2020.05.11.txt

==========New Papers==========
1, TITLE:       SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
http://arxiv.org/abs/2005.04114
AUTHORS:        Da Yin ; Tao Meng ; Kai-Wei Chang
COMMENTS:       ACL-2020
HIGHLIGHT:      We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics.

2, TITLE:       Sparsely-Labeled Source Assisted Domain Adaptation
http://arxiv.org/abs/2005.04111
AUTHORS:        Wei Wang ; Zhihui Wang ; Yuankai Xiang ; Jing Sun ; Haojie Li ; Fuming Sun ; Zhengming Ding
COMMENTS:       22 pages, 6 figures, submitted to the Pattern Recognition
HIGHLIGHT:      This paper proposes a novel Sparsely-Labeled Source Assisted Domain Adaptation (SLSA-DA) algorithm to address the challenge with limited labeled source domain samples.

3, TITLE:       Beyond Accuracy: Behavioral Testing of NLP models with CheckList
http://arxiv.org/abs/2005.04118
AUTHORS:        Marco Tulio Ribeiro ; Tongshuang Wu ; Carlos Guestrin ; Sameer Singh
HIGHLIGHT:      Inspired by principles of behavioral testing in software engineering, we introduce CheckList, a task-agnostic methodology for testing NLP models.

4, TITLE:       NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
http://arxiv.org/abs/2005.04117
AUTHORS:        Abdelrahman Abdelhamed ; Mahmoud Afifi ; Radu Timofte ; Michael S. Brown ; Yue Cao ; Zhilu Zhang ; Wangmeng Zuo ; Xiaoling Zhang ; Jiye Liu ; Wendong Chen ; Changyuan Wen ; Meng Liu ; Shuailin Lv ; Yunchao Zhang ; Zhihong Pan ; Baopu Li ; Teng Xi ; Yanwen Fan ; Xiyu Yu ; Gang Zhang ; Jingtuo Liu ; Junyu Han ; Errui Ding ; Songhyun Yu ; Bumjun Park ; Jechang Jeong ; Shuai Liu ; Ziyao Zong ; Nan Nan ; Chenghua Li ; Zengli Yang ; Long Bao ; Shuangquan Wang ; Dongwoon Bai ; Jungwon Lee ; Youngjung Kim ; Kyeongha Rho ; Changyeop Shin ; Sungho Kim ; Pengliang Tang ; Yiyun Zhao ; Yuqian Zhou ; Yuchen Fan ; Thomas Huang ; Zhihao Li ; Nisarg A. Shah ; Wei Liu ; Qiong Yan ; Yuzhi Zhao ; Marcin Możejko ; Tomasz Latkowski ; Lukasz Treszczotko ; Michał Szafraniuk ; Krzysztof Trojanowski ; Yanhong Wu ; Pablo Navarrete Michelini ; Fengshuo Hu ; Yunhua Lu ; Sujin Kim ; Wonjin Kim ; Jaayeon Lee ; Jang-Hwan Choi ; Magauiya Zhussip ; Azamat Khassenov ; Jong Hyun Kim ; Hwechul Cho ; Priya Kansal ; Sabari Nathan ; Zhangyu Ye ; Xiwen Lu ; Yaqi Wu ; Jiangxin Yang ; Yanlong Cao ; Siliang Tang ; Yanpeng Cao ; Matteo Maggioni ; Ioannis Marras ; Thomas Tanay ; Gregory Slabaugh ; Youliang Yan ; Myungjoo Kang ; Han-Soo Choi ; Kyungmin Song ; Shusong Xu ; Xiaomu Lu ; Tingniao Wang ; Chunxia Lei ; Bin Liu ; Rajat Gupta ; Vineet Kumar
HIGHLIGHT:      A total of 22 teams, proposing 24 methods, competed in the final phase of the challenge.

5, TITLE:       The ghosts of forgotten things: A study on size after forgetting
http://arxiv.org/abs/2005.04123
AUTHORS:        Paolo Liberatore
HIGHLIGHT:      This article discusses the implications of such an increase and analyzes the computational properties of the phenomenon.

6, TITLE:       K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations
http://arxiv.org/abs/2005.04120
AUTHORS:        Cheul Young Park ; Narae Cha ; Soowon Kang ; Auk Kim ; Ahsan Habib Khandoker ; Leontios Hadjileontiadis ; Alice Oh ; Yong Jeong ; Uichin Lee
COMMENTS:       20 pages, 4 figures, for associated dataset, see https://doi.org/10.5281/zenodo.3814370
HIGHLIGHT:      K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations

7, TITLE:       Data-Free Network Quantization With Adversarial Knowledge Distillation
http://arxiv.org/abs/2005.04136
AUTHORS:        Yoojin Choi ; Jihwan Choi ; Mostafa El-Khamy ; Jungwon Lee
COMMENTS:       CVPR 2020 Joint Workshop on Efficient Deep Learning in Computer Vision (EDLCV)
HIGHLIGHT:      In this paper, we consider data-free network quantization with synthetic data.

8, TITLE:       Quantum Natural Language Processing on Near-Term Quantum Computers
http://arxiv.org/abs/2005.04147
AUTHORS:        Konstantinos Meichanetzidis ; Stefano Gogioso ; Giovanni De Felice ; Nicolò Chiappori ; Alexis Toumi ; Bob Coecke
COMMENTS:       13 pages, submitted to Quantum Physics and Logic (QPL) 2020. This work was originally commissioned by Cambridge Quantum Computing (CQC) and was carried out independently by the CQC team and the Hashberg team
HIGHLIGHT:      In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP.

9, TITLE:       Hyperspectral Image Restoration via Global Total Variation Regularized Local nonconvex Low-Rank matrix Approximation
http://arxiv.org/abs/2005.04143
AUTHORS:        Haijin Zeng ; Xiaozhen Xie ; Jifeng Ning
COMMENTS:       Accepted for publication in IEEE IGARSS 2020 conference
HIGHLIGHT:      To cope with these problems, we propose a spatial-spectral TV (SSTV) regularized non-convex local LR matrix approximation (NonLLRTV) method to remove mixed noise in HSIs.

10, TITLE:      Evidence Inference 2.0: More Data, Better Models
http://arxiv.org/abs/2005.04177
AUTHORS:        Jay DeYoung ; Eric Lehman ; Ben Nye ; Iain J. Marshall ; Byron C. Wallace
COMMENTS:       Accepted as workshop paper into BioNLP
HIGHLIGHT:      In this paper, we collect additional annotations to expand the Evidence Inference dataset by 25\%, provide stronger baseline models, systematically inspect the errors that these make, and probe dataset quality.

11, TITLE:      Detecting East Asian Prejudice on Social Media
http://arxiv.org/abs/2005.03909
AUTHORS:        Bertie Vidgen ; Austin Botelho ; David Broniatowski ; Ella Guest ; Matthew Hall ; Helen Margetts ; Rebekah Tromble ; Zeerak Waseem ; Scott Hale
COMMENTS:       12 pages
HIGHLIGHT:      In this paper we report on the creation of a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class. We provide our final model (coded in Python), as well as a new 20,000 tweet training dataset used to make the classifier, two analyses of hashtags associated with East Asian prejudice and the annotation codebook.

12, TITLE:      Text Synopsis Generation for Egocentric Videos
http://arxiv.org/abs/2005.03804
AUTHORS:        Aidean Sharghi ; Niels da Vitoria Lobo ; Mubarak Shah
HIGHLIGHT:      Hence, in this work, we propose to generate a textual synopsis, consisting of a few sentences describing the most important events in a long egocentric videos.

13, TITLE:      One-Shot Object Detection without Fine-Tuning
http://arxiv.org/abs/2005.03819
AUTHORS:        Xiang Li ; Lin Zhang ; Yau Pun Chen ; Yu-Wing Tai ; Chi-Keung Tang
HIGHLIGHT:      In this paper, we attempt to enrich such categories by addressing the one-shot object detection problem, where the number of annotated training examples for learning an unseen class is limited to one.

14, TITLE:      Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction
http://arxiv.org/abs/2005.03818
AUTHORS:        Youngduck Choi ; Yoonho Na ; Youngjik Yoon ; Jonghun Shin ; Chan Bae ; Hongseok Suh ; Byungsoo Kim ; Jaewe Heo
HIGHLIGHT:      To this end, we introduce Rocket, a Tinder-like User Interface for a general class of IESs.

15, TITLE:      Comparative Analysis of Word Embeddings for Capturing Word Similarities
http://arxiv.org/abs/2005.03812
AUTHORS:        Martina Toshevska ; Frosina Stojanovska ; Jovan Kalajdjieski
COMMENTS:       Part of the 6th International Conference on Natural Language Processing (NATP 2020)
HIGHLIGHT:      In this paper, we explore different approaches for creating distributed word representations.

16, TITLE:      Y-Net for Chest X-Ray Preprocessing: Simultaneous Classification of Geometry and Segmentation of Annotations
http://arxiv.org/abs/2005.03824
AUTHORS:        John McManigle ; Raquel Bartz ; Lawrence Carin
COMMENTS:       Accepted EMBC 2020
HIGHLIGHT:      This work introduces a general pre-processing step for chest x-ray input into machine learning algorithms.

17, TITLE:      Blind Backdoors in Deep Learning Models
http://arxiv.org/abs/2005.03823
AUTHORS:        Eugene Bagdasaryan ; Vitaly Shmatikov
HIGHLIGHT:      We investigate a new method for injecting backdoors into machine learning models, based on poisoning the loss computation in the model-training code.

18, TITLE:      Projection & Probability-Driven Black-Box Attack
http://arxiv.org/abs/2005.03837
AUTHORS:        Jie Li ; Rongrong Ji ; Hong Liu ; Jianzhuang Liu ; Bineng Zhong ; Cheng Deng ; Qi Tian
COMMENTS:       CVPR2020
HIGHLIGHT:      In this paper, we propose Projection & Probability-driven Black-box Attack (PPBA) to tackle this problem by reducing the solution space and providing better optimization.

19, TITLE:      Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
http://arxiv.org/abs/2005.03832
AUTHORS:        Kelei He ; Wei Zhao ; Xingzhi Xie ; Wen Ji ; Mingxia Liu ; Zhenyu Tang ; Feng Shi ; Yang Gao ; Jun Liu ; Junfeng Zhang ; Dinggang Shen
HIGHLIGHT:      To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification.

20, TITLE:      Distilling Knowledge from Pre-trained Language Models via Text Smoothing
http://arxiv.org/abs/2005.03848
AUTHORS:        Xing Wu ; Yibing Liu ; Xiangyang Zhou ; Dianhai Yu
COMMENTS:       5 pages, 2 figures
HIGHLIGHT:      As an alternative, we propose a new method for BERT distillation, i.e., asking the teacher to generate smoothed word ids, rather than labels, for teaching the student model in knowledge distillation.

21, TITLE:      Synchronous Bidirectional Learning for Multilingual Lip Reading
http://arxiv.org/abs/2005.03846
AUTHORS:        Mingshuang Luo ; Shuang Yang ; Xilin Chen ; Zitao Liu ; Shiguang Shan
COMMENTS:       12 pages,2 figures,4 tables
HIGHLIGHT:      To make the learning process more targeted at each particular language, we introduce an extra task of predicting the language identity in the learning process.

22, TITLE:      SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
http://arxiv.org/abs/2005.03844
AUTHORS:        Zhenpei Yang ; Yuning Chai ; Dragomir Anguelov ; Yin Zhou ; Pei Sun ; Dumitru Erhan ; Sean Rafferty ; Henrik Kretzschmar
HIGHLIGHT:      In this paper, we present a simple yet effective approach to generate realistic scenario sensor data, based only on a limited amount of lidar and camera data collected by an autonomous vehicle. We also create a novel dataset that contains cases in which two self-driving vehicles observe the same scene at the same time.

23, TITLE:      Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching
http://arxiv.org/abs/2005.03860
AUTHORS:        Yujiao Shi ; Xin Yu ; Dylan Campbell ; Hongdong Li
COMMENTS:       accepted by CVPR2020
HIGHLIGHT:      Existing approaches treat the task as a pure location estimation problem by learning discriminative feature descriptors, but neglect orientation alignment.

24, TITLE:      Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention
http://arxiv.org/abs/2005.03867
AUTHORS:        Myunghun Jung ; Youngmoon Jung ; Jahyun Goo ; Hoirin Kim
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we propose a multi-task network that performs KWS and SV simultaneously to fully utilize the interrelated domain information.

25, TITLE:      Learning hierarchical behavior and motion planning for autonomous driving
http://arxiv.org/abs/2005.03863
AUTHORS:        Jingke Wang ; Yue Wang ; Dongkun Zhang ; Yezhou Yang ; Rong Xiong
HIGHLIGHT:      To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution.

26, TITLE:      Point Cloud Completion by Skip-attention Network with Hierarchical Folding
http://arxiv.org/abs/2005.03871
AUTHORS:        Xin Wen ; Tianyang Li ; Zhizhong Han ; Yu-Shen Liu
HIGHLIGHT:      To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion.

27, TITLE:      OpenEDS2020: Open Eyes Dataset
http://arxiv.org/abs/2005.03876
AUTHORS:        Cristina Palmero ; Abhishek Sharma ; Karsten Behrendt ; Kapil Krishnakumar ; Oleg V. Komogortsev ; Sachin S. Talathi
COMMENTS:       Description of dataset used in OpenEDS2020 challenge: https://research.fb.com/programs/openeds-2020-challenge/
HIGHLIGHT:      We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras.

28, TITLE:      Is an Affine Constraint Needed for Affine Subspace Clustering?
http://arxiv.org/abs/2005.03888
AUTHORS:        Chong You ; Chun-Guang Li ; Daniel P. Robinson ; Rene Vidal
COMMENTS:       ICCV 2019. Including proofs that are omitted in the conference version
HIGHLIGHT:      Subspace clustering methods based on expressing each data point as a linear combination of other data points have achieved great success in computer vision applications such as motion segmentation, face and digit clustering.

29, TITLE:      Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking
http://arxiv.org/abs/2005.03898
AUTHORS:        Lenz Belzner ; Martin Wirsing
HIGHLIGHT:      In this paper we propose Policy Synthesis under probabilistic Constraints (PSyCo), a systematic engineering method for synthesizing safe policies under probabilistic constraints with reinforcement learning and Bayesian model checking.

30, TITLE:      Machine Learning on Graphs: A Model and Comprehensive Taxonomy
http://arxiv.org/abs/2005.03675
AUTHORS:        Ines Chami ; Sami Abu-El-Haija ; Bryan Perozzi ; Christopher Ré ; Kevin Murphy
HIGHLIGHT:      Specifically, we propose a Graph Encoder Decoder Model (GRAPHEDM), which generalizes popular algorithms for semi-supervised learning on graphs (e.g. GraphSage, Graph Convolutional Networks, Graph Attention Networks), and unsupervised learning of graph representations (e.g. DeepWalk, node2vec, etc) into a single consistent approach.

31, TITLE:      Learning to Segment Actions from Observation and Narration
http://arxiv.org/abs/2005.03684
AUTHORS:        Daniel Fried ; Jean-Baptiste Alayrac ; Phil Blunsom ; Chris Dyer ; Stephen Clark ; Aida Nematzadeh
COMMENTS:       ACL 2020
HIGHLIGHT:      We apply a generative segmental model of task structure, guided by narration, to action segmentation in video.

32, TITLE:      Source-Relaxed Domain Adaptation for Image Segmentation
http://arxiv.org/abs/2005.03697
AUTHORS:        Mathilde Bateson ; Hoel Kervadec ; Jose Dolz ; Herve Lombaert ; Ismail Ben Ayed
HIGHLIGHT:      We propose a novel formulation for adapting segmentation networks, which relaxes such a constraint.

33, TITLE:      LIIR at SemEval-2020 Task 12: A Cross-Lingual Augmentation Approach for Multilingual Offensive Language Identification
http://arxiv.org/abs/2005.03695
AUTHORS:        Erfan Ghadery ; Marie-Francine Moens
HIGHLIGHT:      For other languages we propose a cross-lingual augmentation approach in order to enrich training data and we use Multilingual BERT to obtain sentence representations.

34, TITLE:      A Systematic Assessment of Syntactic Generalization in Neural Language Models
http://arxiv.org/abs/2005.03692
AUTHORS:        Jennifer Hu ; Jon Gauthier ; Peng Qian ; Ethan Wilcox ; Roger P. Levy
COMMENTS:       To appear in the Proceedings of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      We present a systematic evaluation of the syntactic knowledge of neural language models, testing 20 combinations of model types and data sizes on a set of 34 syntactic test suites.

35, TITLE:      A Hand Motion-guided Articulation and Segmentation Estimation
http://arxiv.org/abs/2005.03691
AUTHORS:        Richard Sahala Hartanto ; Ryoichi Ishikawa ; Menandro Roxas ; Takeshi Oishi
HIGHLIGHT:      In this paper, we present a method for simultaneous articulation model estimation and segmentation of an articulated object in RGB-D images using human hand motion.

36, TITLE:      Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
http://arxiv.org/abs/2005.04014
AUTHORS:        Mehmet Yamac ; Mete Ahishali ; Aysen Degerli ; Serkan Kiranyaz ; Muhammad E. H. Chowdhury ; Moncef Gabbouj
COMMENTS:       10 pages
HIGHLIGHT:      In this study, we propose a novel approach for Covid-19 recognition from chest X-ray images.

37, TITLE:      Literature Triage on Genomic Variation Publications by Knowledge-enhanced Multi-channel CNN
http://arxiv.org/abs/2005.04044
AUTHORS:        Chenhui Lv ; Qian Lu ; Xiang Zhang
HIGHLIGHT:      Some knowledge bases, including UniProtKB/Swiss-Prot and NHGRI-EBI GWAS Catalog are created for collecting concerning publications.

38, TITLE:      Computational Complexity of Synchronization under Regular Commutative Constraints
http://arxiv.org/abs/2005.04042
AUTHORS:        Stefan Hoffmann
COMMENTS:       12 pages paper + 11 pages appendix, including 2 figures
HIGHLIGHT:      Here we study the computational complexity of the constrained synchronization problem for the class of regular commutative constraint languages.

39, TITLE:      Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
http://arxiv.org/abs/2005.04069
AUTHORS:        Jiapan Gu ; Ziyuan Zhao ; Zeng Zeng ; Yuzhe Wang ; Zhengyiren Qiu ; Bharadwaj Veeravalli ; Brian Kim Poh Goh ; Glenn Kunnath Bonney ; Krishnakumar Madhavan ; Chan Wan Ying ; Lim Kheng Choon ; Thng Choon Hua ; Pierce KH Chow
COMMENTS:       Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada
HIGHLIGHT:      In this paper, we propose an end-to-end deep learning framework for mutation prediction in APOB, COL11A1 and ATRX genes using multiphasic CT scans.

40, TITLE:      Communication memento: Memoryless communication complexity
http://arxiv.org/abs/2005.04068
AUTHORS:        Srinivasan Arunachalam ; Supartha Podder
COMMENTS:       27 Pages
HIGHLIGHT:      In this paper, we establish following: (1) We show that the memoryless communication complexity of $F$ characterizes the logarithm of the size of the smallest bipartite branching program computing $F$ (up to a factor 2); (2) We give exponential separations between the classical variants of memoryless communication models; (3) We exhibit exponential quantum-classical separations in the four variants of the memoryless communication model.

41, TITLE:      Active Preference Learning using Maximum Regret
http://arxiv.org/abs/2005.04067
AUTHORS:        Nils Wilde ; Dana Kulic ; Stephen L. Smith
HIGHLIGHT:      We study active preference learning as a framework for intuitively specifying the behaviour of autonomous robots.

42, TITLE:      Fast Automatic Visibility Optimization for Thermal Synthetic Aperture Visualization
http://arxiv.org/abs/2005.04065
AUTHORS:        Indrajit Kurmi ; David C. Schedl ; Oliver Bimber
COMMENTS:       5 pages, 4 figures, 1 table, in IEEE Geoscience and Remote Sensing Letters, 2020
HIGHLIGHT:      In this article, we describe and validate the first fully automatic parameter optimization for thermal synthetic aperture visualization.

43, TITLE:      TSDM: Tracking by SiamRPN++ with a Depth-refiner and a Mask-generator
http://arxiv.org/abs/2005.04063
AUTHORS:        Pengyao Zhao ; Quanli Liu ; Wei Wang ; Qiang Guo
COMMENTS:       6 Pages, 6 Figures, 2 Tables
HIGHLIGHT:      In this paper, a RGB-D tracker named TSDM is proposed, which is composed of a Mask-generator (M-g), SiamRPN++ and a Depth-refiner (D-r).

44, TITLE:      A Sim2Real Deep Learning Approach for the Transformation of Images from Multiple Vehicle-Mounted Cameras to a Semantically Segmented Image in Bird's Eye View
http://arxiv.org/abs/2005.04078
AUTHORS:        Lennart Reiher ; Bastian Lampe ; Lutz Eckstein
COMMENTS:       Accepted to be published as part of the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, September 20-23, 2020
HIGHLIGHT:      This paper describes a methodology to obtain a corrected 360{\deg} BEV image given images from multiple vehicle-mounted cameras.

45, TITLE:      Learning to Detect Unacceptable Machine Translations for Downstream Tasks
http://arxiv.org/abs/2005.03925
AUTHORS:        Meng Zhang ; Xin Jiang ; Yang Liu ; Qun Liu
HIGHLIGHT:      In this work, we put machine translation in a cross-lingual pipeline and introduce downstream tasks to define task-specific acceptability of machine translations.

46, TITLE:      Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation
http://arxiv.org/abs/2005.03924
AUTHORS:        Shuchao Pang ; Anan Du ; Mehmet A. Orgun ; Yan Wang ; Quanzheng Sheng ; Shoujin Wang ; Xiaoshui Huang ; Zhemei Yu
HIGHLIGHT:      To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.

47, TITLE:      Context-Sensitive Generation Network for Handing Unknown Slot Values in Dialogue State Tracking
http://arxiv.org/abs/2005.03923
AUTHORS:        Puhai Yang ; Heyan Huang ; Xian-Ling Mao
HIGHLIGHT:      To tackle the problem, in this paper, we propose a novel Context-Sensitive Generation network (CSG) which can facilitate the representation of out-of-vocabulary words when generating the unknown slot value.

48, TITLE:      Learning Generalized Spoof Cues for Face Anti-spoofing
http://arxiv.org/abs/2005.03922
AUTHORS:        Haocheng Feng ; Zhibin Hong ; Haixiao Yue ; Yang Chen ; Keyao Wang ; Junyu Han ; Jingtuo Liu ; Errui Ding
COMMENTS:       16 pages
HIGHLIGHT:      In this paper, we reformulate FAS in an anomaly detection perspective and propose a residual-learning framework to learn the discriminative live-spoof differences which are defined as the spoof cues.

49, TITLE:      Efficient convolutional neural networks with smaller filters for human activity recognition using wearable sensors
http://arxiv.org/abs/2005.03948
AUTHORS:        Yin Tang ; Qi Teng ; Lei Zhang ; Fuhong Min ; Jun He
COMMENTS:       10 pages, 10 figures
HIGHLIGHT:      In the paper, inspired by the idea, we proposed a lightweight CNN using re-designed Lego filters for the use of HAR.

50, TITLE:      Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
http://arxiv.org/abs/2005.03947
AUTHORS:        Trung B. Nguyen ; Will N. Browne ; Mengjie Zhang
COMMENTS:       accepted by The Genetic and Evolutionary Computation Conference (GECCO 2020)
HIGHLIGHT:      We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs.

51, TITLE:      RetinaMask: A Face Mask detector
http://arxiv.org/abs/2005.03950
AUTHORS:        Mingjie Jiang ; Xinqi Fan
HIGHLIGHT:      To contribute to public healthcare for human beings, we propose RetinaMask, which is a high-accuracy and efficient face mask detector.

52, TITLE:      On Vocabulary Reliance in Scene Text Recognition
http://arxiv.org/abs/2005.03959
AUTHORS:        Zhaoyi Wan ; Jielei Zhang ; Liang Zhang ; Jiebo Luo ; Cong Yao
COMMENTS:       CVPR'20
HIGHLIGHT:      In this paper, we establish an analytical framework to conduct an in-depth study on the problem of vocabulary reliance in scene text recognition.

53, TITLE:      Neural Object Learning for 6D Pose Estimation Using a Few Cluttered Images
http://arxiv.org/abs/2005.03717
AUTHORS:        Kiru Park ; Timothy Patten ; Markus Vincze
HIGHLIGHT:      This paper proposes a method, Neural Object Learning (NOL), that creates synthetic images of objects in arbitrary poses by combining only a few observations from cluttered images.

54, TITLE:      Towards Conversational Recommendation over Multi-Type Dialogs
http://arxiv.org/abs/2005.03954
AUTHORS:        Zeming Liu ; Haifeng Wang ; Zheng-Yu Niu ; Hua Wu ; Wanxiang Che ; Ting Liu
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.

55, TITLE:      SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
http://arxiv.org/abs/2005.03724
AUTHORS:        Yang Gao ; Wei Zhao ; Steffen Eger
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques.

56, TITLE:      On the complexity of computing integral bases of function fields
http://arxiv.org/abs/2005.03964
AUTHORS:        Simon Abelard
COMMENTS:       Preliminary version
HIGHLIGHT:      We study the problem of computing an integral basis of the algebraic function field $K(\mathcal{C})$ and give new complexity bounds for three known algorithms dealing with this problem.

57, TITLE:      Recognizing Magnification Levels in Microscopic Snapshots
http://arxiv.org/abs/2005.03748
AUTHORS:        Manit Zaveri ; Shivam Kalra ; Morteza Babaie ; Sultaan Shah ; Savvas Damskinos ; Hany Kashani ; H. R. Tizhoosh
COMMENTS:       4 pages, 3 figures, 1 table
HIGHLIGHT:      In this paper, we extract deep features of the images available on TCGA dataset with known magnification to train a classifier for magnification recognition.

58, TITLE:      Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture
http://arxiv.org/abs/2005.03743
AUTHORS:        Hao Sheng ; Xiao Chen ; Jingyi Su ; Ram Rajagopal ; Andrew Ng
COMMENTS:       CVPR 2020 - Vision for Agriculture; https://www.agriculture-vision.com
HIGHLIGHT:      In this paper, we propose a novel, model-agnostic, data-fusion approach for vegetation-related computer vision tasks.

59, TITLE:      Lenia and Expanded Universe
http://arxiv.org/abs/2005.03742
AUTHORS:        Bert Wang-Chak Chan
COMMENTS:       8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conference
HIGHLIGHT:      We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns.

60, TITLE:      Sentiment Analysis Using Simplified Long Short-term Memory Recurrent Neural Networks
http://arxiv.org/abs/2005.03993
AUTHORS:        Karthik Gopalakrishnan ; Fathi M. Salem
COMMENTS:       6 pages, 6 figures, 6 tables
HIGHLIGHT:      In this work, we perform sentiment analysis on a GOP Debate Twitter dataset.

61, TITLE:      Introduction of a new Dataset and Method for Detecting and Counting the Pistachios based on Deep Learning
http://arxiv.org/abs/2005.03990
AUTHORS:        Mohammad Rahimzadeh ; Abolfazl Attar
COMMENTS:       The dataset and the code of this paper is available on: https://github.com/mr7495/Pesteh-Set https://github.com/mr7495/Pistachio-Counting
HIGHLIGHT:      In this paper, we have introduced and shared a new dataset of pistachios, which is called Pesteh-Set.

62, TITLE:      FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization
http://arxiv.org/abs/2005.03754
AUTHORS:        Esin Durmus ; He He ; Mona Diab
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We tackle the problem of evaluating faithfulness of a generated summary given its source document.

63, TITLE:      NetPyNE implementation and rescaling of the Potjans-Diesmanncortical microcircuit model
http://arxiv.org/abs/2005.03764
AUTHORS:        Cecilia Romaro ; Fernando Araujo Najman ; William W Lytton ; Antonio C Roque ; Salvador Dura-Bernal
HIGHLIGHT:      The Potjans-Diesmann cortical microcircuit model is a widely used model originallyimplemented in NEST.

64, TITLE:      Phonotactic Complexity and its Trade-offs
http://arxiv.org/abs/2005.03774
AUTHORS:        Tiago Pimentel ; Brian Roark ; Ryan Cotterell
COMMENTS:       Published in TACL: https://doi.org/10.1162/tacl_a_00296
HIGHLIGHT:      We present methods for calculating a measure of phonotactic complexity---bits per phoneme---that permits a straightforward cross-linguistic comparison.

65, TITLE:      Planning from Images with Deep Latent Gaussian Process Dynamics
http://arxiv.org/abs/2005.03770
AUTHORS:        Nathanael Bosch ; Jan Achterhold ; Laura Leal-Taixé ; Jörg Stückler
COMMENTS:       Accepted for publication at the 2nd Annual Conference on Learning for Dynamics and Control (L4DC) 2020, with supplementary material. First two authors contributed equally
HIGHLIGHT:      We propose to learn a deep latent Gaussian process dynamics (DLGPD) model that learns low-dimensional system dynamics from environment interactions with visual observations.

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

67, TITLE:      A Gaussian Process Upsampling Model for Improvements in Optical Character Recognition
http://arxiv.org/abs/2005.03780
AUTHORS:        Steven I Reeves ; Dongwook Lee ; Anurag Singh ; Kunal Verma
COMMENTS:       12 pages, 7 figures, 1 table
HIGHLIGHT:      In this paper, we illustrate the efficacy of a Gaussian Process upsampling model for the purposes of improving OCR and extraction through upsampling low resolution documents.

68, TITLE:      Reinforcement Learning with Feedback Graphs
http://arxiv.org/abs/2005.03789
AUTHORS:        Christoph Dann ; Yishay Mansour ; Mehryar Mohri ; Ayush Sekhari ; Karthik Sridharan
HIGHLIGHT:      We study episodic reinforcement learning in Markov decision processes when the agent receives additional feedback per step in the form of several transition observations.

69, TITLE:      ProSelfLC: Progressive Self Label Correction for Target Revising in Label Noise
http://arxiv.org/abs/2005.03788
AUTHORS:        Xinshao Wang ; Yang Hua ; Elyor Kodirov ; Neil M. Robertson
COMMENTS:       Target revising, softer targets, entropy regularisation, EM algorithm. A label distribution defines both the semantic class and similarity structure!
HIGHLIGHT:      In this work, we address robust deep learning under label noise (semi-supervised learning) from the perspective of target revising.

70, TITLE:      MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems
http://arxiv.org/abs/2005.03795
AUTHORS:        Anuradha Kar
COMMENTS:       https://github.com/anuradhakar49/MLGaze
HIGHLIGHT:      In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models.

71, TITLE:      Federated Generative Adversarial Learning
http://arxiv.org/abs/2005.03793
AUTHORS:        Chenyou Fan ; Ping Liu
HIGHLIGHT:      To handle those problems, we propose a novel generative learning scheme utilizing a federated learning framework.

72, TITLE:      BCI-Controlled Hands-Free Wheelchair Navigation with Obstacle Avoidance
http://arxiv.org/abs/2005.04209
AUTHORS:        Ramy Mounir ; Redwan Alqasemi ; Rajiv Dubey
COMMENTS:       Accepted by IROS 2018 workshop on "Haptic-enabled shared control of robotic systems: a compromise between teleoperation and autonomy"
HIGHLIGHT:      BCI-Controlled Hands-Free Wheelchair Navigation with Obstacle Avoidance

73, TITLE:      Condensed Movies: Story Based Retrieval with Contextual Embeddings
http://arxiv.org/abs/2005.04208
AUTHORS:        Max Bain ; Arsha Nagrani ; Andrew Brown ; Andrew Zisserman
HIGHLIGHT:      Our objective in this work is the long range understanding of the narrative structure of movies. To this end, we make the following four contributions: (i) We create the Condensed Movie Dataset (CMD) consisting of the key scenes from over 3K movies: each key scene is accompanied by a high level semantic description of the scene, character face tracks, and metadata about the movie.

74, TITLE:      The critical locus of overparameterized neural networks
http://arxiv.org/abs/2005.04210
AUTHORS:        Y. Cooper
HIGHLIGHT:      In this paper, we work toward a better understanding of the geometry of the loss function $L$ of overparameterized feedforward neural networks.



==========Updates to Previous Papers==========
1, TITLE:       Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
http://arxiv.org/abs/1911.09837
AUTHORS:        Jianyu Su ; Peter A. Beling ; Rui Guo ; Kyungtae Han
COMMENTS:       Accepted by ITSC 2020
HIGHLIGHT:      This paper proposes novel approaches to the acceleration prediction problem.

2, TITLE:       Oriented Objects as pairs of Middle Lines
http://arxiv.org/abs/1912.10694
AUTHORS:        Haoran Wei ; Yue Zhang ; Zhonghan Chang ; Hao Li ; Hongqi Wang ; Xian Sun
HIGHLIGHT:      In this paper, we propose a novel model named Oriented Objects Detection Network O^2-DNet to detect oriented objects by predicting a pair of middle lines inside each target.

3, TITLE:       Rolling-Unrolling LSTMs for Action Anticipation from First-Person Video
http://arxiv.org/abs/2005.02190
AUTHORS:        Antonino Furnari ; Giovanni Maria Farinella
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:1905.09035
HIGHLIGHT:      In this paper, we tackle the problem of egocentric action anticipation, i.e., predicting what actions the camera wearer will perform in the near future and which objects they will interact with.

4, TITLE:       Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
http://arxiv.org/abs/1906.03843
AUTHORS:        YooJung Choi ; Golnoosh Farnadi ; Behrouz Babaki ; Guy Van den Broeck
HIGHLIGHT:      In this paper, we study fairness of naive Bayes classifiers, which allow partial observations.

5, TITLE:       Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts
http://arxiv.org/abs/1803.04488
AUTHORS:        Faisal Alshargi ; Saeedeh Shekarpour ; Tommaso Soru ; Amit Sheth
COMMENTS:       Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)
HIGHLIGHT:      In this paper, we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts: (i) the categorization aspect, (ii) the hierarchical aspect, and (iii) the relational aspect.

6, TITLE:       A Novel CNN-based Method for Accurate Ship Detection in HR Optical Remote Sensing Images via Rotated Bounding Box
http://arxiv.org/abs/2004.07124
AUTHORS:        Linhao Li ; Zhiqiang Zhou ; Bo Wang ; Lingjuan Miao ; Hua Zong
HIGHLIGHT:      In this paper, a novel CNN-based ship detection method is proposed, by overcoming some common deficiencies of current CNN-based methods in ship detection.

7, TITLE:       Temporal Constraint Satisfaction Problems in Fixed-Point Logic
http://arxiv.org/abs/2002.09451
AUTHORS:        Manuel Bodirsky ; Wied Pakusa ; Jakub Rydval
COMMENTS:       67 pages
HIGHLIGHT:      We prove that there is no Maltsev condition that characterizes Datalog already for the CSPs of first-order reducts of (Q;<); such CSPs are called temporal CSPs and are of fundamental importance in infinite-domain constraint satisfaction.

8, TITLE:       Automatic Detection of Generated Text is Easiest when Humans are Fooled
http://arxiv.org/abs/1911.00650
AUTHORS:        Daphne Ippolito ; Daniel Duckworth ; Chris Callison-Burch ; Douglas Eck
COMMENTS:       ACL 2020 Camera Ready
HIGHLIGHT:      Here, we perform careful benchmarking and analysis of three popular sampling-based decoding strategies---top-$k$, nucleus sampling, and untruncated random sampling---and show that improvements in decoding methods have primarily optimized for fooling humans.

9, TITLE:       Retrofitting Parallelism onto OCaml
http://arxiv.org/abs/2004.11663
AUTHORS:        KC Sivaramakrishnan ; Stephen Dolan ; Leo White ; Sadiq Jaffer ; Tom Kelly ; Anmol Sahoo ; Sudha Parimala ; Atul Dhiman ; Anil Madhavapeddy
COMMENTS:       Submitted to ICFP 2020
HIGHLIGHT:      To this end, the paper presents a series of novel techniques and demonstrates that the new GC strikes a balance between performance and feature backwards compatibility for sequential programs and scales admirably on modern multicore processors.

10, TITLE:      Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers
http://arxiv.org/abs/2002.09854
AUTHORS:        Huanneng Qiu ; Matthew Garratt ; David Howard ; Sreenatha Anavatti
COMMENTS:       GECCO2020
HIGHLIGHT:      Here we try to overcome the transfer problem from a different perspective, by designing a spiking neurocontroller which uses synaptic plasticity to cross the reality gap via online adaptation.

11, TITLE:      Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
http://arxiv.org/abs/2004.07443
AUTHORS:        Weiyi Xie ; Colin Jacobs ; Jean-Paul Charbonnier ; Bram van Ginneken
HIGHLIGHT:      In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module.

12, TITLE:      ViewSynth: Learning Local Features from Depth using View Synthesis
http://arxiv.org/abs/1911.10248
AUTHORS:        Jisan Mahmud ; Rajat Vikram Singh ; Peri Akiva ; Spondon Kundu ; Kuan-Chuan Peng ; Jan-Michael Frahm
COMMENTS:       technical report; 14 pages, 4 figures, 5 tables
HIGHLIGHT:      To address the limitations of these methods, we propose a framework ViewSynth, to jointly learn: (1) viewpoint invariant keypoint-descriptor from depth images using a proposed Contrastive Matching Loss, and (2) view synthesis of depth images from different viewpoints using the proposed View Synthesis Module and View Synthesis Loss.

13, TITLE:      Visualisation and knowledge discovery from interpretable models
http://arxiv.org/abs/2005.03632
AUTHORS:        Sreejita Ghosh ; Peter Tino ; Kerstin Bunte
COMMENTS:       Accepted for proceedings of the International Joint Conference on Neural Networks (IJCNN) 2020
HIGHLIGHT:      In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem.

14, TITLE:      What do you mean, BERT? Assessing BERT as a Distributional Semantics Model
http://arxiv.org/abs/1911.05758
AUTHORS:        Timothee Mickus ; Denis Paperno ; Mathieu Constant ; Kees van Deemter
HIGHLIGHT:      In this work, we focus on BERT, a deep neural network that produces contextualized embeddings and has set the state-of-the-art in several semantic tasks, and study the semantic coherence of its embedding space.

15, TITLE:      Keyframing the Future: Keyframe Discovery for Visual Prediction and Planning
http://arxiv.org/abs/1904.05869
AUTHORS:        Karl Pertsch ; Oleh Rybkin ; Jingyun Yang ; Shenghao Zhou ; Konstantinos G. Derpanis ; Kostas Daniilidis ; Joseph Lim ; Andrew Jaegle
COMMENTS:       Conference on Learning for Dynamics and Control, 2020. Website: https://sites.google.com/view/keyin/home
HIGHLIGHT:      We propose a model that learns to discover these important events and the times when they occur and uses them to represent the full sequence.

16, TITLE:      WikiUMLS: Aligning UMLS to Wikipedia via Cross-lingual Neural Ranking
http://arxiv.org/abs/2005.01281
AUTHORS:        Afshin Rahimi ; Timothy Baldwin ; Karin Verspoor
HIGHLIGHT:      We propose a cross-lingual neural reranking model to match a UMLS concept with a Wikipedia page, which achieves a recall@1 of 71%, a substantial improvement of 20% over word- and char-level BM25, enabling manual alignment with minimal effort.

17, TITLE:      Lake Ice Monitoring with Webcams and Crowd-Sourced Images
http://arxiv.org/abs/2002.07875
AUTHORS:        Rajanie Prabha ; Manu Tom ; Mathias Rothermel ; Emmanuel Baltsavias ; Laura Leal-Taixe ; Konrad Schindler
COMMENTS:       Accepted for ISPRS Congress 2020, Nice, France
HIGHLIGHT:      As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.

18, TITLE:      Learning efficient structured dictionary for image classification
http://arxiv.org/abs/2002.03271
AUTHORS:        Zi-Qi Li ; Jun Sun ; Xiao-Jun Wu ; He-Feng Yin
COMMENTS:       Journal of Electronic Imaging (major revision)
HIGHLIGHT:      In this paper, we present an efficient structured dictionary learning (ESDL) method which takes both the diversity and label information of training samples into account.

19, TITLE:      ATFaceGAN: Single Face Image Restoration and Recognition from Atmospheric Turbulence
http://arxiv.org/abs/1910.03119
AUTHORS:        Chun Pong Lau ; Hossein Souri ; Rama Chellappa
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      To mitigate the degradation due to turbulence which includes deformation and blur, we propose a generative single frame restoration algorithm which disentangles the blur and deformation due to turbulence and reconstructs a restored image.

20, TITLE:      DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
http://arxiv.org/abs/2003.06777
AUTHORS:        Chi Zhang ; Yujun Cai ; Guosheng Lin ; Chunhua Shen
COMMENTS:       Extended version of DeepEMD in CVPR2020 (oral)
HIGHLIGHT:      In this work, we develop methods for few-shot image classification from a new perspective of optimal matching between image regions.

21, TITLE:      On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
http://arxiv.org/abs/2005.01196
AUTHORS:        Wei Zhao ; Goran Glavaš ; Maxime Peyrard ; Yang Gao ; Robert West ; Steffen Eger
COMMENTS:       ACL2020 Camera Ready (v2: several small fixes, e.g., Unicode errors)
HIGHLIGHT:      In this paper, we concern ourselves with reference-free machine translation (MT) evaluation where we directly compare source texts to (sometimes low-quality) system translations, which represents a natural adversarial setup for multilingual encoders.

22, TITLE:      Vid2Curve: Simultaneously Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
http://arxiv.org/abs/2005.03372
AUTHORS:        Peng Wang ; Lingjie Liu ; Nenglun Chen ; Hung-Kuo Chu ; Christian Theobalt ; Wenping Wang
COMMENTS:       Accepted by SIGGRAPH 2020
HIGHLIGHT:      Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on.

23, TITLE:      Modeling Online Discourse with Coupled Distributed Topics
http://arxiv.org/abs/1809.07282
AUTHORS:        Nikita Srivatsan ; Zachary Wojtowicz ; Taylor Berg-Kirkpatrick
COMMENTS:       EMNLP 2018
HIGHLIGHT:      In this paper, we propose a deep, globally normalized topic model that incorporates structural relationships connecting documents in socially generated corpora, such as online forums.

24, TITLE:      Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
http://arxiv.org/abs/2004.09036
AUTHORS:        Yefei Zha ; Ruobing Li ; Hui Lin
COMMENTS:       To appear at ACL 2020 (long paper)
HIGHLIGHT:      In this paper, we propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts.

25, TITLE:      Fact-based Dialogue Generation with Convergent and Divergent Decoding
http://arxiv.org/abs/2005.03174
AUTHORS:        Ryota Tanaka ; Akinobu Lee
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      Various methods were proposed to focus on generating informative words that contain facts effectively.

26, TITLE:      Self-Augmentation: Generalizing Deep Networks to Unseen Classes for Few-Shot Learning
http://arxiv.org/abs/2004.00251
AUTHORS:        Jin-Woo Seo ; Hong-Gyu Jung ; Seong-Whan Lee
COMMENTS:       The first two authors contributed equally to this work
HIGHLIGHT:      To tackle this issue, we propose self-augmentation that consolidates self-mix and self-distillation.

27, TITLE:      Self-Attention with Cross-Lingual Position Representation
http://arxiv.org/abs/2004.13310
AUTHORS:        Liang Ding ; Longyue Wang ; Dacheng Tao
COMMENTS:       To appear in ACL 2020
HIGHLIGHT:      In this paper, we augment SANs with \emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence.

28, TITLE:      Area Attention
http://arxiv.org/abs/1810.10126
AUTHORS:        Yang Li ; Lukasz Kaiser ; Samy Bengio ; Si Si
COMMENTS:       @InProceedings{pmlr-v97-li19e, title = {Area Attention}, author = {Li, Yang and Kaiser, Lukasz and Bengio, Samy and Si, Si}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {3846--3855}, year = {2019}, volume = {97}, series = {Proceedings of Machine Learning Research}, publisher = {PMLR} }
HIGHLIGHT:      We propose area attention: a way to attend to areas in the memory, where each area contains a group of items that are structurally adjacent, e.g., spatially for a 2D memory such as images, or temporally for a 1D memory such as natural language sentences.

29, TITLE:      Tatamibari is NP-complete
http://arxiv.org/abs/2003.08331
AUTHORS:        Aviv Adler ; Jeffrey Bosboom ; Erik D. Demaine ; Martin L. Demaine ; Quanquan C. Liu ; Jayson Lynch
COMMENTS:       26 pages, 21 figures. New discussion of safe placement of wires in Sections 3.2 and 3.5. To appear at the 10th International Conference on Fun with Algorithms (FUN 2020)
HIGHLIGHT:      Along the way, we introduce a gadget framework for proving hardness of similar puzzles involving area coverage, and show that it applies to an existing NP-hardness proof for Spiral Galaxies.

30, TITLE:      Deep Global Registration
http://arxiv.org/abs/2004.11540
AUTHORS:        Christopher Choy ; Wei Dong ; Vladlen Koltun
COMMENTS:       Accepted for CVPR'20 oral presentation
HIGHLIGHT:      We present Deep Global Registration, a differentiable framework for pairwise registration of real-world 3D scans.

31, TITLE:      Exploring Content Selection in Summarization of Novel Chapters
http://arxiv.org/abs/2005.01840
AUTHORS:        Faisal Ladhak ; Bryan Li ; Yaser Al-Onaizan ; Kathleen McKeown
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.

32, TITLE:      Meta-Learning Initializations for Image Segmentation
http://arxiv.org/abs/1912.06290
AUTHORS:        Sean M. Hendryx ; Andrew B. Leach ; Paul D. Hein ; Clayton T. Morrison
HIGHLIGHT:      We extend first-order model agnostic meta-learning algorithms (including FOMAML and Reptile) to image segmentation, present a novel neural network architecture built for fast learning which we call EfficientLab, and leverage a formal definition of the test error of meta-learning algorithms to decrease error on out of distribution tasks. We also construct a small benchmark dataset, FP-k, for the empirical study of how meta-learning systems perform in both few- and many-shot settings.

33, TITLE:      Program Sketching with Live Bidirectional Evaluation
http://arxiv.org/abs/1911.00583
AUTHORS:        Justin Lubin ; Nick Collins ; Cyrus Omar ; Ravi Chugh
HIGHLIGHT:      We present a system called Smyth for program sketching in a typed functional language whereby the concrete evaluation of ordinary assertions gives rise to input-output examples, which are then used to guide the search to complete the holes.

34, TITLE:      Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network
http://arxiv.org/abs/1904.03182
AUTHORS:        Valentin Peretroukhin ; Brandon Wagstaff ; Matthew Giamou ; Jonathan Kelly
COMMENTS:       A shortened version of this work appears in the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'19) Workshop on Uncertainty and Robustness in Deep Visual Learning, Long Beach, California, USA, Jun. 16-20 2019, pp. 83-86
HIGHLIGHT:      In this work, we present a method to extract probabilistic estimates of rotation from deep regression models.

35, TITLE:      More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction
http://arxiv.org/abs/2004.03186
AUTHORS:        Xu Han ; Tianyu Gao ; Yankai Lin ; Hao Peng ; Yaoliang Yang ; Chaojun Xiao ; Zhiyuan Liu ; Peng Li ; Maosong Sun ; Jie Zhou
HIGHLIGHT:      In this paper, we look back at existing RE methods, analyze key challenges we are facing nowadays, and show promising directions towards more powerful RE.

36, TITLE:      Enhancing Answer Boundary Detection for Multilingual Machine Reading Comprehension
http://arxiv.org/abs/2004.14069
AUTHORS:        Fei Yuan ; Linjun Shou ; Xuanyu Bai ; Ming Gong ; Yaobo Liang ; Nan Duan ; Yan Fu ; Daxin Jiang
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      In this paper, we propose two auxiliary tasks in the fine-tuning stage to create additional phrase boundary supervision: (1) A mixed MRC task, which translates the question or passage to other languages and builds cross-lingual question-passage pairs; (2) A language-agnostic knowledge masking task by leveraging knowledge phrases mined from web.

37, TITLE:      TransSent: Towards Generation of Structured Sentences with Discourse Marker
http://arxiv.org/abs/1909.05364
AUTHORS:        Xing Wu ; Dongjun Wei ; Liangjun Zang ; Jizhong Han ; Songlin Hu
COMMENTS:       5 figures
HIGHLIGHT:      Therefore, we propose a task that mimics this process, called discourse transfer.

38, TITLE:      Emerging Cross-lingual Structure in Pretrained Language Models
http://arxiv.org/abs/1911.01464
AUTHORS:        Shijie Wu ; Alexis Conneau ; Haoran Li ; Luke Zettlemoyer ; Veselin Stoyanov
COMMENTS:       ACL 2020
HIGHLIGHT:      We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of several factors that influence why these models are so effective for cross-lingual transfer.

39, TITLE:      Pre-train and Plug-in: Flexible Conditional Text Generation with Variational Auto-Encoders
http://arxiv.org/abs/1911.03882
AUTHORS:        Yu Duan ; Canwen Xu ; Jiaxin Pei ; Jialong Han ; Chenliang Li
COMMENTS:       Accepted as a long paper at ACL 2020
HIGHLIGHT:      In this paper, we present a new framework named Pre-train and Plug-in Variational Auto-Encoder (PPVAE) towards flexible conditional text generation.

40, TITLE:      The Danish Gigaword Project
http://arxiv.org/abs/2005.03521
AUTHORS:        Leon Strømberg-Derczynski ; Rebekah Baglini ; Morten H. Christiansen ; Manuel R. Ciosici ; Jacob Aarup Dalsgaard ; Riccardo Fusaroli ; Peter Juel Henrichsen ; Rasmus Hvingelby ; Andreas Kirkedal ; Alex Speed Kjeldsen ; Claus Ladefoged ; Finn Årup Nielsen ; Malte Lau Petersen ; Jonathan Hvithamar Rystrøm ; Daniel Varab
HIGHLIGHT:      This paper describes the Danish Gigaword project, which aims to construct a freely-available one billion word corpus of Danish text that represents the breadth of the written language.

41, 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:       To be published in Proceedings of the the 21st International Conference on Artificial Intelligence in Education (AIED 2020)
HIGHLIGHT:      We propose a machine learning approach to generate personalized feedback, which takes individual needs of students into account.

42, TITLE:      Cloud-Based Face and Speech Recognition for Access Control Applications
http://arxiv.org/abs/2004.11168
AUTHORS:        Nathalie Tkauc ; Thao Tran ; Kevin Hernandez-Diaz ; Fernando Alonso-Fernandez
COMMENTS:       Published at Proc. 6th International Workshop on Security and Privacy in the Cloud, SPC, in conjunction with IEEE Conference on Communications and Network Security, CNS, Avignon, France, 29 June - 1 July 2020
HIGHLIGHT:      This paper describes the implementation of a system to recognize employees and visitors wanting to gain access to a physical office through face images and speech-to-text recognition.

43, TITLE:      PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry
http://arxiv.org/abs/2003.07723
AUTHORS:        Thomas Haider ; Steffen Eger ; Evgeny Kim ; Roman Klinger ; Winfried Menninghaus
COMMENTS:       Emotion, Aesthetic Emotions, Literature, Poetry, Annotation, Corpora, Emotion Recognition, Multi-Label
HIGHLIGHT:      Most approaches to emotion analysis of social media, literature, news, and other domains focus exclusively on basic emotion categories as defined by Ekman or Plutchik.

44, TITLE:      MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
http://arxiv.org/abs/2005.03545
AUTHORS:        Devamanyu Hazarika ; Roger Zimmermann ; Soujanya Poria
HIGHLIGHT:      In this paper, we aim to learn effective modality representations to aid the process of fusion.

45, TITLE:      Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
http://arxiv.org/abs/2005.03572
AUTHORS:        Zhaohui Zheng ; Ping Wang ; Dongwei Ren ; Wei Liu ; Rongguang Ye ; Qinghua Hu ; Wangmeng Zuo
COMMENTS:       All the source code and trained models are available at https://github.com/Zzh-tju/CIoU arXiv admin note: text overlap with arXiv:1911.08287
HIGHLIGHT:      In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.

46, TITLE:      Targeted Attack for Deep Hashing based Retrieval
http://arxiv.org/abs/2004.07955
AUTHORS:        Jiawang Bai ; Bin Chen ; Yiming Li ; Dongxian Wu ; Weiwei Guo ; Shu-tao Xia ; En-hui Yang
COMMENTS:       21 pages
HIGHLIGHT:      In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.

47, TITLE:      Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines
http://arxiv.org/abs/2005.03106
AUTHORS:        Gabriel Salomon ; Rayson Laroca ; David Menotti
COMMENTS:       Accepted for presentation at the 2020 International Joint Conference on Neural Networks (IJCNN)
HIGHLIGHT:      Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters.

48, TITLE:      DMCP: Differentiable Markov Channel Pruning for Neural Networks
http://arxiv.org/abs/2005.03354
AUTHORS:        Shaopeng Guo ; Yujie Wang ; Quanquan Li ; Junjie Yan
COMMENTS:       CVPR2020 Oral. Code has been released at https://github.com/zx55/dmcp
HIGHLIGHT:      In this paper, we propose a novel differentiable method for channel pruning, named Differentiable Markov Channel Pruning (DMCP), to efficiently search the optimal sub-structure.