluate its performance on clinical aortic flow data.

39, TITLE:      Learning large logic programs by going beyond entailment
http://arxiv.org/abs/2004.09855
AUTHORS:        Andrew Cropper ; Sebastijan Dumančić
COMMENTS:       Accepted for IJCAI20
HIGHLIGHT:      We implement our idea in Brute, a new ILP system which uses best-first search, guided by an example-dependent loss function, to incrementally build programs.

40, TITLE:      Fixed-Target Runtime Analysis
http://arxiv.org/abs/2004.09613
AUTHORS:        Maxim Buzdalov ; Benjamin Doerr ; Carola Doerr ; Dmitry Vinokurov
COMMENTS:       This is a slightly extended version of the paper accepted to GECCO 2020
HIGHLIGHT:      In this work, we conduct an in-depth study on the advantages and limitations of fixed-target analyses.

41, TITLE:      Self-Supervised Feature Extraction for 3D Axon Segmentation
http://arxiv.org/abs/2004.09629
AUTHORS:        Tzofi Klinghoffer ; Peter Morales ; Young-Gyun Park ; Nicholas Evans ; Kwanghun Chung ; Laura J. Brattain
COMMENTS:       Accepted to CVPR Computer Vision for Microscopy Image Analysis Workshop 2020. 7 pages. 3 Figures
HIGHLIGHT:      We propose a self-supervised auxiliary task that utilizes the tube-like structure of axons to build a feature extractor from unlabeled data.

42, TITLE:      Intelligent Querying for Target Tracking in Camera Networks using Deep Q-Learning with n-Step Bootstrapping
http://arxiv.org/abs/2004.09632
AUTHORS:        Anil Sharma ; Saket Anand ; Sanjit K. Kaul
COMMENTS:       Camera Selections for Target Tracking
HIGHLIGHT:      In this paper, we address the problem of intelligent scheduling of re-identification queries in a multi-camera tracking setting.

43, TITLE:      Rice grain disease identification using dual phase convolutional neural network-based system aimed at small dataset
http://arxiv.org/abs/2004.09870
AUTHORS:        Tashin Ahmed ; Chowdhury Rafeed Rahman ; Md. Faysal Mahmud Abid
HIGHLIGHT:      In this work, a CNN based dual phase method has been proposed which can work effectively on small rice grain disease dataset with heterogeneity.

44, TITLE:      CovidAID: COVID-19 Detection Using Chest X-Ray
http://arxiv.org/abs/2004.09803
AUTHORS:        Arpan Mangal ; Surya Kalia ; Harish Rajgopal ; Krithika Rangarajan ; Vinay Namboodiri ; Subhashis Banerjee ; Chetan Arora
HIGHLIGHT:      In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing.

45, TITLE:      Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition
http://arxiv.org/abs/2004.09802
AUTHORS:        Qi Li ; Hanlin Mo ; Jinghan Zhao ; Hongxiang Hao ; Hua Li
HIGHLIGHT:      In this work, we propose a novel feature called spatio-temporal dual affine differential invariant (STDADI).

46, TITLE:      Keyphrase Generation with Cross-Document Attention
http://arxiv.org/abs/2004.09800
AUTHORS:        Shizhe Diao ; Yan Song ; Tong Zhang
COMMENTS:       13 pages, 3 figures
HIGHLIGHT:      In this paper, we propose CDKGen, a Transformer-based keyphrase generator, which expands the Transformer to global attention with cross-document attention networks to incorporate available documents as references so as to generate better keyphrases with the guidance of topic information.

47, TITLE:      Adaptive Interaction Fusion Networks for Fake News Detection
http://arxiv.org/abs/2004.10009
AUTHORS:        Lianwei Wu ; Yuan Rao
COMMENTS:       Accepted at the 24th European Conference on Artificial Intelligence (ECAI 2020)
HIGHLIGHT:      In this paper, we propose Adaptive Interaction Fusion Networks (AIFN) to fulfill cross-interaction fusion among features for fake news detection.

48, TITLE:      Characterizing Boundedness in Chase Variants
http://arxiv.org/abs/2004.10030
AUTHORS:        Stathis Delivorias ; Michel Leclère ; Marie-Laure Mugnier ; Federico Ulliana
COMMENTS:       Under consideration for publication in Theory and Practice of Logic Programming
HIGHLIGHT:      Hence, we investigate the decidability of the k-boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k.

49, TITLE:      Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
http://arxiv.org/abs/2004.10016
AUTHORS:        Mohammad Reza Loghmani ; Luca Robbiano ; Mirco Planamente ; Kiru Park ; Barbara Caputo ; Markus Vincze
HIGHLIGHT:      We propose a novel RGB-D DA method that reduces the synthetic-to-real domain shift by exploiting the inter-modal relation between the RGB and depth image.

50, TITLE:      Learning Relation Ties with a Force-Directed Graph in Distant Supervised Relation Extraction
http://arxiv.org/abs/2004.10051
AUTHORS:        Yuming Shang ; Heyan Huang ; Xin Sun ; Xianling Mao
COMMENTS:       Learning Relation Ties
HIGHLIGHT:      To solve this problem, in this paper, we propose a novel force-directed graph based relation extraction model to comprehensively learn relation ties.

51, TITLE:      Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression
http://arxiv.org/abs/2004.10043
AUTHORS:        Shurun Wang ; Shiqi Wang ; Wenhan Yang ; Xinfeng Zhang ; Shanshe Wang ; Siwei Ma ; Wen Gao
HIGHLIGHT:      With numerous approaches proposed to efficiently compress the texture and visual features serving human visual perception and machine intelligence respectively, much less work has been dedicated to studying the interactions between them.

52, TITLE:      A Novel Graphic Bending Transformation on Benchmark
http://arxiv.org/abs/2004.10042
AUTHORS:        Fengyang Sun ; Qingrui Ni ; Lin Wang ; Bo Yang ; Chunxiuzi Liu
COMMENTS:       11 pages, 7 figures, submitted to the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN XVI) held in Leiden, The Netherlands on September 5-9, 2020
HIGHLIGHT:      In this paper, inspired from image processing, we investigate a novel graphic conformal mapping transformation on benchmark problems to deform the function shape.

53, TITLE:      Leveraging Cognitive Search Patterns to Enhance Automated Natural Language Retrieval Performance
http://arxiv.org/abs/2004.10035
AUTHORS:        Bhawani Selvaretnam ; Mohammed Belkhatir
HIGHLIGHT:      Over the past two decades, a significant body of works has advanced technical retrieval prowess while several studies have shed light on issues pertaining to human search behavior.

54, TITLE:      Knowledge Graph Embedding with Linear Representation for Link Prediction
http://arxiv.org/abs/2004.10037
AUTHORS:        Yanhui Peng ; Jing Zhang
HIGHLIGHT:      In this paper, we propose a novel embedding model, namely LineaRE, which is capable of modeling four connectivity patterns (symmetry, antisymmetry, inversion, and composition) and four mapping properties of relations (one-to-one, one-to-many, many-to-one, and many-to-many).

55, TITLE:      Tensor Networks for Medical Image Classification
http://arxiv.org/abs/2004.10076
AUTHORS:        Raghavendra Selvan ; Erik B Dam
COMMENTS:       Accepted for publication at International Conference on Medical Imaging with Deep Learning (MIDL), 2020. Reviews on Openreview here: https://openreview.net/forum?id=jjk6bxk07G
HIGHLIGHT:      In this work, we focus on the class of Tensor Networks, which has been a work horse for physicists in the last two decades to analyse quantum many-body systems.

56, TITLE:      AMP-Net: Denoising based Deep Unfolding for Compressive Image Sensing
http://arxiv.org/abs/2004.10078
AUTHORS:        Zhonghao Zhang ; Yipeng Liu ; Jiani Liu ; Fei Wen ; Ce Zhu
HIGHLIGHT:      In this paper, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net.

57, TITLE:      Frequency-Weighted Robust Tensor Principal Component Analysis
http://arxiv.org/abs/2004.10068
AUTHORS:        Shenghan Wang ; Yipeng Liu ; Lanlan Feng ; Ce Zhu
HIGHLIGHT:      In this paper, we incorporate frequency component analysis into t-SVD to enhance the RTPCA performance.

58, TITLE:      Local Clustering with Mean Teacher for Semi-supervised Learning
http://arxiv.org/abs/2004.09665
AUTHORS:        Zexi Chen ; Benjamin Dutton ; Bharathkumar Ramachandra ; Tianfu Wu ; Ranga Raju Vatsavai
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias.

59, TITLE:      The Panacea Threat Intelligence and Active Defense Platform
http://arxiv.org/abs/2004.09662
AUTHORS:        Adam Dalton ; Ehsan Aghaei ; Ehab Al-Shaer ; Archna Bhatia ; Esteban Castillo ; Zhuo Cheng ; Sreekar Dhaduvai ; Qi Duan ; Md Mazharul Islam ; Younes Karimi ; Amir Masoumzadeh ; Brodie Mather ; Sashank Santhanam ; Samira Shaikh ; Tomek Strzalkowski ; Bonnie J. Dorr
COMMENTS:       Accepted at STOC
HIGHLIGHT:      We describe Panacea, a system that supports natural language processing (NLP) components for active defenses against social engineering attacks.

60, TITLE:      Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling
http://arxiv.org/abs/2004.09890
AUTHORS:        David Harbecke ; Christoph Alt
COMMENTS:       ACL 2020 Student Research Workshop
HIGHLIGHT:      Thus, we propose OLM: a novel explanation method that combines occlusion and language models to sample valid and syntactically correct replacements with high likelihood, given the context of the original input.

61, TITLE:      Contextual Neural Machine Translation Improves Translation of Cataphoric Pronouns
http://arxiv.org/abs/2004.09894
AUTHORS:        KayYen Wong ; Sameen Maruf ; Gholamreza Haffari
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this work, we investigate the effect of future sentences as context by comparing the performance of a contextual NMT model trained with the future context to the one trained with the past context.

62, TITLE:      Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution
http://arxiv.org/abs/2004.09681
AUTHORS:        Yingruo Fan ; Jacqueline C. K. Lam ; Victor O. K. Li
COMMENTS:       Accepted at AAAI2020
HIGHLIGHT:      In contrast, we present a new learning framework that automatically learns the latent relationships of AUs via establishing semantic correspondences between feature maps.

63, TITLE:      Mirror Ritual: An Affective Interface for Emotional Self-Reflection
http://arxiv.org/abs/2004.09685
AUTHORS:        Nina Rajcic ; Jon McCormack
COMMENTS:       Paper presented at ACM CHI2020: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ACM, New York, April 2020
HIGHLIGHT:      This paper introduces a new form of real-time affective interface that engages the user in a process of conceptualisation of their emotional state.

64, TITLE:      LRCN-RetailNet: A recurrent neural network architecture for accurate people counting
http://arxiv.org/abs/2004.09672
AUTHORS:        Lucas Massa ; Adriano Barbosa ; Krerley Oliveira ; Thales Vieira
HIGHLIGHT:      We introduce LRCN-RetailNet: a recurrent neural network architecture capable of learning a non-linear regression model and accurately predicting the people count from videos captured by low-cost surveillance cameras.

65, TITLE:      Image Retrieval using Multi-scale CNN Features Pooling
http://arxiv.org/abs/2004.09695
AUTHORS:        Federico Vaccaro ; Marco Bertini ; Tiberio Uricchio ; Alberto Del Bimbo
HIGHLIGHT:      In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network.

66, TITLE:      Alleviating the Incompatibility between Cross Entropy Loss and Episode Training for Few-shot Skin Disease Classification
http://arxiv.org/abs/2004.09694
AUTHORS:        Wei Zhu ; Haofu Liao ; Wenbin Li ; Weijian Li ; Jiebo Luo
HIGHLIGHT:      Inspired by the recent success of Few-Shot Learning (FSL) in natural image classification, we propose to apply FSL to skin disease identification to address the extreme scarcity of training sample problem.

67, TITLE:      On the Parameterised Complexity of Induced Multipartite Graph Parameters
http://arxiv.org/abs/2004.09938
AUTHORS:        Ryan L. Mann ; Luke Mathieson ; Catherine Greenhill
COMMENTS:       9 pages, 0 figures
HIGHLIGHT:      We introduce a family of graph parameters, called induced multipartite graph parameters, and study their computational complexity.

68, TITLE:      DIET: Lightweight Language Understanding for Dialogue Systems
http://arxiv.org/abs/2004.09936
AUTHORS:        Tanja Bunk ; Daksh Varshneya ; Vladimir Vlasov ; Alan Nichol
HIGHLIGHT:      We introduce the Dual Intent and Entity Transformer (DIET) architecture, and study the effectiveness of different pre-trained representations on intent and entity prediction, two common dialogue language understanding tasks.

69, TITLE:      Learning Continuous Treatment Policy and Bipartite Embeddings for Matching with Heterogeneous Causal Effects
http://arxiv.org/abs/2004.09703
AUTHORS:        Will Y. Zou ; Smitha Shyam ; Michael Mui ; Mingshi Wang ; Jan Pedersen ; Zoubin Ghahramani
HIGHLIGHT:      We propose to formulate the effectiveness of treatment as a parametrizable model, expanding to a multitude of treatment intensities and complexities through the continuous policy treatment function, and the likelihood of matching.

70, TITLE:      TTNet: Real-time temporal and spatial video analysis of table tennis
http://arxiv.org/abs/2004.09927
AUTHORS:        Roman Voeikov ; Nikolay Falaleev ; Ruslan Baikulov
COMMENTS:       6th International Workshop on Computer Vision in Sports (CVsports) at CVPR 2020
HIGHLIGHT:      We present a neural network TTNet aimed at real-time processing of high-resolution table tennis videos, providing both temporal (events spotting) and spatial (ball detection and semantic segmentation) data.

71, TITLE:      Knowledge Refactoring for Program Induction
http://arxiv.org/abs/2004.09931
AUTHORS:        Sebastijan Dumancic ; Andrew Cropper
COMMENTS:       7 pages, 6 figures
HIGHLIGHT:      Our goal is to give a machine learning system similar abilities so that it can learn more efficiently.

72, TITLE:      Relabel the Noise: Joint Extraction of Entities and Relations via Cooperative Multiagents
http://arxiv.org/abs/2004.09930
AUTHORS:        Daoyuan Chen ; Yaliang Li ; Kai Lei ; Ying Shen
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      We propose a joint extraction approach to address this problem by re-labeling noisy instances with a group of cooperative multiagents.

73, TITLE:      Word Embedding-based Text Processing for Comprehensive Summarization and Distinct Information Extraction
http://arxiv.org/abs/2004.09719
AUTHORS:        Xiangpeng Wan ; Hakim Ghazzai ; Yehia Massoud
COMMENTS:       This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA)
HIGHLIGHT:      In this paper, we propose two automated text processing frameworks specifically designed to analyze online reviews.

74, TITLE:      NPF-MVSNet: Normal and Pyramid Feature Aided Unsupervised MVS Network
http://arxiv.org/abs/2004.09722
AUTHORS:        Baichuan Huang ; Can Huang ; Yijia He ; Jingbin Liu ; Xiao Liu
COMMENTS:       Welcome to communicate with the author by the repo https://github.com/whubaichuan/NPF-MVSNet
HIGHLIGHT:      We proposed an unsupervised learning-based network, named NPF-MVSNet, for multi-view stereo reconstruction without ground-truth 3D training data.

75, TITLE:      The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic
http://arxiv.org/abs/2004.09968
AUTHORS:        Viet Duong ; Phu Pham ; Tongyu Yang ; Yu Wang ; Jiebo Luo
HIGHLIGHT:      This paper aims to discover the social implications of this unprecedented disruption in our interactive society regarding both the general public and higher education populations by mining people's opinions on social media.

76, TITLE:      TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
http://arxiv.org/abs/2004.09725
AUTHORS:        Simona Santamaria ; David Dao ; Björn Lütjens ; Ce Zhang
COMMENTS:       *Authors have contributed equally. Published as Spotlight Presentation at ICLR 2020 Workshop on Tackling Climate Change with Machine Learning
HIGHLIGHT:      To be robust against untruthful reporting, we propose TrueBranch, a metric learning-based algorithm that verifies the truthfulness of drone imagery from forest conservation projects.

77, TITLE:      Weakly Aligned Joint Cross-Modality Super Resolution
http://arxiv.org/abs/2004.09965
AUTHORS:        Guy Shacht ; Sharon Fogel ; Dov Danon ; Daniel Cohen-Or
HIGHLIGHT:      To this end, Cross-Modality Super-Resolution methods were introduced, where an RGB image of a high-resolution assists in increasing the resolution of the low-resolution modality.

78, TITLE:      Large Population Sizes and Crossover Help in Dynamic Environments
http://arxiv.org/abs/2004.09949
AUTHORS:        Johannes Lengler ; Jonas Meier
HIGHLIGHT:      In this paper, we study the effect of larger population sizes for Dynamic BinVal, the extremal form of dynamic linear functions.

79, TITLE:      Explainable Goal-Driven Agents and Robots -- A Comprehensive Review and New Framework
http://arxiv.org/abs/2004.09705
AUTHORS:        Fatai Sado ; Chu Kiong Loo ; Matthias Kerzel ; Stefan Wermter
HIGHLIGHT:      The review highlights key strategies that emphasize transparency and understandability, and continual learning for explainability.

80, TITLE:      Algorithms for slate bandits with non-separable reward functions
http://arxiv.org/abs/2004.09957
AUTHORS:        Jason Rhuggenaath ; Alp Akcay ; Yingqian Zhang ; Uzay Kaymak
HIGHLIGHT:      In this paper, we study a slate bandit problem where the function that determines the slate-level reward is non-separable: the optimal value of the function cannot be determined by learning the optimal action for each slot.

81, TITLE:      Neural Abstractive Summarization with Structural Attention
http://arxiv.org/abs/2004.09739
AUTHORS:        Tanya Chowdhury ; Sachin Kumar ; Tanmoy Chakraborty
COMMENTS:       7 pages, 4 tables, 2 figures, IJCAI 2020
HIGHLIGHT:      In this work, we present a hierarchical encoder based on structural attention to model such inter-sentence and inter-document dependencies.

82, TITLE:      BERT-ATTACK: Adversarial Attack Against BERT Using BERT
http://arxiv.org/abs/2004.09984
AUTHORS:        Linyang Li ; Ruotian Ma ; Qipeng Guo ; Xiangyang Xue ; Xipeng Qiu
COMMENTS:       8 pages, 2 figures,
HIGHLIGHT:      In this paper, we propose \textbf{BERT-Attack}, a high-quality and effective method to generate adversarial samples using pre-trained masked language models exemplified by BERT.

83, TITLE:      Towards Generalization of 3D Human Pose Estimation In The Wild
http://arxiv.org/abs/2004.09989
AUTHORS:        Renato Baptista ; Alexandre Saint ; Kassem Al Ismaeil ; Djamila Aouada
HIGHLIGHT:      In this paper, we propose 3DBodyTex.

84, TITLE:      Beyond Optimizing for Clicks: Incorporating Editorial Values in News Recommendation
http://arxiv.org/abs/2004.09980
AUTHORS:        Feng Lu ; Anca Dumitrache ; David Graus
COMMENTS:       To appear in UMAP 2020
HIGHLIGHT:      In this paper we study an automated news recommender system in the context of a news organization's editorial values.

85, TITLE:      Train No Evil: Selective Masking for Task-guided Pre-training
http://arxiv.org/abs/2004.09733
AUTHORS:        Yuxian Gu ; Zhengyan Zhang ; Xiaozhi Wang ; Zhiyuan Liu ; Maosong Sun
COMMENTS:       6 pages, 2 figures
HIGHLIGHT:      In this paper, we propose a selective masking task-guided pre-training method and add it between the general pre-training and fine-tuning.

86, TITLE:      Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
http://arxiv.org/abs/2004.09974
AUTHORS:        Canxiang Yan ; Jianhao Yan ; Yangyin Xu ; Cheng Niu ; Jie Zhou
HIGHLIGHT:      In this paper, an automatic commenting task is proposed for long novels, which involves understanding context of more than tens of thousands of words.

87, TITLE:      Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness
http://arxiv.org/abs/2004.09731
AUTHORS:        Zheng Zhang ; Lizi Liao ; Xiaoyan Zhu ; Tat-Seng Chua ; Zitao Liu ; Yan Huang ; Minlie Huang
HIGHLIGHT:      We therefore propose an opposite behavior aware framework for policy learning in goal-oriented dialogues.

88, TITLE:      Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
http://arxiv.org/abs/2004.09524
AUTHORS:        Asad Khan ; E. A. Huerta ; Arnav Das
COMMENTS:       21 pages, 10 figures, 1 appendix, 1 Interactive visualization at https://khanx169.github.io/smr_bbm_v2/interactive_results.html
HIGHLIGHT:      To quantify the suitability of deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers, we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes.

89, TITLE:      Discrete Variational Attention Models for Language Generation
http://arxiv.org/abs/2004.09764
AUTHORS:        Xianghong Fang ; Haoli Bai ; Zenglin Xu ; Michael Lyu ; Irwin King
COMMENTS:       7 pages, 3 figures
HIGHLIGHT:      To tackle these issues, in this paper we propose the discrete variational attention model with categorical distribution over the attention mechanism owing to the discrete nature in languages.

90, TITLE:      Fine-Grained Expression Manipulation via Structured Latent Space
http://arxiv.org/abs/2004.09769
AUTHORS:        Junshu Tang ; Zhiwen Shao ; Lizhuang Ma
HIGHLIGHT:      To tackle this limitation, we propose an end-to-end expression-guided generative adversarial network (EGGAN), which utilizes structured latent codes and continuous expression labels as input to generate images with expected expressions.

91, TITLE:      Take a NAP: Non-Autoregressive Prediction for Pedestrian Trajectories
http://arxiv.org/abs/2004.09760
AUTHORS:        Hao Xue ; Du. Q. Huynh ; Mark Reynolds
HIGHLIGHT:      In this paper, we present NAP, a non-autoregressive method for trajectory prediction.

92, TITLE:      The 1st Agriculture-Vision Challenge: Methods and Results
http://arxiv.org/abs/2004.09754
AUTHORS:        Mang Tik Chiu ; Xingqian Xu ; Kai Wang ; Jennifer Hobbs ; Naira Hovakimyan ; Thomas S. Huang ; Honghui Shi ; Yunchao Wei ; Zilong Huang ; Alexander Schwing ; Robert Brunner ; Ivan Dozier ; Wyatt Dozier ; Karen Ghandilyan ; David Wilson ; Hyunseong Park ; Junhee Kim ; Sungho Kim ; Qinghui Liu ; Michael C. Kampffmeyer ; Robert Jenssen ; Arnt B. Salberg ; Alexandre Barbosa ; Rodrigo Trevisan ; Bingchen Zhao ; Shaozuo Yu ; Siwei Yang ; Yin Wang ; Hao Sheng ; Xiao Chen ; Jingyi Su ; Ram Rajagopal ; Andrew Ng ; Van Thong Huynh ; Soo-Hyung KimIn-Seop Nan ; Ujjwal Baid ; Shubham Innani ; Prasad Dutande ; Bhakti Baheti ; Jianyu Tang
COMMENTS:       CVPR 2020 Workshop
HIGHLIGHT:      The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.

93, TITLE:      PAI-GCN: Permutable Anisotropic Graph Convolutional Networks for 3D Shape Representation Learning
http://arxiv.org/abs/2004.09995
AUTHORS:        Zhongpai Gao ; Guangtao Zhai ; Juyong Zhang ; Yiyan Yang ; Xiaokang Yang
HIGHLIGHT:      In this paper, we propose a permutable anisotropic convolutional operation (PAI-Conv) that learns adaptive soft-permutation matrices for each node according to the geometric shape of its neighbors and performs shared anisotropic filters as CNN does.

94, TITLE:      MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
http://arxiv.org/abs/2004.09750
AUTHORS:        Yu Qiu ; Yun Liu ; Jing Xu
HIGHLIGHT:      To address the above problems, we propose MiniSeg, a lightweight deep learning model for efficient COVID-19 segmentation. In addition, we build a comprehensive COVID-19 segmentation benchmark for comparing MiniSeg with traditional methods.

95, TITLE:      pomdp_py: A Framework to Build and Solve POMDP Problems
http://arxiv.org/abs/2004.10099
AUTHORS:        Kaiyu Zheng ; Stefanie Tellex
COMMENTS:       5 pages, 3 figures. Submitted to ICAPS 2020 Planning and Robotics (PlanRob) Workshop
HIGHLIGHT:      In this paper, we present pomdp_py, a general purpose Partially Observable Markov Decision Process (POMDP) library written in Python and Cython.

96, TITLE:      Curriculum Pre-training for End-to-End Speech Translation
http://arxiv.org/abs/2004.10093
AUTHORS:        Chengyi Wang ; Yu Wu ; Shujie Liu ; Ming Zhou ; Zhenglu Yang
COMMENTS:       accepted by ACL2020
HIGHLIGHT:      Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages.

97, TITLE:      TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling
http://arxiv.org/abs/2004.10087
AUTHORS:        Libo Qin ; Xiao Xu ; Wanxiang Che ; Ting Liu
HIGHLIGHT:      In this paper, we propose a Token-level Dynamic Graph-Interactive Network (TD-GIN) for joint multiple intent detection and slot filling, where we model the interaction between multiple intents and each token slot in a unified graph architecture.



==========Updates to Previous Papers==========
1, TITLE:       A Simple Method for Computing Some Pseudo-Elliptic Integrals in Terms of Elementary Functions
http://arxiv.org/abs/2004.04910
AUTHORS:        Sam Blake
HIGHLIGHT:      We introduce a method for computing some pseudo-elliptic integrals in terms of elementary functions.

2, TITLE:       Exposing Fake Images with Forensic Similarity Graphs
http://arxiv.org/abs/1912.02861
AUTHORS:        Owen Mayer ; Matthew C. Stamm
COMMENTS:       16 pages, under review at IEEE Journal of Selected Topics in Signal Processing
HIGHLIGHT:      We propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems.

3, TITLE:       Nearly Optimal Sparse Polynomial Multiplication
http://arxiv.org/abs/1901.09355
AUTHORS:        Vasileios Nakos
COMMENTS:       Accepted to IEEE Transactions on Information Theory
HIGHLIGHT:      In this work we give a clean, nearly optimal algorithm for the sparse polynomial multiplication problem.

4, TITLE:       Probabilistic process algebra and strategic interleaving
http://arxiv.org/abs/1912.10041
AUTHORS:        C. A. Middelburg
COMMENTS:       23 pages, arXiv admin note: substantial text overlap with arXiv:1703.06822; 23 pages, important corollary of Theorem 2 added; 23 pages, minor error on page 17 corrected and corollary of Theorem 2 tightened; 23 pages, Theorem 2 moved (now Theorem 3) and adapted; 23 pages, Section 4.3 rewritten
HIGHLIGHT:      The extension covers probabilistic interleaving strategies.

5, TITLE:       User Generated Data: Achilles' Heel of BERT
http://arxiv.org/abs/2003.12932
AUTHORS:        Ankit Kumar ; Piyush Makhija ; Anuj Gupta
COMMENTS:       7 pages, 2 figures, 6 plots
HIGHLIGHT:      In this work we systematically show that when the text data is noisy, there is a significant degradation in the performance of BERT.

6, TITLE:       A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
http://arxiv.org/abs/1907.02731
AUTHORS:        Elena Burceanu ; Marius Leordeanu
COMMENTS:       6+1 pages, 6 figures, 3 tables, accepted at IJCAI2020
HIGHLIGHT:      We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods.

7, TITLE:       Two Player Hidden Pointer Chasing and Multi-Pass Lower Bounds in Turnstile Streams
http://arxiv.org/abs/2002.12856
AUTHORS:        Anay Mehrotra ; Vibhor Porwal ; Raghunath Tewari
COMMENTS:       The authors have withdrawn this paper due to an error in the proof of Lemma 3.4
HIGHLIGHT:      We present a two-player version ($\mathsf{HPC}^2$) of $\mathsf{HPC}^4$ that has matching communication complexity to $\mathsf{HPC}^4$.

8, TITLE:       NMS by Representative Region: Towards Crowded Pedestrian Detection by Proposal Pairing
http://arxiv.org/abs/2003.12729
AUTHORS:        Xin Huang ; Zheng Ge ; Zequn Jie ; Osamu Yoshie
COMMENTS:       Accepted by CVPR2020. The first two authors contributed equally, and are listed in alphabetical order
HIGHLIGHT:      To avoid such a dilemma, this paper proposes a novel Representative Region NMS approach leveraging the less occluded visible parts, effectively removing the redundant boxes without bringing in many false positives.

9, TITLE:       Why You Cannot (Yet) Write an "Interval Arithmetic" Library in Common Lisp
http://arxiv.org/abs/2003.03831
AUTHORS:        Marco Antoniotti
COMMENTS:       11 pages, paper submitted to the European Lisp Symposium 2020, Zurich, CH
HIGHLIGHT:      Why You Cannot (Yet) Write an "Interval Arithmetic" Library in Common Lisp

10, TITLE:      A note on the parametric integer programming in the average case: sparsity, proximity, and FPT-algorithms
http://arxiv.org/abs/2002.01307
AUTHORS:        D. V. Gribanov ; D. S. Malyshev ; P. M. Pardalos
COMMENTS:       Unfortunately, previous version of the paper contains mathematical errors. Results about complexity of the unbounded knapsack problem were incorrect, so we erase them from text and add some new results
HIGHLIGHT:      We consider the Integer Linear Programming (ILP) problem $\max\{c^\top x : A x \leq b,\, x \in Z^n \}$, parameterized by a right-hand side vector $b \in Z^m$, where $A \in Z^{m \times n}$ is a matrix of the rank $n$.

11, TITLE:      AriEL: volume coding for sentence generation
http://arxiv.org/abs/2003.13600
AUTHORS:        Luca Celotti ; Simon Brodeur ; Jean Rouat
HIGHLIGHT:      We improve on the performance of some of the standard methods in deep learning to generate sentences by uniformly sampling a continuous space.

12, TITLE:      Regularized Binary Network Training
http://arxiv.org/abs/1812.11800
AUTHORS:        Sajad Darabi ; Mouloud Belbahri ; Matthieu Courbariaux ; Vahid Partovi Nia
COMMENTS:       NeurIPS19 Workshop on Energy Efficient Machine Learning and Cognitive Computing (2019)
HIGHLIGHT:      We propose to improve the binary training method, by introducing a new regularization function that encourages training weights around binary values.

13, TITLE:      BARNet: Bilinear Attention Network with Adaptive Receptive Field for Surgical Instrument Segmentation
http://arxiv.org/abs/2001.07093
AUTHORS:        Zhen-Liang Ni ; Gui-Bin Bian ; Guan-An Wang ; Xiao-Hu Zhou ; Zeng-Guang Hou ; Xiao-Liang Xie ; Zhen Li ; Yu-Han Wang
COMMENTS:       Accepted by IJCAI 2020
HIGHLIGHT:      In this paper, we propose a novel bilinear attention network with adaptive receptive field to solve these two challenges.

14, TITLE:      Comparison of object detection methods for crop damage assessment using deep learning
http://arxiv.org/abs/1912.13199
AUTHORS:        Ali HamidiSepehr ; Seyed Vahid Mirnezami ; James Ward
HIGHLIGHT:      The goal of this study was a proof-of-concept to detect damaged crop areas from aerial imagery using computer vision and deep learning techniques.

15, TITLE:      Continual Reinforcement Learning in 3D Non-stationary Environments
http://arxiv.org/abs/1905.10112
AUTHORS:        Vincenzo Lomonaco ; Karan Desai ; Eugenio Culurciello ; Davide Maltoni
COMMENTS:       Accepted in the CLVision Workshop at CVPR2020: 13 pages, 4 figures, 5 tables
HIGHLIGHT:      In this paper we propose and openly release CRLMaze, a new benchmark for learning continually through reinforcement in a complex 3D non-stationary task based on ViZDoom and subject to several environmental changes.

16, TITLE:      ContCap: A scalable framework for continual image captioning
http://arxiv.org/abs/1909.08745
AUTHORS:        Giang Nguyen ; Tae Joon Jun ; Trung Tran ; Tolcha Yalew ; Daeyoung Kim
COMMENTS:       8 pages
HIGHLIGHT:      In this work, we propose ContCap, a framework generating captions over a series of new tasks coming, seamlessly integrating continual learning into image captioning besides addressing catastrophic forgetting.

17, TITLE:      Unsupervised Detection of Distinctive Regions on 3D Shapes
http://arxiv.org/abs/1905.01684
AUTHORS:        Xianzhi Li ; Lequan Yu ; Chi-Wing Fu ; Daniel Cohen-Or ; Pheng-Ann Heng
COMMENTS:       Accepted by ACM TOG
HIGHLIGHT:      This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.

18, TITLE:      StackNet: Stacking Parameters for Continual learning
http://arxiv.org/abs/1809.02441
AUTHORS:        Jangho Kim ; Jeesoo Kim ; Nojun Kwak
COMMENTS:       CVPR 2020 Workshop on Continual Learning in Computer Vision
HIGHLIGHT:      In this paper, we propose a continual learning method that is able to learn additional tasks while retaining the performance of previously learned tasks by stacking parameters.

19, TITLE:      Inpainting via Generative Adversarial Networks for CMB data analysis
http://arxiv.org/abs/2004.04177
AUTHORS:        Alireza Vafaei Sadr ; Farida Farsian
COMMENTS:       19 pages, 21 figures. Prepared for submission to JCAP. All codes will be published after acceptance
HIGHLIGHT:      In this work, we propose a new method to inpaint the CMB signal in regions masked out following a point source extraction process.

20, TITLE:      Weakly supervised multiple instance learning histopathological tumor segmentation
http://arxiv.org/abs/2004.05024
AUTHORS:        Marvin Lerousseau ; Maria Vakalopoulou ; Marion Classe ; Julien Adam ; Enzo Battistella ; Alexandre Carré ; Théo Estienne ; Théophraste Henry ; Eric Deutsch ; Nikos Paragios
COMMENTS:       10 pages, 3 figures; added code + results url
HIGHLIGHT:      To this end, in this paper, we propose a weakly supervised framework relying on weak standard clinical practice annotations, available in most medical centers.

21, TITLE:      Computing the multilinear factors of lacunary polynomials without heights
http://arxiv.org/abs/1311.5694
AUTHORS:        Arkadev Chattopadhyay ; Bruno Grenet ; Pascal Koiran ; Natacha Portier ; Yann Strozecki
COMMENTS:       34 pages
HIGHLIGHT:      We present a deterministic algorithm which computes the multilinear factors of multivariate lacunary polynomials over number fields.

22, TITLE:      TAPAS: Weakly Supervised Table Parsing via Pre-training
http://arxiv.org/abs/2004.02349
AUTHORS:        Jonathan Herzig ; Paweł Krzysztof Nowak ; Thomas Müller ; Francesco Piccinno ; Julian Martin Eisenschlos
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this paper, we present TAPAS, an approach to question answering over tables without generating logical forms.

23, TITLE:      Self-Supervised training for blind multi-frame video denoising
http://arxiv.org/abs/2004.06957
AUTHORS:        Valéry Dewil ; Jérémy Anger ; Axel Davy ; Thibaud Ehret ; Pablo Arias ; Gabriele Facciolo
COMMENTS:       14 pages
HIGHLIGHT:      We propose a self-supervised approach for training multi-frame video denoising networks.

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

25, TITLE:      MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
http://arxiv.org/abs/1911.04464
AUTHORS:        Siddharth Bhatia ; Bryan Hooi ; Minji Yoon ; Kijung Shin ; Christos Faloutsos
COMMENTS:       8 pages, Accepted at AAAI Conference on Artificial Intelligence (AAAI), 2020 [oral paper]; minor fixes, updated experiments
HIGHLIGHT:      In this work, we propose MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data.

26, TITLE:      Perfectly Parallel Fairness Certification of Neural Networks
http://arxiv.org/abs/1912.02499
AUTHORS:        Caterina Urban ; Maria Christakis ; Valentin Wüstholz ; Fuyuan Zhang
HIGHLIGHT:      In this paper, we propose a perfectly parallel static analysis for certifying causal fairness of feed-forward neural networks used for classification of tabular data.

27, TITLE:      Pseudospectral Shattering, the Sign Function, and Diagonalization in Nearly Matrix Multiplication Time
http://arxiv.org/abs/1912.08805
AUTHORS:        Jess Banks ; Jorge Garza Vargas ; Archit Kulkarni ; Nikhil Srivastava
COMMENTS:       70 pages, 3 figures, comments welcome. Slightly edited intro from previous version + explicit statement of forward error Theorem (Corolary 1.7)
HIGHLIGHT:      (2) We rigorously analyze Roberts' Newton iteration method for computing the matrix sign function in finite arithmetic, itself an open problem in numerical analysis since at least 1986.

28, TITLE:      Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure Games
http://arxiv.org/abs/1908.04777
AUTHORS:        Xusen Yin ; Jonathan May
HIGHLIGHT:      We consider the task of learning to play families of text-based computer adventure games, i.e., fully textual environments with a common theme (e.g. cooking) and goal (e.g. prepare a meal from a recipe) but with different specifics; new instances of such games are relatively straightforward for humans to master after a brief exposure to the genre but have been curiously difficult for computer agents to learn.

29, TITLE:      Long-tail Visual Relationship Recognition with a Visiolinguistic Hubless Loss
http://arxiv.org/abs/2004.00436
AUTHORS:        Sherif Abdelkarim ; Panos Achlioptas ; Jiaji Huang ; Boyang Li ; Kenneth Church ; Mohamed Elhoseiny
HIGHLIGHT:      In this paper, we propose to study a novel task concerning the generalization of visual relationships that are on the distribution's tail, i.e. we investigate how to help AI systems to better recognize rare relationships like <S:dog, P:riding, O:horse>, where the subject S, predicate P, and/or the object O come from the tail of the corresponding distributions. To achieve this goal, we first introduce two large-scale visual-relationship detection benchmarks built upon the widely used Visual Genome and GQA datasets.

30, TITLE:      Random Bias Initialization Improves Quantized Training
http://arxiv.org/abs/1909.13446
AUTHORS:        Xinlin Li ; Vahid Partovi Nia
HIGHLIGHT:      We start with analyzing full-precision neural networks with ReLU activation and compare it with its binarized version.

31, TITLE:      Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
http://arxiv.org/abs/1907.03799
AUTHORS:        Vincenzo Lomonaco ; Davide Maltoni ; Lorenzo Pellegrini
COMMENTS:       Accepted in the CLVision Workshop at CVPR2020: 12 pages, 7 figures, 5 tables, 3 algorithms
HIGHLIGHT:      In this paper, we introduce a novel continual learning protocol based on the CORe50 benchmark and propose two rehearsal-free continual learning techniques, CWR* and AR1*, that can learn effectively even in the challenging case of nearly 400 small non-i.i.d. incremental batches.

32, TITLE:      Deep Slow Motion Video Reconstruction with Hybrid Imaging System
http://arxiv.org/abs/2002.12106
AUTHORS:        Avinash Paliwal ; Nima Khademi Kalantari
COMMENTS:       IEEE TPAMI and ICCP 2020. Project page containing code and video at http://faculty.cs.tamu.edu/nimak/Papers/ICCP2020_Slomo
HIGHLIGHT:      In this paper, we address this problem using two video streams as input; an auxiliary video with high frame rate and low spatial resolution, providing temporal information, in addition to the standard main video with low frame rate and high spatial resolution.

33, TITLE:      A New Meta-Baseline for Few-Shot Learning
http://arxiv.org/abs/2003.04390
AUTHORS:        Yinbo Chen ; Xiaolong Wang ; Zhuang Liu ; Huijuan Xu ; Trevor Darrell
COMMENTS:       Code is available on https://github.com/cyvius96/few-shot-meta-baseline
HIGHLIGHT:      We present a Meta-Baseline method, by pre-training a classifier on all base classes and meta-learning on a nearest-centroid based few-shot classification algorithm, it outperforms recent state-of-the-art methods by a large margin.

34, TITLE:      Crafty: Efficient, HTM-Compatible Persistent Transactions
http://arxiv.org/abs/2004.00262
AUTHORS:        Kaan Genç ; Michael D. Bond ; Guoqing Harry Xu
COMMENTS:       32 pages, 24 figures. To appear in PLDI 2020
HIGHLIGHT:      This paper introduces Crafty, a new approach for ensuring consistency and atomicity on persistent memory operations using commodity hardware with existing hardware transactional memory (HTM) capabilities, while incurring low overhead.

35, TITLE:      Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection
http://arxiv.org/abs/1906.01906
AUTHORS:        Stephan Rasp ; Hauke Schulz ; Sandrine Bony ; Bjorn Stevens
HIGHLIGHT:      This paper presents an example of how two tools that have recently become accessible to a wide range of researchers, crowd-sourcing and deep learning, can be combined to explore satellite imagery at scale.

36, TITLE:      Deep Molecular Programming: A Natural Implementation of Binary-Weight ReLU Neural Networks
http://arxiv.org/abs/2003.13720
AUTHORS:        Marko Vasic ; Cameron Chalk ; Sarfraz Khurshid ; David Soloveichik
HIGHLIGHT:      We discover a surprisingly tight connection between a popular class of neural networks (Binary-weight ReLU aka BinaryConnect) and a class of coupled chemical reactions that are absolutely robust to reaction rates.

37, TITLE:      FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction
http://arxiv.org/abs/2003.13989
AUTHORS:        Haotian Yang ; Hao Zhu ; Yanru Wang ; Mingkai Huang ; Qiu Shen ; Ruigang Yang ; Xun Cao
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input.

38, TITLE:      Compressed MRI Reconstruction Exploiting a Rotation-Invariant Total Variation Discretization
http://arxiv.org/abs/1911.11854
AUTHORS:        Erfan Ebrahim Esfahani ; Alireza Hosseini
HIGHLIGHT:      Inspired by the first-order method of Malitsky and Pock, we propose a new variational framework for compressed MR image reconstruction which introduces the application of a rotation-invariant discretization of total variation functional into MR imaging while exploiting BM3D frame as a sparsifying transform.

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

40, TITLE:      Learning When and Where to Zoom with Deep Reinforcement Learning
http://arxiv.org/abs/2003.00425
AUTHORS:        Burak Uzkent ; Stefano Ermon
COMMENTS:       To appear in CVPR 2020 as an Oral Presentation. The code can be found at https://github.com/ermongroup/PatchDrop
HIGHLIGHT:      In this direction, we propose PatchDrop a reinforcement learning approach to dynamically identify when and where to use/acquire high resolution data conditioned on the paired, cheap, low resolution images.

41, TITLE:      Face Recognition: Too Bias, or Not Too Bias?
http://arxiv.org/abs/2002.06483
AUTHORS:        Joseph P Robinson ; Gennady Livitz ; Yann Henon ; Can Qin ; Yun Fu ; Samson Timoner
COMMENTS:       Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020
HIGHLIGHT:      We reveal critical insights into problems of bias in state-of-the-art facial recognition (FR) systems using a novel Balanced Faces In the Wild (BFW) dataset: data balanced for gender and ethnic groups.

42, TITLE:      An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism
http://arxiv.org/abs/2004.06673
AUTHORS:        Tongxue Zhou ; Stéphane Canu ; Su Ruan
COMMENTS:       14 pages, 6figures
HIGHLIGHT:      In this paper, we propose a U-Net based segmentation network using attention mechanism.

43, TITLE:      Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem
http://arxiv.org/abs/2004.04498
AUTHORS:        Danielle Saunders ; Bill Byrne
COMMENTS:       ACL 2020
HIGHLIGHT:      During inference we propose a lattice-rescoring scheme which outperforms all systems evaluated in Stanovsky et al (2019) on WinoMT with no degradation of general test set BLEU, and we show this scheme can be applied to remove gender bias in the output of `black box` online commercial MT systems.

44, TITLE:      Process algebra, process scheduling, and mutual exclusion
http://arxiv.org/abs/2003.00473
AUTHORS:        C. A. Middelburg
COMMENTS:       15 pages, there is noticeable text overlap with earlier papers (arXiv:1912.10041, arXiv:1703.06822); 15 pages, Section 3.2 improved; 15 pages, minor improvements including replacement of reference at end Section 3.2
HIGHLIGHT:      In the current paper, we do so with the variant of ACP known as ACP$_\epsilon$.

45, TITLE:      Where is the Information in a Deep Neural Network?
http://arxiv.org/abs/1905.12213
AUTHORS:        Alessandro Achille ; Giovanni Paolini ; Stefano Soatto
HIGHLIGHT:      For the latter, we introduce a notion of effective information in the activations, which are deterministic functions of future inputs.

46, TITLE:      Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module
http://arxiv.org/abs/2004.08656
AUTHORS:        Yanlu Wei ; Renshuai Tao ; Zhangjie Wu ; Yuqing Ma ; Libo Zhang ; Xianglong Liu
COMMENTS:       10 pages, 8 figures; for data and code, see https://github.com/OPIXray-author/OPIXray
HIGHLIGHT:      We evaluate our method on the OPIXray dataset and compare it to several baselines, including popular methods for detection and attention mechanisms.

47, TITLE:      PaStaNet: Toward Human Activity Knowledge Engine
http://arxiv.org/abs/2004.00945
AUTHORS:        Yong-Lu Li ; Liang Xu ; Xinpeng Liu ; Xijie Huang ; Yue Xu ; Shiyi Wang ; Hao-Shu Fang ; Ze Ma ; Mingyang Chen ; Cewu Lu
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available: http://hake-mvig.cn/
HIGHLIGHT:      In light of this, we propose a new path: infer human part states first and then reason out the activities based on part-level semantics.

48, TITLE:      Cross-Batch Memory for Embedding Learning
http://arxiv.org/abs/1912.06798
AUTHORS:        Xun Wang ; Haozhi Zhang ; Weilin Huang ; Matthew R. Scott
COMMENTS:       CVPR 2020 Oral
HIGHLIGHT:      In this paper, we identify a "slow drift" phenomena by observing that the embedding features drift exceptionally slow even as the model parameters are updating throughout the training process.

49, TITLE:      Enhancing Machine Translation with Dependency-Aware Self-Attention
http://arxiv.org/abs/1909.03149
AUTHORS:        Emanuele Bugliarello ; Naoaki Okazaki
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      We show the efficacy of each approach on WMT English-German and English-Turkish, and WAT English-Japanese translation tasks.

50, TITLE:      Physics-as-Inverse-Graphics: Unsupervised Physical Parameter Estimation from Video
http://arxiv.org/abs/1905.11169
AUTHORS:        Miguel Jaques ; Michael Burke ; Timothy Hospedales
COMMENTS:       11 pages
HIGHLIGHT:      We propose a model that is able to perform unsupervised physical parameter estimation of systems from video, where the differential equations governing the scene dynamics are known, but labeled states or objects are not available.

51, TITLE:      Process algebra with strategic interleaving
http://arxiv.org/abs/1703.06822
AUTHORS:        J. A. Bergstra ; C. A. Middelburg
COMMENTS:       19 pages, this version is a revision of the published version
HIGHLIGHT:      Therefore, we extend ACP in this paper with the latter form of interleaving.

52, TITLE:      Combining Pretrained High-Resource Embeddings and Subword Representations for Low-Resource Languages
http://arxiv.org/abs/2003.04419
AUTHORS:        Machel Reid ; Edison Marrese-Taylor ; Yutaka Matsuo
COMMENTS:       Accepted to the "AfricaNLP - Unlocking Local Languages" workshop at ICLR 2020
HIGHLIGHT:      To help circumvent this issue, we explore techniques exploiting the qualities of morphologically rich languages (MRLs), while leveraging pretrained word vectors in well-resourced languages.

53, TITLE:      Learning to Generate Grounded Visual Captions without Localization Supervision
http://arxiv.org/abs/1906.00283
AUTHORS:        Chih-Yao Ma ; Yannis Kalantidis ; Ghassan AlRegib ; Peter Vajda ; Marcus Rohrbach ; Zsolt Kira
HIGHLIGHT:      In this work, we help the model to achieve this via a novel cyclical training regimen that forces the model to localize each word in the image after the sentence decoder generates it, and then reconstruct the sentence from the localized image region(s) to match the ground-truth.

54, TITLE:      Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks
http://arxiv.org/abs/1907.13106
AUTHORS:        Rajeev Yasarla ; Federico Perazzi ; Vishal M. Patel
COMMENTS:       Accepted at TIP 2020
HIGHLIGHT:      We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring.

55, TITLE:      Lifted multiplicity codes and the disjoint repair group property
http://arxiv.org/abs/1905.02270
AUTHORS:        Ray Li ; Mary Wootters
COMMENTS:       19 pages; A previous version claimed that a lifted code is exactly the span of all good monomials, but in fact the span of all good monomials only forms a subset of the lifted code. This does not change our main result
HIGHLIGHT:      We consider a generalization of their construction, which we call lifted multiplicity codes.

56, TITLE:      That which we call private
http://arxiv.org/abs/1908.03566
AUTHORS:        Úlfar Erlingsson ; Ilya Mironov ; Ananth Raghunathan ; Shuang Song
HIGHLIGHT:      That which we call private

57, TITLE:      Multiparty Selection
http://arxiv.org/abs/2004.05548
AUTHORS:        Ke Chen ; Adrian Dumitrescu
COMMENTS:       12 pages, 2 figures, and new section for finding an approximate median among $k$ players has been added
HIGHLIGHT:      In particular, we present a deterministic protocol for finding an approximate median among $k$ players.