2020.05.25.txt

==========New Papers==========
1, TITLE:       SpotFast Networks with Memory Augmented Lateral Transformers for Lipreading
http://arxiv.org/abs/2005.10903
AUTHORS:        Peratham Wiriyathammabhum
HIGHLIGHT:      This paper presents a novel deep learning architecture for word-level lipreading.

2, TITLE:       Joint Detection and Tracking in Videos with Identification Features
http://arxiv.org/abs/2005.10905
AUTHORS:        Bharti Munjal ; Abdul Rafey Aftab ; Sikandar Amin ; Meltem D. Brandlmaier ; Federico Tombari ; Fabio Galasso
COMMENTS:       Accepted at Image and Vision Computing Journal
HIGHLIGHT:      Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos.

3, TITLE:       Solving a steady-state PDE using spiking networks and neuromorphic hardware
http://arxiv.org/abs/2005.10904
AUTHORS:        J. Darby Smith ; William Severa ; Aaron J. Hill ; Leah Reeder ; Brian Franke ; Richard B. Lehoucq ; Ojas D. Parekh ; James B. Aimone
COMMENTS:       Submitted to 2020 International Conference on Neuromorphic Systems (2020 ICONS)
HIGHLIGHT:      Here, we leverage the parallel and event-driven structure to solve a steady state heat equation using a random walk method.

4, TITLE:       Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality
http://arxiv.org/abs/2005.10912
AUTHORS:        Prudhvi Thirumalaraju ; Manoj Kumar Kanakasabapathy ; Charles L Bormann ; Raghav Gupta ; Rohan Pooniwala ; Hemanth Kandula ; Irene Souter ; Irene Dimitriadis ; Hadi Shafiee
HIGHLIGHT:      Here, we evaluate multi-layered CNNs developed from scratch and popular deep-learning architectures such as Inception v3, ResNET, Inception-ResNET-v2, and Xception in differentiating between embryos based on their morphological quality at 113 hours post insemination (hpi).

5, TITLE:       Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning
http://arxiv.org/abs/2005.10915
AUTHORS:        Sourya Dipta Das ; Soumil Mandal
COMMENTS:       Proceedings of the International Workshop on Semantic Evaluation (SemEval)
HIGHLIGHT:      In this article, we describe the system that we used for the memotion analysis challenge, which is Task 8 of SemEval-2020.

6, TITLE:       Large scale evaluation of importance maps in automatic speech recognition
http://arxiv.org/abs/2005.10929
AUTHORS:        Viet Anh Trinh ; Michael I Mandel
COMMENTS:       submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper, we propose a metric that we call the structured saliency benchmark (SSBM) to evaluate importance maps computed for automatic speech recognizers on individual utterances.

7, TITLE:       Dynamics-Aware Latent Space Reachability for Exploration in Temporally-Extended Tasks
http://arxiv.org/abs/2005.10934
AUTHORS:        Homanga Bharadhwaj ; Animesh Garg ; Florian Shkurti
COMMENTS:       Preprint. Preliminary report
HIGHLIGHT:      In this paper, we propose an exploration framework, which learns a dynamics-aware manifold of reachable states.

8, TITLE:       When Dictionary Learning Meets Deep Learning: Deep Dictionary Learning and Coding Network for Image Recognition with Limited Data
http://arxiv.org/abs/2005.10940
AUTHORS:        Hao Tang ; Hong Liu ; Wei Xiao ; Nicu Sebe
COMMENTS:       Accepted to TNNLS, an extended version of a paper published in WACV2019. arXiv admin note: substantial text overlap with arXiv:1809.04185
HIGHLIGHT:      We present a new Deep Dictionary Learning and Coding Network (DDLCN) for image recognition tasks with limited data.

9, TITLE:       Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning
http://arxiv.org/abs/2005.10957
AUTHORS:        Yiping Wang ; David Farnell ; Hossein Farahani ; Mitchell Nursey ; Basile Tessier-Cloutier ; Steven J. M. Jones ; David G. Huntsman ; C. Blake Gilks ; Ali Bashashati
HIGHLIGHT:      We utilized a \textit{two}-stage deep transfer learning algorithm based on convolutional neural networks (CNN) and progressive resizing for automatic classification of epithelial ovarian carcinoma WSIs.

10, TITLE:      A Group-Theoretic Framework for Knowledge Graph Embedding
http://arxiv.org/abs/2005.10956
AUTHORS:        Tong Yang ; Long Sha ; Pengyu Hong
COMMENTS:       8 pages, 3 tables
HIGHLIGHT:      Motivated by the theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework, in which relations are embedded as group elements, and entities are represented by vectors in group action spaces.

11, TITLE:      Head2Head: Video-based Neural Head Synthesis
http://arxiv.org/abs/2005.10954
AUTHORS:        Mohammad Rami Koujan ; Michail Christos Doukas ; Anastasios Roussos ; Stefanos Zafeiriou
COMMENTS:       To be published in 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
HIGHLIGHT:      In this paper, we propose a novel machine learning architecture for facial reenactment.

12, TITLE:      A Concise Review of Recent Few-shot Meta-learning Methods
http://arxiv.org/abs/2005.10953
AUTHORS:        Xiaoxu Li ; Zhuo Sun ; Jing-Hao Xue ; Zhanyu Ma
COMMENTS:       7 pages
HIGHLIGHT:      In this short communication, we give a concise review on recent representative methods in few-shot meta-learning, which are categorized into four branches according to their technical characteristics.

13, TITLE:      Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making
http://arxiv.org/abs/2005.10960
AUTHORS:        Harini Suresh ; Natalie Lao ; Ilaria Liccardi
COMMENTS:       10 pages
HIGHLIGHT:      We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals.

14, TITLE:      Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification
http://arxiv.org/abs/2005.10979
AUTHORS:        Prateek Shroff ; Tianlong Chen ; Yunchao Wei ; Zhangyang Wang
HIGHLIGHT:      In this paper, we tried to focus on these marginal differences to extract more representative features.

15, TITLE:      SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
http://arxiv.org/abs/2005.10977
AUTHORS:        Zhi Qiao ; Yu Zhou ; Dongbao Yang ; Yucan Zhou ; Weiping Wang
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this work, we propose a semantics enhanced encoder-decoder framework to robustly recognize low-quality scene texts.

16, TITLE:      Conditionally Deep Hybrid Neural Networks Across Edge and Cloud
http://arxiv.org/abs/2005.10851
AUTHORS:        Yinghan Long ; Indranil Chakraborty ; Kaushik Roy
COMMENTS:       6 pages, 5 figures, 4 tables
HIGHLIGHT:      To address these challenges, we propose a conditionally deep hybrid neural network for enabling AI-based fog computing.

17, TITLE:      Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
http://arxiv.org/abs/2005.10987
AUTHORS:        Youngjoon Yu ; Hong Joo Lee ; Byeong Cheon Kim ; Jung Uk Kim ; Yong Man Ro
HIGHLIGHT:      In this paper, we investigated whether the current multimodal fusion model utilizes the complementary intelligence to defend against adversarial attacks.

18, TITLE:      A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images
http://arxiv.org/abs/2005.10986
AUTHORS:        Jia-Wei Chen ; Rongfang Wang ; Fan Ding ; Bo Liu ; Licheng Jiao ; Jie Zhang
HIGHLIGHT:      In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image.

19, TITLE:      Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time
http://arxiv.org/abs/2005.10865
AUTHORS:        Benjamin E. Nye ; Ani Nenkova ; Iain J. Marshall ; Byron C. Wallace
COMMENTS:       6 pages, 4 figures
HIGHLIGHT:      We introduce Trialstreamer, a living database of clinical trial reports.

20, TITLE:      Deep learning application of vibration data for predictive maintenance of gravity acceleration equipment
http://arxiv.org/abs/2005.10985
AUTHORS:        SeonWoo Lee ; YuHyeon Tak ; HoJun Yang ; JaeHeung Yang ; GangMin Lim ; KyuSung Kim ; ByeongKeun Choi ; JangWoo Kwon
COMMENTS:       15 pages, 10 figures
HIGHLIGHT:      In this paper, we propose a predictive maintenance model that can proactively prevent failures that may occur in a hypergravity accelerator. We attached a 4-channel accelerometer to the bearing housing which is a rotor, and obtained time-amplitude data from measured values by sampling.

21, TITLE:      RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation
http://arxiv.org/abs/2005.10984
AUTHORS:        Donggen Dai ; Wangkit Wong ; Zhuojun Chen
HIGHLIGHT:      We address the challenging problem of RGB image-based head pose estimation.

22, TITLE:      RV-FuseNet: Range View based Fusion of Time-Series LiDAR Data for Joint 3D Object Detection and Motion Forecasting
http://arxiv.org/abs/2005.10863
AUTHORS:        Ankit Laddha ; Shivam Gautam ; Gregory P. Meyer ; Carlos Vallespi-Gonzalez
COMMENTS:       In submission to BMVC 2020
HIGHLIGHT:      We present a novel end-to-end approach that uses raw time-series LiDAR data to jointly solve both detection and prediction.

23, TITLE:      Spoof Face Detection Via Semi-Supervised Adversarial Training
http://arxiv.org/abs/2005.10999
AUTHORS:        Chengwei Chen ; Wang Yuan ; Xuequan Lu ; Lizhuang Ma
COMMENTS:       Submitted
HIGHLIGHT:      In this paper, we propose a semi-supervised adversarial learning framework for spoof face detection, which largely relaxes the supervision condition.

24, TITLE:      Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
http://arxiv.org/abs/2005.10872
AUTHORS:        Michelle A. Lee ; Carlos Florensa ; Jonathan Tremblay ; Nathan Ratliff ; Animesh Garg ; Fabio Ramos ; Dieter Fox
HIGHLIGHT:      In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline, while requiring minimal interactions with the environment.

25, TITLE:      A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans
http://arxiv.org/abs/2005.10992
AUTHORS:        Nhan T. Nguyen ; Dat Q. Tran ; Nghia T. Nguyen ; Ha Q. Nguyen
HIGHLIGHT:      We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans.

26, TITLE:      Unsupervised Domain Adaptation in Semantic Segmentation: a Review
http://arxiv.org/abs/2005.10876
AUTHORS:        Marco Toldo ; Andrea Maracani ; Umberto Michieli ; Pietro Zanuttigh
COMMENTS:       34 pages, 7 figures, 2 tables
HIGHLIGHT:      The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.

27, TITLE:      Algebraic Hardness versus Randomness in Low Characteristic
http://arxiv.org/abs/2005.10885
AUTHORS:        Robert Andrews
COMMENTS:       32 pages
HIGHLIGHT:      We show that lower bounds for explicit constant-variate polynomials over fields of characteristic $p > 0$ are sufficient to derandomize polynomial identity testing over fields of characteristic $p$.

28, TITLE:      Evaluating Neural Morphological Taggers for Sanskrit
http://arxiv.org/abs/2005.10893
AUTHORS:        Ashim Gupta ; Amrith Krishna ; Pawan Goyal ; Oliver Hellwig
COMMENTS:       Accepted to SIGMORPHON Workshop at ACL 2020
HIGHLIGHT:      Neural sequence labelling approaches have achieved state of the art results in morphological tagging.

29, TITLE:      Extracting Daily Dosage from Medication Instructions in EHRs: An Automated Approach and Lessons Learned
http://arxiv.org/abs/2005.10899
AUTHORS:        Diwakar Mahajan ; Jennifer J. Liang ; Ching-Huei Tsou
COMMENTS:       10 pages, 4 figures, 9 tables
HIGHLIGHT:      Here, we present an automated approach to calculate daily dosage for all medications in EHR structured data.

30, TITLE:      Algebraic Global Gadgetry for Surjective Constraint Satisfaction
http://arxiv.org/abs/2005.11307
AUTHORS:        Hubie Chen
HIGHLIGHT:      We present an algebraic framework for proving hardness results on surjective CSPs; essentially, this framework computes global gadgetry that permits one to present a reduction from a classical CSP to a surjective CSP.

31, TITLE:      Comparative Study of Machine Learning Models and BERT on SQuAD
http://arxiv.org/abs/2005.11313
AUTHORS:        Devshree Patel ; Param Raval ; Ratnam Parikh ; Yesha Shastri
HIGHLIGHT:      This study aims to provide a comparative analysis of performance of certain models popular in machine learning and the BERT model on the Stanford Question Answering Dataset (SQuAD).

32, TITLE:      Digital Neural Networks in the Brain: From Mechanisms for Extracting Structure in the World To Self-Structuring the Brain Itself
http://arxiv.org/abs/2005.11203
AUTHORS:        Alexandre Pitti ; Mathias Quoy ; Catherine Lavandier ; Sofiane Boucenna
HIGHLIGHT:      We propose that the neural mechanism used by the prefrontal cortex (PFC) to detect structure in temporal sequences, based on the temporal order of incoming information, has served as second purpose to the spatial ordering and indexing of brain networks.

33, TITLE:      KL-Divergence-Based Region Proposal Network for Object Detection
http://arxiv.org/abs/2005.11220
AUTHORS:        Geonseok Seo ; Jaeyoung Yoo ; Jaeseok Choi ; Nojun Kwak
COMMENTS:       5 pages, 3 figures, Accepted to ICIP 2020
HIGHLIGHT:      In this paper, we propose a new region proposal learning method that considers the bounding box offset's uncertainty in the objectness score.

34, TITLE:      A Generative Approach to Titling and Clustering Wikipedia Sections
http://arxiv.org/abs/2005.11216
AUTHORS:        Anjalie Field ; Sascha Rothe ; Simon Baumgartner ; Cong Yu ; Abe Ittycheriah
COMMENTS:       Accepted to WNGT Workshop at ACL 2020
HIGHLIGHT:      We evaluate the performance of transformer encoders with various decoders for information organization through a new task: generation of section headings for Wikipedia articles.

35, TITLE:      Semi-supervised Medical Image Classification with Global Latent Mixing
http://arxiv.org/abs/2005.11217
AUTHORS:        Prashnna Kumar Gyawali ; Sandesh Ghimire ; Pradeep Bajracharya ; Zhiyuan Li ; Linwei Wang
HIGHLIGHT:      In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL.

36, TITLE:      On the suitability of generalized regression neural networks for GNSS position time series prediction for geodetic applications in geodesy and geophysics
http://arxiv.org/abs/2005.11106
AUTHORS:        M. Kiani
HIGHLIGHT:      In this paper, the generalized regression neural network is used to predict the GNSS position time series.

37, TITLE:      A Comparative Evaluation of Heart Rate Estimation Methods using Face Videos
http://arxiv.org/abs/2005.11101
AUTHORS:        Javier Hernandez-Ortega ; Julian Fierrez ; Aythami Morales ; David Diaz
COMMENTS:       Accepted in "IEEE International Workshop on Medical Computing (MediComp) 2020"
HIGHLIGHT:      This paper presents a comparative evaluation of methods for remote heart rate estimation using face videos, i.e., given a video sequence of the face as input, methods to process it to obtain a robust estimation of the subjects heart rate at each moment.

38, TITLE:      L2R2: Leveraging Ranking for Abductive Reasoning
http://arxiv.org/abs/2005.11223
AUTHORS:        Yunchang Zhu ; Liang Pang ; Yanyan Lan ; Xueqi Cheng
COMMENTS:       SIGIR 2020
HIGHLIGHT:      With this new perspective, a novel $L2R^2$ approach is proposed under the learning-to-rank framework.

39, TITLE:      Optimal Lower Bounds for Matching and Vertex Cover in Dynamic Graph Streams
http://arxiv.org/abs/2005.11116
AUTHORS:        Jacques Dark ; Christian Konrad
COMMENTS:       to appear in CCC 2020
HIGHLIGHT:      In this paper, we give simple optimal lower bounds on the one-way two-party communication complexity of approximate Maximum Matching and Minimum Vertex Cover with deletions.

40, TITLE:      Character-level Transformer-based Neural Machine Translation
http://arxiv.org/abs/2005.11239
AUTHORS:        Nikolay Banar ; Walter Daelemans ; Mike Kestemont
HIGHLIGHT:      In this paper, we discuss a novel, Transformer-based approach, that we compare, both in speed and in quality to the Transformer at subword and character levels, as well as previously developed character-level models.

41, TITLE:      A machine learning based software pipeline to pick the variable ordering for algorithms with polynomial inputs
http://arxiv.org/abs/2005.11251
AUTHORS:        Dorian Florescu ; Matthew England
COMMENTS:       Accepted into Proc ICMS 2020
HIGHLIGHT:      Hence in this paper we present a software pipeline to use sklearn to pick the variable ordering for an algorithm that acts on a polynomial system.

42, TITLE:      Investigating Label Bias in Beam Search for Open-ended Text Generation
http://arxiv.org/abs/2005.11009
AUTHORS:        Liang Wang ; Jinlong Liu ; Jingming Liu
COMMENTS:       10 pages, 4 figures, 5 tables
HIGHLIGHT:      This paper provides a series of empirical evidence that label bias is a major reason for such degenerate behaviors of beam search.

43, TITLE:      SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation
http://arxiv.org/abs/2005.11003
AUTHORS:        Jieli Zhou ; Baoyu Jing ; Zeya Wang
HIGHLIGHT:      In addressing this formulated problem, we propose a novel Semi-supervised Open set Domain Adversarial network (SODA), which is able to align the data distributions across different domains in a general domain space and also in a common subspace of source and target data.

44, TITLE:      Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting
http://arxiv.org/abs/2005.11246
AUTHORS:        Quentin Paletta ; Joan Lasenby
COMMENTS:       4 pages, 7 figures, 1 table, accepted for European PV Solar Energy Conference and Exhibition (EU-PVSEC) 2020
HIGHLIGHT:      This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables.

45, TITLE:      NAUTILUS: a Versatile Voice Cloning System
http://arxiv.org/abs/2005.11004
AUTHORS:        Hieu-Thi Luong ; Junichi Yamagishi
COMMENTS:       Submitted to The IEEE/ACM Transactions on Audio, Speech, and Language Processing
HIGHLIGHT:      We introduce a novel speech synthesis system, called NAUTILUS, that can generate speech with a target voice either from a text input or a reference utterance of an arbitrary source speaker.

46, TITLE:      Evaluating Generalisation in General Video Game Playing
http://arxiv.org/abs/2005.11247
AUTHORS:        Martin Balla ; Simon M. Lucas ; Diego Perez-Liebana
COMMENTS:       accepted for publication in IEEE Conference on Games (CoG) 2020
HIGHLIGHT:      This paper focuses on the challenge of the GVGAI learning track in which 3 games are selected and 2 levels are given for training, while 3 hidden levels are left for evaluation.

47, TITLE:      Classification and Clustering of arXiv Documents, Sections, and Abstracts, Comparing Encodings of Natural and Mathematical Language
http://arxiv.org/abs/2005.11021
AUTHORS:        Philipp Scharpf ; Moritz Schubotz ; Abdou Youssef ; Felix Hamborg ; Norman Meuschke ; Bela Gipp
HIGHLIGHT:      In this paper, we show how selecting and combining encodings of natural and mathematical language affect classification and clustering of documents with mathematical content.

48, TITLE:      Symbolic Reasoning about Quantum Circuits in Coq
http://arxiv.org/abs/2005.11023
AUTHORS:        Wenjun Shi ; Qinxiang Cao ; Yuxin Deng ; Hanru Jiang ; Yuan Feng
HIGHLIGHT:      In this paper, we propose a symbolic approach to reasoning about quantum circuits.

49, TITLE:      Living Machines: A study of atypical animacy
http://arxiv.org/abs/2005.11140
AUTHORS:        Mariona Coll Ardanuy ; Federico Nanni ; Kaspar Beelen ; Kasra Hosseini ; Ruth Ahnert ; Jon Lawrence ; Katherine McDonough ; Giorgia Tolfo ; Daniel CS Wilson ; Barbara McGillivray
COMMENTS:       13 pages, 2 figures
HIGHLIGHT:      This paper proposes a new approach to animacy detection, the task of determining whether an entity is represented as animate in a text. To address it, we have created the first dataset for atypical animacy detection, based on nineteenth-century sentences in English, with machines represented as either animate or inanimate.

50, TITLE:      Robust Layout-aware IE for Visually Rich Documents with Pre-trained Language Models
http://arxiv.org/abs/2005.11017
AUTHORS:        Mengxi Wei ; Yifan He ; Qiong Zhang
COMMENTS:       10 pages, to appear in SIGIR 2020 Industry Track
HIGHLIGHT:      We study the problem of information extraction from visually rich documents (VRDs) and present a model that combines the power of large pre-trained language models and graph neural networks to efficiently encode both textual and visual information in business documents.

51, TITLE:      microPhantom: Playing microRTS under uncertainty and chaos
http://arxiv.org/abs/2005.11019
AUTHORS:        Florian Richoux
HIGHLIGHT:      In this paper, we focus on decision-making under uncertainty, by tackling the Unit Production Problem with a method based on a combination of Constraint Programming and decision theory.

52, TITLE:      Intent Mining from past conversations for Conversational Agent
http://arxiv.org/abs/2005.11014
AUTHORS:        Ajay Chatterjee ; Shubhashis Sengupta
COMMENTS:       8 pages, 2 figures
HIGHLIGHT:      In this paper, we present an intent discovery framework that involves 4 primary steps: Extraction of textual utterances from a conversation using a pre-trained domain agnostic Dialog Act Classifier (Data Extraction), automatic clustering of similar user utterances (Clustering), manual annotation of clusters with an intent label (Labeling) and propagation of intent labels to the utterances from the previous step, which are not mapped to any cluster (Label Propagation); to generate intent training data from raw conversations.

53, TITLE:      Reinforcement learning with human advice. A survey
http://arxiv.org/abs/2005.11016
AUTHORS:        Anis Najar ; Mohamed Chetouani
HIGHLIGHT:      In this paper, we provide an overview of the existing methods for integrating human advice into a Reinforcement Learning process.

54, TITLE:      Feature selection for gesture recognition in Internet-of-Things for healthcare
http://arxiv.org/abs/2005.11031
AUTHORS:        Giulia Cisotto ; Martina Capuzzo ; Anna V. Guglielmi ; Andrea Zanella
HIGHLIGHT:      This paper proposes a new algorithm that aims (i) to robustly extract the most relevant features to classify different grasping tasks, and (ii) to retain the natural meaning of the selected features.

55, TITLE:      Bi-direction Context Propagation Network for Real-time Semantic Segmentation
http://arxiv.org/abs/2005.11034
AUTHORS:        Shijie Hao ; Yuan Zhou ; Yanrong Guo
COMMENTS:       9 pages, 6 figures
HIGHLIGHT:      To address this problem, we propose a new Bi-direction Contexts Propagation Network (BCPNet), which performs semantic segmentation in real-time.

56, TITLE:      The Average-Case Time Complexity of Certifying the Restricted Isometry Property
http://arxiv.org/abs/2005.11270
AUTHORS:        Yunzi Ding ; Dmitriy Kunisky ; Alexander S. Wein ; Afonso S. Bandeira
COMMENTS:       12 pages
HIGHLIGHT:      In this paper, we investigate the exact average-case time complexity of certifying the RIP property for $M\times N$ matrices with i.i.d. $\mathcal{N}(0,1/M)$ entries, in the "possible but hard" regime $\sqrt{M} \ll s\lesssim M/\log N$, assuming that $M$ scales proportional to $N$.

57, TITLE:      Givenness Hierarchy Theoretic Cognitive Status Filtering
http://arxiv.org/abs/2005.11267
AUTHORS:        Poulomi Pal ; Lixiao Zhu ; Andrea Golden-Lasher ; Akshay Swaminathan ; Tom Williams
COMMENTS:       To be published in the proceedings of the 2020 Annual Meeting of the Cognitive Science Society (COGSCI). Supplemental materials available at https://osf.io/qse7y/
HIGHLIGHT:      We present and compare two such models of cognitive status: a rule-based Finite State Machine model directly informed by the GH literature and a Cognitive Status Filter designed to more flexibly handle uncertainty.

58, TITLE:      Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification
http://arxiv.org/abs/2005.11043
AUTHORS:        Hongyu Li ; Xiaogang Huang ; Zhihui Fu ; Xiaolin Li
HIGHLIGHT:      Since the resizing operation during deep learning will damage the microstructures of image noise residuals, we propose a framework for directly training images of original input scales without resizing.

59, TITLE:      Polarimetric image augmentation
http://arxiv.org/abs/2005.11044
AUTHORS:        Marc Blanchon ; Olivier Morel ; Fabrice Meriaudeau ; Ralph Seulin ; Désiré Sidibé
COMMENTS:       6 pages, 4 figures, conference
HIGHLIGHT:      We propose to enhance deep learning models through a regularized augmentation procedure applied to polarimetric data in order to characterize scenes more effectively under challenging conditions.

60, TITLE:      PruneNet: Channel Pruning via Global Importance
http://arxiv.org/abs/2005.11282
AUTHORS:        Ashish Khetan ; Zohar Karnin
COMMENTS:       12 pages, 3 figures, Published in ICLR 2020 NAS Workshop
HIGHLIGHT:      In this work, we investigate a simple-yet-effective method for pruning channels based on a computationally light-weight yet effective data driven optimization step that discovers the necessary width per layer.

61, TITLE:      Position-based Scaled Gradient for Model Quantization and Sparse Training
http://arxiv.org/abs/2005.11035
AUTHORS:        Jangho Kim ; KiYoon Yoo ; Nojun Kwak
HIGHLIGHT:      We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly.

62, TITLE:      Style Normalization and Restitution for Generalizable Person Re-identification
http://arxiv.org/abs/2005.11037
AUTHORS:        Xin Jin ; Cuiling Lan ; Wenjun Zeng ; Zhibo Chen ; Li Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains.

63, TITLE:      From ImageNet to Image Classification: Contextualizing Progress on Benchmarks
http://arxiv.org/abs/2005.11295
AUTHORS:        Dimitris Tsipras ; Shibani Santurkar ; Logan Engstrom ; Andrew Ilyas ; Aleksander Madry
HIGHLIGHT:      In this work, we use human studies to investigate the consequences of employing such a pipeline, focusing on the popular ImageNet dataset.

64, TITLE:      RUSSE'2020: Findings of the First Taxonomy Enrichment Task for the Russian language
http://arxiv.org/abs/2005.11176
AUTHORS:        Irina Nikishina ; Varvara Logacheva ; Alexander Panchenko ; Natalia Loukachevitch
HIGHLIGHT:      This paper describes the results of the first shared task on taxonomy enrichment for the Russian language. Instead, we provided a textual corpus where these new terms occurred. For this evaluation campaign, we developed a new evaluation dataset based on unpublished RuWordNet data.

65, TITLE:      Improving Segmentation for Technical Support Problems
http://arxiv.org/abs/2005.11055
AUTHORS:        Kushal Chauhan ; Abhirut Gupta
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this paper, we address the problem of segmentation for technical support questions.

66, TITLE:      GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
http://arxiv.org/abs/2005.11177
AUTHORS:        Umair Qazi ; Muhammad Imran ; Ferda Ofli
COMMENTS:       10 pages, 5 figures, accepted at ACM SIGSPATIAL Special May 2020
HIGHLIGHT:      In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020.

67, TITLE:      Low-Latency Sequence-to-Sequence Speech Recognition and Translation by Partial Hypothesis Selection
http://arxiv.org/abs/2005.11185
AUTHORS:        Danni Liu ; Gerasimos Spanakis ; Jan Niehues
HIGHLIGHT:      We propose three latency reduction techniques for chunk-based incremental inference and evaluate their efficiency in terms of accuracy-latency trade-off.

68, TITLE:      T-RECS: a Transformer-based Recommender Generating Textual Explanations and Integrating Unsupervised Language-based Critiquing
http://arxiv.org/abs/2005.11067
AUTHORS:        Diego Antognini ; Claudiu Musat ; Boi Faltings
COMMENTS:       Under review. 24 pages, 8 figures, 13 tables
HIGHLIGHT:      We propose T-RECS, a multi-task learning Transformer-based model that jointly performs recommendation with textual explanations using a novel multi-aspect masking technique.

69, TITLE:      Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
http://arxiv.org/abs/2005.11061
AUTHORS:        Hokuto Hirano ; Kazuki Koga ; Kazuhiro Takemoto
COMMENTS:       17 pages, 5 figures, 3 tables
HIGHLIGHT:      Thus, we focus on representative DNN models for detecting COVID-19 cases from chest X-ray images and evaluate their vulnerability to UAPs generated using simple iterative algorithms.

70, TITLE:      End-to-end Named Entity Recognition from English Speech
http://arxiv.org/abs/2005.11184
AUTHORS:        Hemant Yadav ; Sreyan Ghosh ; Yi Yu ; Rajiv Ratn Shah
COMMENTS:       submitted to Interspeech-2020
HIGHLIGHT:      In this paper, we introduce a first publicly available NER annotated dataset for English speech and present an E2E approach, which jointly optimizes the ASR and NER tagger components.

71, TITLE:      Bootstrapping Named Entity Recognition in E-Commerce with Positive Unlabeled Learning
http://arxiv.org/abs/2005.11075
AUTHORS:        Hanchu Zhang ; Leonhard Hennig ; Christoph Alt ; Changjian Hu ; Yao Meng ; Chao Wang
COMMENTS:       Accepted at ECNLP 3 (ACL 2020)
HIGHLIGHT:      To address this problem, we present a bootstrapped positive-unlabeled learning algorithm that integrates domain-specific linguistic features to quickly and efficiently expand the seed dictionary.

72, TITLE:      Simplify-then-Translate: Automatic Preprocessing for Black-Box Machine Translation
http://arxiv.org/abs/2005.11197
AUTHORS:        Sneha Mehta ; Bahareh Azarnoush ; Boris Chen ; Avneesh Saluja ; Vinith Misra ; Ballav Bihani ; Ritwik Kumar
HIGHLIGHT:      In this work, we introduce a method to improve such systems via automatic pre-processing (APP) using sentence simplification.

73, TITLE:      Driver Identification through Stochastic Multi-State Car-Following Modeling
http://arxiv.org/abs/2005.11077
AUTHORS:        Donghao Xu ; Zhezhang Ding ; Chenfeng Tu ; Huijing Zhao ; Mathieu Moze ; François Aioun ; Franck Guillemard
COMMENTS:       13 pages, 4 figures. Submitted to T.ITS
HIGHLIGHT:      In this study, a joint model of the two types of heterogeneity in car-following behavior is proposed as an approach of driver profiling and identification.

74, TITLE:      Deep covariate-learning: optimising information extraction from terrain texture for geostatistical modelling applications
http://arxiv.org/abs/2005.11194
AUTHORS:        Charlie Kirkwood
COMMENTS:       13 pages, 8 figures, to be submitted to journal
HIGHLIGHT:      In this paper we present a solution to this problem in the form of a deep learning approach to automatically deriving optimal task-specific terrain texture covariates from a standard SRTM 90m gridded digital elevation model (DEM).

75, TITLE:      An Introduction to Neural Architecture Search for Convolutional Networks
http://arxiv.org/abs/2005.11074
AUTHORS:        George Kyriakides ; Konstantinos Margaritis
COMMENTS:       17 pages, 4 figures
HIGHLIGHT:      In this work, we provide an introduction to the basic concepts of NAS for convolutional networks, along with the major advances in search spaces, algorithms and evaluation techniques.

76, TITLE:      Point2Mesh: A Self-Prior for Deformable Meshes
http://arxiv.org/abs/2005.11084
AUTHORS:        Rana Hanocka ; Gal Metzer ; Raja Giryes ; Daniel Cohen-Or
COMMENTS:       SIGGRAPH 2020; Project page: https://ranahanocka.github.io/point2mesh/
HIGHLIGHT:      In this paper, we introduce Point2Mesh, a technique for reconstructing a surface mesh from an input point cloud.

77, TITLE:      Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
http://arxiv.org/abs/2005.11081
AUTHORS:        Natalia Vesselinova ; Rebecca Steinert ; Daniel F. Perez-Ramirez ; Magnus Boman
COMMENTS:       27 pages, 0 figures, submitted to IEEE Access journal
HIGHLIGHT:      Relevant developments in machine learning research on graphs is surveyed, for this purpose.

78, TITLE:      Deep Learning Based Detection and Localization of Cerebal Aneurysms in Computed Tomography Angiography
http://arxiv.org/abs/2005.11098
AUTHORS:        Ziheng Duan ; Daniel Montes ; Yangsibo Huang ; Dufan Wu ; Javier M. Romero ; Ramon Gilberto Gonzalez ; Quanzheng Li
HIGHLIGHT:      In this work, we proposed DeepBrain, a deep learning based cerebral aneurysm detection and localization algorithm.

79, TITLE:      DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-temporal Models
http://arxiv.org/abs/2005.11093
AUTHORS:        Yania Molina Souto ; Rafael Pereira ; Rocío Zorrilla ; Anderson Chaves ; Brian Tsan ; Florin Rusu ; Eduardo Ogasawara ; Artur Ziviani ; Fabio Porto
HIGHLIGHT:      In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queries.

80, TITLE:      Misregistration Measurement and Improvement for Sentinel-1 SAR and Sentinel-2 Optical images
http://arxiv.org/abs/2005.11092
AUTHORS:        Yuanxin Ye ; Chao Yang ; Bai Zhu ; Youquan He
HIGHLIGHT:      To eliminate the misregistration, we use some representative geometric transformation models such as polynomial models, projective models, and rational function models for the co-registration of the two types of images, and compare and analyze their registration accuracy under different number of control points and different terrains.



==========Updates to Previous Papers==========
1, TITLE:       Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges
http://arxiv.org/abs/2004.01536
AUTHORS:        Ylva Jansson ; Tony Lindeberg
COMMENTS:       11 pages, 5 figures
HIGHLIGHT:      We, therefore, present a theoretical analysis of invariance and covariance properties of scale channel networks and perform an experimental evaluation of the ability of different types of scale channel networks to generalise to previously unseen scales.

2, TITLE:       Rapid Probabilistic Interest Learning from Domain-Specific Pairwise Image Comparisons
http://arxiv.org/abs/1706.05850
AUTHORS:        Michael Burke ; Siyabonga Mbonambi ; Purity Molala ; Raesetje Sefala
HIGHLIGHT:      This work uses pairwise image comparisons to reduce the labelling burden on these users, and introduces an image interest estimation approach that performs similarly to recent data hungry deep learning approaches trained using pairwise ranking losses.

3, TITLE:       Machine learning on Big Data from Twitter to understand public reactions to COVID-19
http://arxiv.org/abs/2005.08817
AUTHORS:        Jia Xue ; Junxiang Chen ; Chen Chen ; ChengDa Zheng ; Tingshao Zhu
HIGHLIGHT:      The study aims to understand Twitter users' discussions and reactions about the COVID-19.

4, TITLE:       XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
http://arxiv.org/abs/2004.01401
AUTHORS:        Yaobo Liang ; Nan Duan ; Yeyun Gong ; Ning Wu ; Fenfei Guo ; Weizhen Qi ; Ming Gong ; Linjun Shou ; Daxin Jiang ; Guihong Cao ; Xiaodong Fan ; Ruofei Zhang ; Rahul Agrawal ; Edward Cui ; Sining Wei ; Taroon Bharti ; Ying Qiao ; Jiun-Hung Chen ; Winnie Wu ; Shuguang Liu ; Fan Yang ; Daniel Campos ; Rangan Majumder ; Ming Zhou
HIGHLIGHT:      In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks.

5, TITLE:       Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection
http://arxiv.org/abs/2005.08706
AUTHORS:        Lu Zhang ; Jian Zhang ; Zhibin Li ; Jingsong Xu
COMMENTS:       We want to withdraw this paper. There are some insufficient explanations and bad figure presentation
HIGHLIGHT:      To this end, this paper proposes Two-Branch Collaborative Graph Neural Networks to collaboratively represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end fashion.

6, TITLE:       A Text Reassembling Approach to Natural Language Generation
http://arxiv.org/abs/2005.07988
AUTHORS:        Xiao Li ; Kees van Deemter ; Chenghua Lin
HIGHLIGHT:      Focussing on some of the key NLG tasks (namely Content Selection, Lexical Choice, and Linguistic Realisation), we propose a novel approach, called the Text Reassembling approach to NLG (TRG), which approaches the ideal of a purely statistical approach very closely, and which is at the same time highly transparent.

7, TITLE:       Fast Complete Algorithm for Multiplayer Nash Equilibrium
http://arxiv.org/abs/2002.04734
AUTHORS:        Sam Ganzfried
HIGHLIGHT:      We describe a new complete algorithm for computing Nash equilibrium in multiplayer general-sum games, based on a quadratically-constrained feasibility program formulation.

8, TITLE:       Robust Speaker Recognition Using Speech Enhancement And Attention Model
http://arxiv.org/abs/2001.05031
AUTHORS:        Yanpei Shi ; Qiang Huang ; Thomas Hain
COMMENTS:       Acceptted by Odyssey 2020
HIGHLIGHT:      In this paper, a novel architecture for speaker recognition is proposed by cascading speech enhancement and speaker processing.

9, TITLE:       MineReduce: an approach based on data mining for problem size reduction
http://arxiv.org/abs/2005.07415
AUTHORS:        Marcelo Rodrigues de Holanda Maia ; Alexandre Plastino ; Puca Huachi Vaz Penna
HIGHLIGHT:      In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction.

10, TITLE:      CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
http://arxiv.org/abs/2004.13795
AUTHORS:        Daniele Cattaneo ; Domenico Giorgio Sorrenti ; Abhinav Valada
COMMENTS:       Spotlight talk at IEEE ICRA 2020 Workshop on Emerging Learning and Algorithmic Methods for Data Association in Robotics
HIGHLIGHT:      In this paper, we now take it a step further by introducing CMRNet++, which is a significantly more robust model that not only generalizes to new places effectively, but is also independent of the camera parameters.

11, TITLE:      Speech-XLNet: Unsupervised Acoustic Model Pretraining For Self-Attention Networks
http://arxiv.org/abs/1910.10387
AUTHORS:        Xingchen Song ; Guangsen Wang ; Zhiyong Wu ; Yiheng Huang ; Dan Su ; Dong Yu ; Helen Meng
COMMENTS:       \c{opyright} 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      In this paper, we present an XLNet-like pretraining scheme "Speech-XLNet" for unsupervised acoustic model pretraining to learn speech representations with SAN.

12, TITLE:      Tangent Images for Mitigating Spherical Distortion
http://arxiv.org/abs/1912.09390
AUTHORS:        Marc Eder ; Mykhailo Shvets ; John Lim ; Jan-Michael Frahm
COMMENTS:       Updated version of CVPR 2020 publication (9 pages, 13 pages supplementary). Code: https://github.com/meder411/Tangent-Images
HIGHLIGHT:      In this work, we propose "tangent images," a spherical image representation that facilitates transferable and scalable $360^\circ$ computer vision.

13, TITLE:      Exploring Crowd Co-creation Scenarios for Sketches
http://arxiv.org/abs/2005.07328
AUTHORS:        Devi Parikh ; C. Lawrence Zitnick
HIGHLIGHT:      As a first step towards studying the ability of human crowds and machines to effectively co-create, we explore several human-only collaborative co-creation scenarios.

14, TITLE:      Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
http://arxiv.org/abs/2004.09666
AUTHORS:        Ming Y. Lu ; Drew F. K. Williamson ; Tiffany Y. Chen ; Richard J. Chen ; Matteo Barbieri ; Faisal Mahmood
HIGHLIGHT:      Here we present CLAM - Clustering-constrained attention multiple instance learning, an easy-to-use, high-throughput, and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems.

15, TITLE:      Astroalign: A Python module for astronomical image registration
http://arxiv.org/abs/1909.02946
AUTHORS:        Martin Beroiz ; Juan B. Cabral ; Bruno Sanchez
COMMENTS:       4 pages, 2 figures, Python package
HIGHLIGHT:      We present an algorithm implemented in the astroalign Python module for image registration in astronomy.

16, TITLE:      Perturbations on the Perceptual Ball
http://arxiv.org/abs/1912.09405
AUTHORS:        Andrew Elliott ; Stephen Law ; Chris Russell
COMMENTS:       First two authors contributed equally to this work. Preprint under review
HIGHLIGHT:      We present a simple regularisation of Adversarial Perturbations based upon the perceptual loss.

17, TITLE:      Efficient and Phase-aware Video Super-resolution for Cardiac MRI
http://arxiv.org/abs/2005.10626
AUTHORS:        Jhih-Yuan Lin ; Yu-Cheng Chang ; Winston H. Hsu
COMMENTS:       MICCAI 2020
HIGHLIGHT:      To this end, we propose a novel end-to-end trainable network to solve CMR video super-resolution problem without the hardware upgrade and the scanning protocol modifications.

18, TITLE:      Medical Image Segmentation via Unsupervised Convolutional Neural Network
http://arxiv.org/abs/2001.10155
AUTHORS:        Junyu Chen ; Eric C. Frey
HIGHLIGHT:      In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised.

19, TITLE:      Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
http://arxiv.org/abs/2005.10622
AUTHORS:        Cong Fei ; Bin Wang ; Yuzheng Zhuang ; Zongzhang Zhang ; Jianye Hao ; Hongbo Zhang ; Xuewu Ji ; Wulong Liu
COMMENTS:       7 papges, 3 figures
HIGHLIGHT:      In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary skill selector.

20, TITLE:      Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
http://arxiv.org/abs/2003.07018
AUTHORS:        Yong Guo ; Jian Chen ; Jingdong Wang ; Qi Chen ; Jiezhang Cao ; Zeshuai Deng ; Yanwu Xu ; Mingkui Tan
COMMENTS:       This paper is accepted by CVPR 2020
HIGHLIGHT:      To address the above issues, we propose a dual regression scheme by introducing an additional constraint on LR data to reduce the space of the possible functions.

21, TITLE:      Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
http://arxiv.org/abs/2005.05050
AUTHORS:        Jian Zhan ; Joao Cartucho ; Stamatia Giannarou
COMMENTS:       7 pages, 5 figures, ICRA 2020
HIGHLIGHT:      To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation.

22, TITLE:      Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
http://arxiv.org/abs/2005.10754
AUTHORS:        Hong Joo Lee ; Seong Tae Kim ; Hakmin Lee ; Nassir Navab ; Yong Man Ro
HIGHLIGHT:      To address this issue, a generic and efficient segmentation framework to construct ensemble segmentation models is devised in this paper.

23, TITLE:      Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
http://arxiv.org/abs/2005.10550
AUTHORS:        Renato Hermoza ; Gabriel Maicas ; Jacinto C. Nascimento ; Gustavo Carneiro
COMMENTS:       Early accept at MICCAI 2020
HIGHLIGHT:      In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation.

24, TITLE:      SafeComp: Protocol For Certifying Cloud Computations Integrity
http://arxiv.org/abs/2005.10786
AUTHORS:        Evgeny Shishkin ; Evgeny Kislitsyn
HIGHLIGHT:      We present a multi-party interactive protocol called SafeComp that solves this problem under specified constraints.

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

26, TITLE:      Text-to-Text Pre-Training for Data-to-Text Tasks
http://arxiv.org/abs/2005.10433
AUTHORS:        Mihir Kale
HIGHLIGHT:      We study the pre-train + fine-tune strategy for data-to-text tasks.

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

28, TITLE:      Repairing and Mechanising the JavaScript Relaxed Memory Model
http://arxiv.org/abs/2005.10554
AUTHORS:        Conrad Watt ; Christopher Pulte ; Anton Podkopaev ; Guillaume Barbier ; Stephen Dolan ; Shaked Flur ; Jean Pichon-Pharabod ; Shu-yu Guo
COMMENTS:       16 pages, 13 figiures
HIGHLIGHT:      We propose a correction, which also incorporates a previously proposed fix for a failure of the model to provide Sequential Consistency of Data-Race-Free programs (SC-DRF), an important correctness condition.

29, TITLE:      BARNet: Bilinear Attention Network with Adaptive Receptive Fields 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
HIGHLIGHT:      In this paper, we propose a novel bilinear attention network with adaptive receptive field to solve these two challenges.

30, TITLE:      A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
http://arxiv.org/abs/2005.10326
AUTHORS:        Apostolia Tsirikoglou ; Karin Stacke ; Gabriel Eilertsen ; Martin Lindvall ; Jonas Unger
COMMENTS:       Presented at the ICLR 2020 Workshop on AI for Overcoming Global Disparities in Cancer Care (AI4CC)
HIGHLIGHT:      In this study, we investigate mitigation strategies for limited data access scenarios.

31, TITLE:      Explainable Machine Learning in Deployment
http://arxiv.org/abs/1909.06342
AUTHORS:        Umang Bhatt ; Alice Xiang ; Shubham Sharma ; Adrian Weller ; Ankur Taly ; Yunhan Jia ; Joydeep Ghosh ; Ruchir Puri ; José M. F. Moura ; Peter Eckersley
COMMENTS:       ACM Conference on Fairness, Accountability, and Transparency (FAT*) 2020
HIGHLIGHT:      To facilitate end user interaction, we develop a framework for establishing clear goals for explainability.

32, TITLE:      Learning to Understand Child-directed and Adult-directed Speech
http://arxiv.org/abs/2005.02721
AUTHORS:        Lieke Gelderloos ; Grzegorz Chrupała ; Afra Alishahi
COMMENTS:       ACL 2020. Corrected plot legends fig. 1 and 2
HIGHLIGHT:      This study explores the effect of child-directed speech when learning to extract semantic information from speech directly.

33, TITLE:      FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
http://arxiv.org/abs/1910.04076
AUTHORS:        Varun Ravi Kumar ; Sandesh Athni Hiremath ; Stefan Milz ; Christian Witt ; Clement Pinnard ; Senthil Yogamani ; Patrick Mader
COMMENTS:       Camera ready version accepted for ICRA 2020. Supplementary material is removed from previous arXiv version and it will be part of an expanded journal version
HIGHLIGHT:      In this paper, we explore Euclidean distance estimation on fisheye cameras for automotive scenes. To encourage further research in this area, we will release our dataset as part of the WoodScape project \cite{yogamani2019woodscape}.

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

35, TITLE:      On the effectiveness of GAN generated cardiac MRIs for segmentation
http://arxiv.org/abs/2005.09026
AUTHORS:        Youssef Skandarani ; Nathan Painchaud ; Pierre-Marc Jodoin ; Alain Lalande
COMMENTS:       4 pages, Accepted for MIDL 2020
HIGHLIGHT:      In this work, we propose a Variational Autoencoder (VAE) - Generative Adversarial Networks (GAN) model that can produce highly realistic MRI together with its pixel accurate groundtruth for the application of cine-MR image cardiac segmentation.

36, 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 propose a new task of conversational recommendation over 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.

37, TITLE:      Mixed Precision DNNs: All you need is a good parametrization
http://arxiv.org/abs/1905.11452
AUTHORS:        Stefan Uhlich ; Lukas Mauch ; Fabien Cardinaux ; Kazuki Yoshiyama ; Javier Alonso Garcia ; Stephen Tiedemann ; Thomas Kemp ; Akira Nakamura
COMMENTS:       International Conference on Learning Representations (ICLR) 2020; Source code at https://github.com/sony/ai-research-code
HIGHLIGHT:      Specifically, we propose to parametrize the quantizer with the step size and dynamic range.

38, TITLE:      NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
http://arxiv.org/abs/2001.03360
AUTHORS:        Qi Wang ; Junyu Gao ; Wei Lin ; Xuelong Li
COMMENTS:       10 pages, 5 figures
HIGHLIGHT:      To remedy this problem, we construct a large-scale congested crowd counting and localization dataset, NWPU-Crowd, consisting of 5,109 images, in a total of 2,133,375 annotated heads with points and boxes.

39, TITLE:      Computation of the Expected Euler Characteristic for the Largest Eigenvalue of a Real Non-central Wishart Matrix
http://arxiv.org/abs/1903.10099
AUTHORS:        Nobuki Takayama ; Lin Jiu ; Satoshi Kuriki ; Yi Zhang
HIGHLIGHT:      We give an approximate formula for the distribution of the largest eigenvalue of real Wishart matrices by the expected Euler characteristic method for the general dimension.

40, TITLE:      CamemBERT: a Tasty French Language Model
http://arxiv.org/abs/1911.03894
AUTHORS:        Louis Martin ; Benjamin Muller ; Pedro Javier Ortiz Suárez ; Yoann Dupont ; Laurent Romary ; Éric Villemonte de la Clergerie ; Djamé Seddah ; Benoît Sagot
COMMENTS:       ACL 2020 long paper. Web site: https://camembert-model.fr
HIGHLIGHT:      In this paper, we investigate the feasibility of training monolingual Transformer-based language models for other languages, taking French as an example and evaluating our language models on part-of-speech tagging, dependency parsing, named entity recognition and natural language inference tasks.

41, TITLE:      Learning to Dress 3D People in Generative Clothing
http://arxiv.org/abs/1907.13615
AUTHORS:        Qianli Ma ; Jinlong Yang ; Anurag Ranjan ; Sergi Pujades ; Gerard Pons-Moll ; Siyu Tang ; Michael J. Black
COMMENTS:       CVPR-2020 camera ready. Code and data are available at https://cape.is.tue.mpg.de
HIGHLIGHT:      Three-dimensional human body models are widely used in the analysis of human pose and motion.

42, TITLE:      Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
http://arxiv.org/abs/2005.10266
AUTHORS:        Liang-Chieh Chen ; Raphael Gontijo Lopes ; Bowen Cheng ; Maxwell D. Collins ; Ekin D. Cubuk ; Barret Zoph ; Hartwig Adam ; Jonathon Shlens
COMMENTS:       21 pages including reference
HIGHLIGHT:      In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation.

43, TITLE:      Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments
http://arxiv.org/abs/1901.03438
AUTHORS:        Alex Warstadt ; Samuel R. Bowman
HIGHLIGHT:      We introduce a new analysis dataset that also has broad coverage of linguistic phenomena. We annotate the development set of the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018) for the presence of 13 classes of syntactic phenomena including various forms of argument alternations, movement, and modification.

44, TITLE:      Shattered Sets and the Hilbert Function
http://arxiv.org/abs/1511.08245
AUTHORS:        Shay Moran ; Cyrus Rashtchian
COMMENTS:       19 pages, 2 figures. Fixed typo in Theorem 2.3
HIGHLIGHT:      We study complexity measures on subsets of the boolean hypercube and exhibit connections between algebra (the Hilbert function) and combinatorics (VC theory).

45, TITLE:      Template-Based Automatic Search of Compact Semantic Segmentation Architectures
http://arxiv.org/abs/1904.02365
AUTHORS:        Vladimir Nekrasov ; Chunhua Shen ; Ian Reid
COMMENTS:       Updated runtime numbers on CityScapes. WACV 2020
HIGHLIGHT:      In contrast, in this work we propose a novel solution able to find light-weight and accurate segmentation architectures starting from only few blocks of a pre-trained classification network.

46, TITLE:      Channel Pruning via Optimal Thresholding
http://arxiv.org/abs/2003.04566
AUTHORS:        Yun Ye ; Ganmei You ; Jong-Kae Fwu ; Xia Zhu ; Qing Yang ; Yuan Zhu
COMMENTS:       9 pages, 9 figures, 4 tables
HIGHLIGHT:      In this paper, we present a simple yet effective method, termed Optimal Thresholding (OT), to prune channels with layer dependent thresholds that optimally separate important from negligible channels.

47, TITLE:      Exploring Spatial-Temporal Multi-Frequency Analysis for High-Fidelity and Temporal-Consistency Video Prediction
http://arxiv.org/abs/2002.09905
AUTHORS:        Beibei Jin ; Yu Hu ; Qiankun Tang ; Jingyu Niu ; Zhiping Shi ; Yinhe Han ; Xiaowei Li
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we point out the necessity of exploring multi-frequency analysis to deal with the two problems.

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

49, TITLE:      Handling Incomplete Heterogeneous Data using VAEs
http://arxiv.org/abs/1807.03653
AUTHORS:        Alfredo Nazabal ; Pablo M. Olmos ; Zoubin Ghahramani ; Isabel Valera
HIGHLIGHT:      In this paper, we propose a general framework to design VAEs suitable for fitting incomplete heterogenous data.

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

51, TITLE:      Talk to Papers: Bringing Neural Question Answering to Academic Search
http://arxiv.org/abs/2004.02002
AUTHORS:        Tianchang Zhao ; Kyusong Lee
COMMENTS:       demo paper accepted at ACL 2020
HIGHLIGHT:      We introduce Talk to Papers, which exploits the recent open-domain question answering (QA) techniques to improve the current experience of academic search.

52, TITLE:      Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
http://arxiv.org/abs/2004.14133
AUTHORS:        Deng-Ping Fan ; Tao Zhou ; Ge-Peng Ji ; Yi Zhou ; Geng Chen ; Huazhu Fu ; Jianbing Shen ; Ling Shao
COMMENTS:       To appear in IEEE TMI. The code is released in: https://github.com/DengPingFan/Inf-Net
HIGHLIGHT:      Our semi-supervised framework can improve the learning ability and achieve a higher performance.

53, TITLE:      Induced Inflection-Set Keyword Search in Speech
http://arxiv.org/abs/1910.12299
AUTHORS:        Oliver Adams ; Matthew Wiesner ; Jan Trmal ; Garrett Nicolai ; David Yarowsky
COMMENTS:       To appear in SIGMORPHON 2020
HIGHLIGHT:      We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.

54, TITLE:      Distributional Reinforcement Learning with Ensembles
http://arxiv.org/abs/2003.10903
AUTHORS:        Björn Lindenberg ; Jonas Nordqvist ; Karl-Olof Lindahl
COMMENTS:       15 pages, 2 figures
HIGHLIGHT:      Specifically, we propose an extension to categorical reinforcement learning, where distributional learning targets are implicitly based on the total information gathered by an ensemble.