2020.04.17.txt

==========New Papers==========

1, TITLE:       Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation

http://arxiv.org/abs/2004.07386

AUTHORS:        Rajkumar Muthusamy ; Xiaoqian Huang ; Yahya Zweiri ; Lakmal Seneviratne ; Dongming Gan

COMMENTS:       18 pages, 14 figures

HIGHLIGHT:      In this paper, a novel dynamic vision-based finger system for slip detection and suppression is proposed.



2, TITLE:       Exploiting Categorical Structure Using Tree-Based Methods

http://arxiv.org/abs/2004.07383

AUTHORS:        Brian Lucena

COMMENTS:       To appear in AISTATS 2020 Proceedings

HIGHLIGHT:      We develop a mathematical framework for representing the
 structure of categorical variables and show how to generalize decision 
trees to make use of this structure.



3, TITLE:       Neuromorphic Eye-in-Hand Visual Servoing

http://arxiv.org/abs/2004.07398

AUTHORS:        Rajkumar Muthusamy ; Abdulla Ayyad ; Mohamad Halwani ; Yahya Zweiri ; Dongming Gan ; Lakmal Seneviratne

COMMENTS:       8 pages, 10 figures

HIGHLIGHT:      In this paper, we present a visual servoing method using
 an event camera and a switching control strategy to explore, reach and 
grasp to achieve a manipulation task.



4, TITLE:       Representation Learning of Histopathology Images using Graph Neural Networks

http://arxiv.org/abs/2004.07399

AUTHORS:        Mohammed Adnan ; Shivam Kalra ; Hamid R. Tizhoosh

COMMENTS:       Published in CVMI at CVPR Workshops, 2020

HIGHLIGHT:      We propose a two-stage framework for WSI representation learning.



5, TITLE:       Joint Supervised and Self-Supervised Learning for 3D Real-World Challenges

http://arxiv.org/abs/2004.07392

AUTHORS:        Antonio Alliegro ; Davide Boscaini ; Tatiana Tommasi

HIGHLIGHT:      We propose to enrich standard feature representations by
 leveraging self-supervision through a multi-task model that can solve a
 3D puzzle while learning the main task of shape classification or part 
segmentation.



6, TITLE:       Trakhtenbrot's Theorem in Coq, A Constructive Approach to Finite Model Theory

http://arxiv.org/abs/2004.07390

AUTHORS:        Dominik Kirst ; Dominique Larchey-Wendling

HIGHLIGHT:      We study finite first-order satisfiability (FSAT) in the constructive setting of dependent type theory.



7, TITLE:       Generalized Shortest Path-based Superpixels for Accurate Segmentation of Spherical Images

http://arxiv.org/abs/2004.07394

AUTHORS:        Rémi Giraud ; Rodrigo Borba Pinheiro ; Yannick Berthoumieu

HIGHLIGHT:      In this paper, we introduce a new superpixel method for 
spherical images called SphSPS (for Spherical Shortest Path-based 
Superpixels).



8, TITLE:       Where can I drive? Deep Ego-Corridor Estimation for Robust Automated Driving

http://arxiv.org/abs/2004.07639

AUTHORS:        Thomas Michalke ; Colin Wüst ; Di Feng ; Claudius Gläser ; Maxim Dolgov ; Fabian Timm

COMMENTS:       6 pages, preprint

HIGHLIGHT:      These deep learning approaches typically propose a 
classification of the free-space using for example semantic 
segmentation.



9, TITLE:       Towards Instance-Level Parser Selection for Cross-Lingual Transfer of Dependency Parsers

http://arxiv.org/abs/2004.07642

AUTHORS:        Robert Litschko ; Ivan Vulić ; Željko Agić ; Goran Glavaš

HIGHLIGHT:      In this work, we propose and argue for a novel 
cross-lingual transfer paradigm: instance-level parser selection (ILPS),
 and present a proof-of-concept study focused on instance-level 
selection in the framework of delexicalized parser transfer.



10, TITLE:      Top-Down Networks: A coarse-to-fine reimagination of CNNs

http://arxiv.org/abs/2004.07629

AUTHORS:        Ioannis Lelekas ; Nergis Tomen ; Silvia L. Pintea ; Jan C. van Gemert

COMMENTS:       CVPR Workshop Deep Vision 2020

HIGHLIGHT:      In this paper we reverse the feature extraction part of 
standard bottom-up architectures and turn them upside-down: We propose 
top-down networks.



11, TITLE:      A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation

http://arxiv.org/abs/2004.07633

AUTHORS:        Jan Deriu ; Katsiaryna Mlynchyk ; Philippe Schläpfer ; 
Alvaro Rodrigo ; Dirk von Grünigen ; Nicolas Kaiser ; Kurt Stockinger ; 
Eneko Agirre ; Mark Cieliebak

HIGHLIGHT:      In this paper, we introduce a novel methodology to 
efficiently construct a corpus for question answering over structured 
data.



12, TITLE:      Neural Data-to-Text Generation with Dynamic Content Planning

http://arxiv.org/abs/2004.07426

AUTHORS:        Kai Chen ; Fayuan Li ; Baotian Hu ; Weihua Peng ; Qingcai Chen ; Yajuan Lv ; Yong Zhu ; Hong Yu

COMMENTS:       25 pages, 1 figure and 6 tables

HIGHLIGHT:      To alleviate these problems, we propose a Neural 
data-to-text generation model with Dynamic content Planning, named NDP 
for abbreviation.



13, TITLE:      Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection

http://arxiv.org/abs/2004.07667

AUTHORS:        Shauli Ravfogel ; Yanai Elazar ; Hila Gonen ; Michael Twiton ; Yoav Goldberg

COMMENTS:       Accepted as a long paper in ACL 2020

HIGHLIGHT:      We present Iterative Null-space Projection (INLP), a 
novel method for removing information from neural representations.



14, TITLE:      Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks

http://arxiv.org/abs/2004.07407

AUTHORS:        Aryan Mobiny ; Pietro Antonio Cicalese ; Samira Zare ; 
Pengyu Yuan ; Mohammadsajad Abavisani ; Carol C. Wu ; Jitesh Ahuja ; 
Patricia M. de Groot ; Hien Van Nguyen

HIGHLIGHT:      Motivated by this challenge, our paper proposes a novel 
learning architecture, called Detail-Oriented Capsule Networks (DECAPS),
 for the automatic diagnosis of COVID-19 from Computed Tomography (CT) 
scans.



15, TITLE:      Old is $\mathbf{\mathcal{G}^{old}}$: Redefining the Adversarially Learned One-Class Classifier Training Paradigm

http://arxiv.org/abs/2004.07657

AUTHORS:        Muhammad Zaigham Zaheer ; Jin-ha Lee ; Marcella Astrid ; Seung-Ik Lee

COMMENTS:       Accepted at CVPR2020

HIGHLIGHT:      In this \bluetwo{study}, we propose a framework that 
effectively generates stable results across a wide range of training 
steps and allows us to use both \blue{the} generator and the 
discriminator of an adversarial model for efficient and robust anomaly 
detection.



16, TITLE:      Combinatorial 3D Shape Generation via Sequential Assembly

http://arxiv.org/abs/2004.07414

AUTHORS:        Jungtaek Kim ; Hyunsoo Chung ; Minsu Cho ; Jaesik Park

COMMENTS:       22 pages, 22 figures, 1 table. All images were resized to satisfy a size limit

HIGHLIGHT:      In this work, we propose a new 3D shape generation 
algorithm that aims to create such a combinatorial configuration from a 
set of volumetric primitives. We also introduce a new dataset for 
combinatorial 3D shape generation.



17, TITLE:      Joint Semantic Segmentation and Boundary Detection using Iterative Pyramid Contexts

http://arxiv.org/abs/2004.07684

AUTHORS:        Mingmin Zhen ; Jinglu Wang ; Lei Zhou ; Shiwei Li ; Tianwei Shen ; Jiaxiang Shang ; Tian Fang ; Quan Long

HIGHLIGHT:      In this paper, we present a joint multi-task learning 
framework for semantic segmentation and boundary detection.



18, TITLE:      Contextual Two-Stage U-Nets for Robust Pulmonary Lobe Segmentation in CT Scans of COVID-19 and COPD Patients

http://arxiv.org/abs/2004.07443

AUTHORS:        Weiyi Xie ; Colin Jacobs ; Jean-Paul Charbonnier ; Bram van Ginneken

HIGHLIGHT:      In this paper, we propose a contextual two-stage U-net 
(CTSU-Net) that leverages global context by introducing a first stage in
 which the receptive field encompasses the entire scan and by using a 
novel non-local neural network module.



19, TITLE:      Resolving the Optimal Metric Distortion Conjecture

http://arxiv.org/abs/2004.07447

AUTHORS:        Vasilis Gkatzelis ; Daniel Halpern ; Nisarg Shah

HIGHLIGHT:      We propose algorithms that choose a point in C using 
only these rankings as input and we provide bounds on their distortion 
(worst-case approximation ratio).



20, TITLE:      On the use of Benford's law to detect GAN-generated images

http://arxiv.org/abs/2004.07682

AUTHORS:        Nicolò Bonettini ; Paolo Bestagini ; Simone Milani ; Stefano Tubaro

HIGHLIGHT:      In this paper, we study the possibility of using 
Benford's law to discriminate GAN-generated images from natural 
photographs.



21, TITLE:      Do sequence-to-sequence VAEs learn global features of sentences?

http://arxiv.org/abs/2004.07683

AUTHORS:        Tom Bosc ; Pascal Vincent

HIGHLIGHT:      To alleviate this, we propose variants based on bag-of-words assumptions and language model pretraining.



22, TITLE:      Video Face Manipulation Detection Through Ensemble of CNNs

http://arxiv.org/abs/2004.07676

AUTHORS:        Nicolò Bonettini ; Edoardo Daniele Cannas ; Sara Mandelli ; Luca Bondi ; Paolo Bestagini ; Stefano Tubaro

HIGHLIGHT:      In this paper, we tackle the problem of face 
manipulation detection in video sequences targeting modern facial 
manipulation techniques.



23, TITLE:      Non-Autoregressive Machine Translation with Latent Alignments

http://arxiv.org/abs/2004.07437

AUTHORS:        Chitwan Saharia ; William Chan ; Saurabh Saxena ; Mohammad Norouzi

HIGHLIGHT:      This paper investigates two latent alignment models for 
non-autoregressive machine translation, namely CTC and Imputer.



24, TITLE:      Measuring Human and Economic Activity from Satellite 
Imagery to Support City-Scale Decision-Making during COVID-19 Pandemic

http://arxiv.org/abs/2004.07438

AUTHORS:        Rodrigo Minetto ; Mauricio Pamplona Segundo ; Gilbert Rotich ; Sudeep Sarkar

COMMENTS:       12 pages, 9 figures, 2 tables

HIGHLIGHT:      In this work, we use a deep learning approach that 
combines strategic location sampling and an ensemble of lightweight 
convolutional neural networks (CNNs) to recognize specific elements in 
satellite images and compute economic indicators based on it, 
automatically.



25, TITLE:      Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation

http://arxiv.org/abs/2004.07672

AUTHORS:        Haoyu Song ; Yan Wang ; Wei-Nan Zhang ; Xiaojiang Liu ; Ting Liu

COMMENTS:       Accepted by ACL2020 main conference. The Camera-ready version will be updated soon

HIGHLIGHT:      In this work, we introduce a three-stage framework that 
employs a generate-delete-rewrite mechanism to delete inconsistent words
 from a generated response prototype and further rewrite it to a 
personality-consistent one.



26, TITLE:      PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks

http://arxiv.org/abs/2004.07464

AUTHORS:        Wenwen Yu ; Ning Lu ; Xianbiao Qi ; Ping Gong ; Rong Xiao

COMMENTS:       The first two authors contributed equally to this work. 8 pages, 3 figures, 4 tables

HIGHLIGHT:      In this paper, we introduce PICK, a framework that is 
effective and robust in handling complex documents layout for KIE by 
combining graph learning with graph convolution operation, yielding a 
richer semantic representation containing the textual and visual 
features and global layout without ambiguity.



27, TITLE:      Paraphrase Augmented Task-Oriented Dialog Generation

http://arxiv.org/abs/2004.07462

AUTHORS:        Silin Gao ; Yichi Zhang ; Zhijian Ou ; Zhou Yu

COMMENTS:       Accepted to ACL 2020, 10 pages, 2 figures

HIGHLIGHT:      We propose a paraphrase augmented response generation 
(PARG) framework that jointly trains a paraphrase model and a response 
generation model to improve the dialog generation performance.



28, TITLE:      Single upper limb pose estimation method based on improved stacked hourglass network

http://arxiv.org/abs/2004.07456

AUTHORS:        Gang Peng ; Yuezhi Zheng ; Jianfeng Li ; Jin Yang ; Zhonghua Deng

HIGHLIGHT:      For use in human-machine cooperative operations, this 
paper proposes a single-person upper limb pose estimation method based 
on an end-to-end approach for accurate and real-time limb pose 
estimation.



29, TITLE:      The Right Tool for the Job: Matching Model and Instance Complexities

http://arxiv.org/abs/2004.07453

AUTHORS:        Roy Schwartz ; Gabi Stanovsky ; Swabha Swayamdipta ; Jesse Dodge ; Noah A. Smith

COMMENTS:       ACL 2020; 12 pages; code available in https://github.com/allenai/sledgehammer

HIGHLIGHT:      To better respect a given inference budget, we propose a
 modification to contextual representation fine-tuning which, during 
inference, allows for an early (and fast) "exit" from neural network 
calculations for simple instances, and late (and accurate) exit for hard
 instances.



30, TITLE:      In Search of Life: Learning from Synthetic Data to Detect Vital Signs in Videos

http://arxiv.org/abs/2004.07691

AUTHORS:        Florin Condrea ; Victor-Andrei Ivan ; Marius Leordeanu

HIGHLIGHT:      In this paper we address this limitation through a novel
 deep learning approach, in which a recurrent deep neural network is 
trained to detect vital signs in the infrared thermal domain from purely
 synthetic data.



31, TITLE:      A Game Theoretic Framework for Model Based Reinforcement Learning

http://arxiv.org/abs/2004.07804

AUTHORS:        Aravind Rajeswaran ; Igor Mordatch ; Vikash Kumar

COMMENTS:       Project webpage: https://sites.google.com/view/mbrl-game

HIGHLIGHT:      To help expose the practical challenges in MBRL and 
simplify algorithm design from the lens of abstraction, we develop a new
 framework that casts MBRL as a game between: (1) a policy player, which
 attempts to maximize rewards under the learned model; (2) a model 
player, which attempts to fit the real-world data collected by the 
policy player.



32, TITLE:      Geometry-Aware Gradient Algorithms for Neural Architecture Search

http://arxiv.org/abs/2004.07802

AUTHORS:        Liam Li ; Mikhail Khodak ; Maria-Florina Balcan ; Ameet Talwalkar

COMMENTS:       31 pages, 5 figures

HIGHLIGHT:      Invoking the theory of mirror descent, we present a 
unifying framework for designing and analyzing gradient-based NAS 
methods that exploit the underlying problem structure to quickly find 
high-performance architectures.



33, TITLE:      Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks

http://arxiv.org/abs/2004.07601

AUTHORS:        Shaoxiong Ji ; Xue Li ; Zi Huang ; Erik Cambria

HIGHLIGHT:      In this paper, we enhance text representation with 
lexicon-based sentiment scores and latent topics, and propose to use 
relation networks for detecting suicidal ideation and mental disorders 
with related risk indicators.



34, TITLE:      Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation

http://arxiv.org/abs/2004.07624

AUTHORS:        Yujia Zhou ; Shumao Pang ; Jun Cheng ; Yuhang Sun ; Yi 
Wu ; Lei Zhao ; Yaqin Liu ; Zhentai Lu ; Wei Yang ; Qianjin Feng

HIGHLIGHT:      In this study, we constructed pyramidal feature sets on 
moving and fixed images and used the warped moving and fixed features to
 estimate their "residual" deformation field at each scale, called the 
Pyramidal Residual Deformation Field Estimation module (PRDFE-Module).



35, TITLE:      Distributed Evolution of Deep Autoencoders

http://arxiv.org/abs/2004.07607

AUTHORS:        Jeff Hajewski ; Suely Oliveira ; Xiaoyu Xing

HIGHLIGHT:      In this work we present a distributed system that uses 
an efficient evolutionary algorithm to design a modular autoencoder.



36, TITLE:      A Local Descriptor with Physiological Characteristic for Finger Vein Recognition

http://arxiv.org/abs/2004.07489

AUTHORS:        Liping Zhang ; Weijun Li ; Xin Ning

HIGHLIGHT:      In this work, we propose a finger vein-specific local 
feature descriptors based physiological characteristic of finger vein 
patterns, i.e., histogram of oriented physiological Gabor responses 
(HOPGR), for finger vein recognition.



37, TITLE:      Hercules: An Autonomous Logistic Vehicle for Contact-less Goods Transportation During the COVID-19 Outbreak

http://arxiv.org/abs/2004.07480

AUTHORS:        Tianyu Liu ; Qinghai Liao ; Lu Gan ; Fulong Ma ; Jie 
Cheng ; Xupeng Xie ; Zhe Wang ; Yingbing Chen ; Yilong Zhu ; Shuyang 
Zhang ; Zhengyong Chen ; Yang Liu ; Yang Yu ; Zitong Guo ; Guang Li ; 
Peidong Yuan ; Dong Han ; Yuying Chen ; Haoyang Ye ; Jianhao Jiao ; Peng
 Yun ; Zhenhua Xu ; Hengli Wang ; Huaiyang Huang ; Sukai Wang ; Peide 
Cai ; Yuxiang Sun ; Yandong Liu ; Lujia Wang ; Ming Liu

HIGHLIGHT:      This article presents Hercules, an autonomous logistic 
vehicle used for contact-less goods transportation during the outbreak 
of COVID-19.



38, TITLE:      Asynchronous Interaction Aggregation for Action Detection

http://arxiv.org/abs/2004.07485

AUTHORS:        Jiajun Tang ; Jin Xia ; Xinzhi Mu ; Bo Pang ; Cewu Lu

HIGHLIGHT:      We propose the Asynchronous Interaction Aggregation 
network (AIA) that leverages different interactions to boost action 
detection.



39, TITLE:      ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking

http://arxiv.org/abs/2004.07482

AUTHORS:        Fatemeh Saleh ; Sadegh Aliakbarian ; Mathieu Salzmann ; Stephen Gould

HIGHLIGHT:      In this paper, we introduce a probabilistic 
autoregressive generative model to score tracklet proposals by directly 
measuring the likelihood that a tracklet represents natural motion.



40, TITLE:      Multi-Objective Evolutionary approach for the 
Performance Improvement of Learners using Ensembling Feature selection 
and Discretization Technique on Medical data

http://arxiv.org/abs/2004.07478

AUTHORS:        Deepak Singh ; Dilip Singh Sisodia ; Pradeep Singh

HIGHLIGHT:      This paper proposes a novel multi-objective based 
dimensionality reduction framework, which incorporates both 
discretization and feature reduction as an ensemble model for performing
 feature selection and discretization.



41, TITLE:      Destination Prediction Based on Partial Trajectory Data

http://arxiv.org/abs/2004.07473

AUTHORS:        Patrick Ebel ; Ibrahim Emre Göl ; Christoph Lingenfelder ; Andreas Vogelsang

COMMENTS:       2020 IEEE Intelligent Vehicles Symposium

HIGHLIGHT:      To evaluate the method, we compare multiple neural 
architectures and present the experimental results of the destination 
prediction.



42, TITLE:      SQE: a Self Quality Evaluation Metric for Parameters Optimization in Multi-Object Tracking

http://arxiv.org/abs/2004.07472

AUTHORS:        Yanru Huang ; Feiyu Zhu ; Zheni Zeng ; Xi Qiu ; Yuan Shen ; Jianan Wu

HIGHLIGHT:      We present a novel self quality evaluation metric SQE 
for parameters optimization in the challenging yet critical multi-object
 tracking task.



43, TITLE:      Learning Furniture Compatibility with Graph Neural Networks

http://arxiv.org/abs/2004.07268

AUTHORS:        Luisa F. Polania ; Mauricio Flores ; Yiran Li ; Matthew Nokleby

COMMENTS:       Accepted for publication at CVPR Workshops

HIGHLIGHT:      We propose a graph neural network (GNN) approach to the 
problem of predicting the stylistic compatibility of a set of furniture 
items from images. We further introduce a new dataset, called the Target
 Furniture Collections dataset, which contains over 6000 furniture items
 that have been hand-curated by stylists to make up 1632 compatible 
sets.



44, TITLE:      Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework

http://arxiv.org/abs/2004.07265

AUTHORS:        Jiehang Zeng ; Lu Liu ; Xiaoqing Zheng

COMMENTS:       7 pages, 1 figures

HIGHLIGHT:      We present a learning method using generative 
adversarial architecture designed to embed the entities and relations of
 the knowledge graphs into a continuous vector space.



45, TITLE:      LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation

http://arxiv.org/abs/2004.07499

AUTHORS:        Dong-Ho Lee ; Rahul Khanna ; Bill Yuchen Lin ; Jamin 
Chen ; Seyeon Lee ; Qinyuan Ye ; Elizabeth Boschee ; Leonardo Neves ; 
Xiang Ren

COMMENTS:       Accepted to the ACL 2020 (demo). The first two authors contributed equally. Project page: http://inklab.usc.edu/leanlife/

HIGHLIGHT:      On three popular NLP tasks (named entity recognition, 
relation extraction, sentiment analysis), we find that using this 
enhanced supervision allows our models to surpass competitive baseline 
F1 scores by more than 5-10 percentage points, while using 2X times 
fewer labeled instances.



46, TITLE:      Steiner Trees for Hereditary Graph Classes: a Treewidth Perspective

http://arxiv.org/abs/2004.07492

AUTHORS:        Hans Bodlaender ; Nick Brettell ; Matthew Johnson ; Giacomo Paesani ; Daniel Paulusma ; Erik Jan van Leeuwen

HIGHLIGHT:      We consider the classical problems (Edge) Steiner Tree 
and Vertex Steiner Tree after restricting the input to some class of 
graphs characterized by a small set of forbidden induced subgraphs.



47, TITLE:      TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition

http://arxiv.org/abs/2004.07493

AUTHORS:        Bill Yuchen Lin ; Dong-Ho Lee ; Ming Shen ; Ryan Moreno ; Xiao Huang ; Prashant Shiralkar ; Xiang Ren

COMMENTS:       Accepted to the ACL 2020. The first two answers contributed equally. Code and data: https://github.com/INK-USC/TriggerNER

HIGHLIGHT:      In this paper, we introduce "entity triggers", an 
effective proxy of human explanations for facilitating label-efficient 
learning of NER models.



48, TITLE:      Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems

http://arxiv.org/abs/2004.07294

AUTHORS:        Martin Hughes ; Marc Goerigk ; Trivikram Dokka

HIGHLIGHT:      We develop algorithms capable of tackling robust 
black-box optimisation problems, where the number of model runs is 
limited.



49, TITLE:      Deep Generation of Coq Lemma Names Using Elaborated Terms

http://arxiv.org/abs/2004.07761

AUTHORS:        Pengyu Nie ; Karl Palmskog ; Junyi Jessy Li ; Milos Gligoric

COMMENTS:       Accepted in IJCAR 2020

HIGHLIGHT:      We present novel generation models for learning and suggesting lemma names for Coq projects.



50, TITLE:      Continual Learning with Extended Kronecker-factored Approximate Curvature

http://arxiv.org/abs/2004.07507

AUTHORS:        Janghyeon Lee ; Hyeong Gwon Hong ; Donggyu Joo ; Junmo Kim

COMMENTS:       CVPR 2020

HIGHLIGHT:      We propose a quadratic penalty method for continual 
learning of neural networks that contain batch normalization (BN) 
layers.



51, TITLE:      Removing Algebraic Data Types from Constrained Horn Clauses Using Difference Predicates

http://arxiv.org/abs/2004.07749

AUTHORS:        Emanuele De Angelis ; Fabio Fioravanti ; Alberto Pettorossi ; Maurizio Proietti

COMMENTS:       10th International Joint Conference on Automated Reasoning (IJCAR 2020). Springer, 2020

HIGHLIGHT:      We propose a new technique for transforming CHCs with 
ADTs into CHCs where predicates are defined over basic types, such as 
integers and booleans, only.



52, TITLE:      On Reductions of Hintikka Sets for Higher-Order Logic

http://arxiv.org/abs/2004.07506

AUTHORS:        Alexander Steen ; Christoph Benzmüller

COMMENTS:       9 pages

HIGHLIGHT:      On Reductions of Hintikka Sets for Higher-Order Logic



53, TITLE:      Explainable Image Classification with Evidence Counterfactual

http://arxiv.org/abs/2004.07511

AUTHORS:        Tom Vermeire ; David Martens

COMMENTS:       23 pages, 13 figures

HIGHLIGHT:      In this paper, SEDC is introduced as a model-agnostic 
instance-level explanation method for image classification to obtain 
visual counterfactual explanations.



54, TITLE:      Local-Global Video-Text Interactions for Temporal Grounding

http://arxiv.org/abs/2004.07514

AUTHORS:        Jonghwan Mun ; Minsu Cho ; Bohyung Han

COMMENTS:       CVPR 2020; code available in https://github.com/JonghwanMun/LGI4temporalgrounding

HIGHLIGHT:      We tackle this problem using a novel regression-based 
model that learns to extract a collection of mid-level features for 
semantic phrases in a text query, which corresponds to important 
semantic entities described in the query (e.g., actors, objects, and 
actions), and reflect bi-modal interactions between the linguistic 
features of the query and the visual features of the video in multiple 
levels.



55, TITLE:      Fast Template Matching and Update for Video Object Tracking and Segmentation

http://arxiv.org/abs/2004.07538

AUTHORS:        Mingjie Sun ; Jimin Xiao ; Eng Gee Lim ; Bingfeng Zhang ; Yao Zhao

HIGHLIGHT:      In this paper, the main task we aim to tackle is the 
multi-instance semi-supervised video object segmentation across a 
sequence of frames where only the first-frame box-level ground-truth is 
provided.



56, TITLE:      ESResNet: Environmental Sound Classification Based on Visual Domain Models

http://arxiv.org/abs/2004.07301

AUTHORS:        Andrey Guzhov ; Federico Raue ; Jörn Hees ; Andreas Dengel

COMMENTS:       8 pages, 4 figures; submitted to ICPR 2020

HIGHLIGHT:      The contribution of this paper is twofold.



57, TITLE:      Multi-Object Tracking with Siamese Track-RCNN

http://arxiv.org/abs/2004.07786

AUTHORS:        Bing Shuai ; Andrew G. Berneshawi ; Davide Modolo ; Joseph Tighe

HIGHLIGHT:      Towards this, we propose Siamese Track-RCNN, a two stage
 detect-and-track framework which consists of three functional branches:
 (1) the detection branch localizes object instances; (2) the 
Siamese-based track branch estimates the object motion and (3) the 
object re-identification branch re-activates the previously terminated 
tracks when they re-emerge.



58, TITLE:      Multimodal and multiview distillation for real-time player detection on a football field

http://arxiv.org/abs/2004.07544

AUTHORS:        Anthony Cioppa ; Adrien Deliège ; Noor Ul Huda ; Rikke Gade ; Marc Van Droogenbroeck ; Thomas B. Moeslund

COMMENTS:       Accepted for the CVSports workshop of CVPR 2020 ; 8 pages + references

HIGHLIGHT:      In this work, we train a network in a knowledge 
distillation approach in which the student and the teacher have 
different modalities and a different view of the same scene.



59, TITLE:      RGBD-Dog: Predicting Canine Pose from RGBD Sensors

http://arxiv.org/abs/2004.07788

AUTHORS:        Sinead Kearney ; Wenbin Li ; Martin Parsons ; Kwang In Kim ; Darren Cosker

COMMENTS:       18 pages, 16 figures, to be published in CVPR 2020

HIGHLIGHT:      In our work, we focus on the problem of 3D canine pose 
estimation from RGBD images, recording a diverse range of dog breeds 
with several Microsoft Kinect v2s, simultaneously obtaining the 3D 
ground truth skeleton via a motion capture system. We generate a dataset
 of synthetic RGBD images from this data.



60, TITLE:      Shortcut Learning in Deep Neural Networks

http://arxiv.org/abs/2004.07780

AUTHORS:        Robert Geirhos ; Jörn-Henrik Jacobsen ; Claudio 
Michaelis ; Richard Zemel ; Wieland Brendel ; Matthias Bethge ; Felix A.
 Wichmann

COMMENTS:       perspective article

HIGHLIGHT:      In this perspective we seek to distil how many of deep 
learning's problem can be seen as different symptoms of the same 
underlying problem: shortcut learning. Based on these observations, we 
develop a set of recommendations for model interpretation and 
benchmarking, highlighting recent advances in machine learning to 
improve robustness and transferability from the lab to real-world 
applications.



61, TITLE:      SCOUT: Self-aware Discriminant Counterfactual Explanations

http://arxiv.org/abs/2004.07769

AUTHORS:        Pei Wang ; Nuno Vasconcelos

COMMENTS:       Accepted to CVPR2020

HIGHLIGHT:      SCOUT: Self-aware Discriminant Counterfactual Explanations



62, TITLE:      DeepFakes Evolution: Analysis of Facial Regions and Fake Detection Performance

http://arxiv.org/abs/2004.07532

AUTHORS:        Ruben Tolosana ; Sergio Romero-Tapiador ; Julian Fierrez ; Ruben Vera-Rodriguez

HIGHLIGHT:      This study provides an exhaustive analysis of both 1st 
and 2nd DeepFake generations in terms of facial regions and fake 
detection performance.



63, TITLE:      Continual Reinforcement Learning with Multi-Timescale Replay

http://arxiv.org/abs/2004.07530

AUTHORS:        Christos Kaplanis ; Claudia Clopath ; Murray Shanahan

HIGHLIGHT:      In this paper, we propose a multi-timescale replay (MTR)
 buffer for improving continual learning in RL agents faced with 
environments that are changing continuously over time at timescales that
 are unknown to the agent.



64, TITLE:      Kvistur 2.0: a BiLSTM Compound Splitter for Icelandic

http://arxiv.org/abs/2004.07776

AUTHORS:        Jón Friðrik Daðason ; David Erik Mollberg ; Hrafn Loftsson ; Kristín Bjarnadóttir

COMMENTS:       Accepted at LREC 2020

HIGHLIGHT:      In this paper, we present a character-based BiLSTM model
 for splitting Icelandic compound words, and show how varying amounts of
 training data affects the performance of the model.



65, TITLE:      Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks

http://arxiv.org/abs/2004.07777

AUTHORS:        Bhanuka Mahanama ; Yasith Jayawardana ; Sampath Jayarathna

HIGHLIGHT:      In this paper, we propose Gaze-Net: A capsule network 
capable of decoding, representing, and estimating gaze information from 
ocular region images.



66, TITLE:      The Impact of Heterogeneity and Geometry on the Proof Complexity of Random Satisfiability

http://arxiv.org/abs/2004.07319

AUTHORS:        Thomas Bläsius ; Tobias Friedrich ; Andreas Göbel ; Jordi Levy ; Ralf Rothenberger

COMMENTS:       50 pages, 2 figures

HIGHLIGHT:      To understand the impact of these two properties on SAT,
 we study the proof complexity of random k-SAT models that allow to 
control heterogeneity and locality.



67, TITLE:      Building a Multi-domain Neural Machine Translation Model using Knowledge Distillation

http://arxiv.org/abs/2004.07324

AUTHORS:        Idriss Mghabbar ; Pirashanth Ratnamogan

HIGHLIGHT:      In this paper, we propose a new training pipeline where 
knowledge distillation and multiple specialized teachers allow us to 
efficiently finetune a model without adding new costs at inference time.



68, TITLE:      Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection

http://arxiv.org/abs/2004.07568

AUTHORS:        Fa-Ting Hong ; Wei-Hong Li ; Wei-Shi Zheng

HIGHLIGHT:      To overcome this problem, we propose learning important 
people detection on partially annotated images. We have collected two 
large-scale datasets for evaluation.



69, TITLE:      AMPSO: Artificial Multi-Swarm Particle Swarm Optimization

http://arxiv.org/abs/2004.07561

AUTHORS:        Haohao Zhou ; Xiangzhi Wei

HIGHLIGHT:      In this paper we propose a novel artificial multi-swarm 
PSO which consists of an exploration swarm, an artificial exploitation 
swarm and an artificial convergence swarm.



70, TITLE:      RescueNet: Joint Building Segmentation and Damage Assessment from Satellite Imagery

http://arxiv.org/abs/2004.07312

AUTHORS:        Rohit Gupta ; Mubarak Shah

COMMENTS:       7 pages, 3 figures, submitted to ICPR

HIGHLIGHT:      We propose RescueNet, a unified model that can 
simultaneously segment buildings and assess the damage levels to 
individual buildings and can be trained end-toend.



71, TITLE:      Evaluation of Generalizability of Neural Program Analyzers under Semantic-Preserving Transformations

http://arxiv.org/abs/2004.07313

AUTHORS:        Md. Rafiqul Islam Rabin ; Mohammad Amin Alipour

COMMENTS:       work in-progress

HIGHLIGHT:      In this paper, we perform a large-scale evaluation of 
the generalizability of two popular neural program analyzers using seven
 semantically-equivalent transformations of programs.



72, TITLE:      Framework for $\exists \mathbb{R}$-Completeness of Two-Dimensional Packing Problems

http://arxiv.org/abs/2004.07558

AUTHORS:        Mikkel Abrahamsen ; Tillmann Miltzow ; Nadja Seiferth

COMMENTS:       98 pages, 60 figures

HIGHLIGHT:      We consider packing problems where only translations are
 allowed as the motions, and problems where arbitrary rigid motions are 
allowed, i.e., both translations and rotations.



73, TITLE:      An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers

http://arxiv.org/abs/2004.07317

AUTHORS:        Bernhard Liebl ; Manuel Burghardt

COMMENTS:       Evaluation of deep neural networks for the segmentation 
of pages of historical newspapers; 21 pages total (incl. references and 
appendix), 7 figures, 5 tables

HIGHLIGHT:      In this paper, we perform a systematic evaluation of 11 
different published DNN backbone architectures and 9 different tiling 
and scaling configurations for separating text, tables or table column 
lines.



74, TITLE:      The Dimensions of Hyperspaces

http://arxiv.org/abs/2004.07798

AUTHORS:        Jack H. Lutz ; Neil Lutz ; Elvira Mayordomo

HIGHLIGHT:      We use the theory of computing to prove general 
hyperspace dimension theorems for three important fractal dimensions.



75, TITLE:      There is Strength in Numbers: Avoiding the 
Hypothesis-Only Bias in Natural Language Inference via Ensemble 
Adversarial Training

http://arxiv.org/abs/2004.07790

AUTHORS:        Joe Stacey ; Pasquale Minervini ; Haim Dubossarsky ; Sebastian Riedel ; Tim Rocktäschel

COMMENTS:       8 pages

HIGHLIGHT:      As a solution, we propose using an ensemble of 
adversaries during the training, encouraging the model to jointly 
decrease the accuracy of these different adversaries while fitting the 
data.



76, TITLE:      HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data

http://arxiv.org/abs/2004.07347

AUTHORS:        Wenhu Chen ; Hanwen Zha ; Zhiyu Chen ; Wenhan Xiong ; Hong Wang ; William Wang

COMMENTS:       Dataset Description of HybridQA

HIGHLIGHT:      To fill in the gap, we present \dataset, a new 
large-scale question-answering dataset that requires reasoning on 
heterogeneous information.



77, TITLE:      Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

http://arxiv.org/abs/2004.07333

AUTHORS:        Colin Bellinger ; Rory Coles ; Mark Crowley ; Isaac Tamblyn

COMMENTS:       To appear in the Canadian Conference on Artificial Intelligence, 2020

HIGHLIGHT:      In this paper, we present a physics-inspired semi-Markov RL environment, namely the phase change environment.



78, TITLE:      An Adaptive Intelligence Algorithm for Undersampled Knee
 MRI Reconstruction: Application to the 2019 fastMRI Challenge

http://arxiv.org/abs/2004.07339

AUTHORS:        Nicola Pezzotti ; Sahar Yousefi ; Mohamed S. Elmahdy ; 
Jeroen van Gemert ; Christophe Schülke ; Mariya Doneva ; Tim Nielsen ; 
Sergey Kastryulin ; Boudewijn P. F. Lelieveldt ; Matthias J. P. van Osch
 ; Elwin de Weerdt ; Marius Staring

HIGHLIGHT:      In this work, we present the application of adaptive intelligence to accelerate MR acquisition.



79, TITLE:      Spatially Attentive Output Layer for Image Classification

http://arxiv.org/abs/2004.07570

AUTHORS:        Ildoo Kim ; Woonhyuk Baek ; Sungwoong Kim

COMMENTS:       First two authors contributed equally. Accepted at CVPR 2020

HIGHLIGHT:      In this paper, we propose a novel spatial output layer 
on top of the existing convolutional feature maps to explicitly exploit 
the location-specific output information.



80, TITLE:      Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision

http://arxiv.org/abs/2004.07703

AUTHORS:        Fei Pan ; Inkyu Shin ; Francois Rameau ; Seokju Lee ; In So Kweon

COMMENTS:       Accepted to CVPR 2020 as an Oral Presentation. Code is available at https://github.com/feipan664/IntraDA

HIGHLIGHT:      In this work, we propose a two-step self-supervised 
domain adaptation approach to minimize the inter-domain and intra-domain
 gap together.



81, TITLE:      Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing

http://arxiv.org/abs/2004.07707

AUTHORS:        Declan Oller ; Tobias Glasmachers ; Giuseppe Cuccu

HIGHLIGHT:      We propose a novel method for analyzing and visualizing 
the complexity of standard reinforcement learning (RL) benchmarks based 
on score distributions.



82, TITLE:      Knowledge Distillation for Action Anticipation via Label Smoothing

http://arxiv.org/abs/2004.07711

AUTHORS:        Guglielmo Camporese ; Pasquale Coscia ; Antonino Furnari ; Giovanni Maria Farinella ; Lamberto Ballan

HIGHLIGHT:      In our experiments, we implement a multi-modal framework
 based on long short-term memory (LSTM) networks to anticipate future 
actions which is able to summarise past observations while making 
predictions of the future at different time steps.



83, TITLE:      Cross-lingual Contextualized Topic Models with Zero-shot Learning

http://arxiv.org/abs/2004.07737

AUTHORS:        Federico Bianchi ; Silvia Terragni ; Dirk Hovy ; Debora Nozza ; Elisabetta Fersini

HIGHLIGHT:      In this paper, we introduce a zero-shot cross-lingual 
topic model, i.e., our model learns topics on one language (here, 
English), and predicts them for documents in other languages.



84, TITLE:      Blur Aware Calibration of Multi-Focus Plenoptic Camera

http://arxiv.org/abs/2004.07745

AUTHORS:        Mathieu Labussière ; Céline Teulière ; Frédéric Bernardin ; Omar Ait-Aider

COMMENTS:       2020 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:      This paper presents a novel calibration algorithm for 
Multi-Focus Plenoptic Cameras (MFPCs) using raw images only.



85, TITLE:      Image Quality Assessment: Unifying Structure and Texture Similarity

http://arxiv.org/abs/2004.07728

AUTHORS:        Keyan Ding ; Kede Ma ; Shiqi Wang ; Eero P. Simoncelli

HIGHLIGHT:      Here we develop the first full-reference image quality model with explicit tolerance to texture resampling.







==========Updates to Previous Papers==========

1, TITLE:       Probabilistic Diagnostic Tests for Degradation Problems in Supervised Learning

http://arxiv.org/abs/2004.02988

AUTHORS:        Gustavo A. Valencia-Zapata ; Carolina Gonzalez-Canas ; Michael G. Zentner ; Okan Ersoy ; Gerhard Klimeck

HIGHLIGHT:      Probabilistic Diagnostic Tests for Degradation Problems in Supervised Learning



2, TITLE:       A Transductive Approach for Video Object Segmentation

http://arxiv.org/abs/2004.07193

AUTHORS:        Yizhuo Zhang ; Zhirong Wu ; Houwen Peng ; Stephen Lin

COMMENTS:       To Appear in CVPR 2020

HIGHLIGHT:      To address this issue, we propose a simple yet strong 
transductive method, in which additional modules, datasets, and 
dedicated architectural designs are not needed.



3, TITLE:       Info Intervention

http://arxiv.org/abs/1907.11090

AUTHORS:        Gong Heyang ; Zhu Ke

HIGHLIGHT:      To highlight the view of causality as information 
transfer we propose the info intervention, which intervening the sending
 out information.



4, TITLE:       BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

http://arxiv.org/abs/2002.05700

AUTHORS:        Gregory Kahn ; Pieter Abbeel ; Sergey Levine

HIGHLIGHT:      In this work, we investigate how to move beyond these 
purely geometric-based approaches using a method that learns about 
physical navigational affordances from experience.



5, TITLE:       Integrating Discrete and Neural Features via Mixed-feature Trans-dimensional Random Field Language Models

http://arxiv.org/abs/2002.05967

AUTHORS:        Silin Gao ; Zhijian Ou ; Wei Yang ; Huifang Xu

COMMENTS:       Accepted for presentation at ICASSP 2020, 5 pages, 2 figures

HIGHLIGHT:      This paper develops a mixed-feature TRF LM and 
demonstrates its advantage in integrating discrete and neural features.



6, TITLE:       Generation mechanism of cell assembly to store information about hand recognition

http://arxiv.org/abs/1909.08158

AUTHORS:        Takahiro Homma

HIGHLIGHT:      In this paper, the relationship between hand movements 
and weight changes during the simulated learning process for hand regard
 was used to elucidate the mechanism through which inhibitory weights 
are generated, which plays an important role in the formation of cell 
assemblies.



7, TITLE:       Distinguish Confusing Law Articles for Legal Judgment Prediction

http://arxiv.org/abs/2004.02557

AUTHORS:        Nuo Xu ; Pinghui Wang ; Long Chen ; Li Pan ; Xiaoyan Wang ; Junzhou Zhao

COMMENTS:       This work has been accepted by the 58th Annual Meeting 
of the Association for Computational Linguistics (ACL 2020)

HIGHLIGHT:      In this paper, we present an end-to-end model, LADAN, to solve the task of LJP.



8, TITLE:       Learning to Predict More Accurate Text Instances for Scene Text Detection

http://arxiv.org/abs/1911.07423

AUTHORS:        XiaoQian Li ; Jie Liu ; ShuWu Zhang ; GuiXuan Zhang

HIGHLIGHT:      In this paper, a pixel-based text detector is proposed 
to facilitate the representation and prediction of text instances with 
arbitrary shapes in a simple manner.



9, TITLE:       Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence

http://arxiv.org/abs/1905.00719

AUTHORS:        Rongpeng Li ; Zhifeng Zhao ; Xing Xu ; Fei Ni ; Honggang Zhang

COMMENTS:       6 figures; accepted by IEEE Wireless Commun with the 
title "The Collective Advantage for Advancing Communications and 
Intelligence"

HIGHLIGHT:      In particular, we introduce a regular language (i.e., 
the information economy metalanguage) supporting the future collective 
communications to augment human intelligence and explain its potential 
applications in naming Internet information and pushing information 
centric networks forward.



10, TITLE:      Interactive Machine Comprehension with Information Seeking Agents

http://arxiv.org/abs/1908.10449

AUTHORS:        Xingdi Yuan ; Jie Fu ; Marc-Alexandre Cote ; Yi Tay ; Christopher Pal ; Adam Trischler

COMMENTS:       ACL2020

HIGHLIGHT:      In this paper, we propose a simple method that reframes 
existing MRC datasets as interactive, partially observable environments.



11, TITLE:      Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

http://arxiv.org/abs/2003.08284

AUTHORS:        Weikai Tan ; Nannan Qin ; Lingfei Ma ; Ying Li ; Jing Du ; Guorong Cai ; Ke Yang ; Jonathan Li

COMMENTS:       Toronto-3D dataset can be downloaded at https://github.com/WeikaiTan/Toronto-3D

HIGHLIGHT:      This paper introduces Toronto-3D, a large-scale urban 
outdoor point cloud dataset acquired by a MLS system in Toronto, Canada 
for semantic segmentation.



12, TITLE:      Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model

http://arxiv.org/abs/2003.13633

AUTHORS:        F. Martínez-Álvarez ; G. Asencio-Cortés ; J. F. Torres ;
 D. Gutiérrez-Avilés ; L. Melgar-García ; R. Pérez-Chacón ; C. 
Rubio-Escudero ; J. C. Riquelme ; A. Troncoso

COMMENTS:       30 pages, 4 figures

HIGHLIGHT:      A novel bioinspired metaheuristic is proposed in this 
work, simulating how the coronavirus spreads and infects healthy people.



13, TITLE:      Perspective Taking in Deep Reinforcement Learning Agents

http://arxiv.org/abs/1907.01851

AUTHORS:        Aqeel Labash ; Jaan Aru ; Tambet Matiisen ; Ardi Tampuu ; Raul Vicente

HIGHLIGHT:      Here we present our progress toward building artificial agents with such abilities.



14, TITLE:      Consistent Multiple Sequence Decoding

http://arxiv.org/abs/2004.00760

AUTHORS:        Bicheng Xu ; Leonid Sigal

HIGHLIGHT:      In this paper, we introduce a consistent multiple 
sequence decoding architecture, which is while relatively simple, is 
general and allows for consistent and simultaneous decoding of an 
arbitrary number of sequences.



15, TITLE:      A Cyclically-Trained Adversarial Network for Invariant Representation Learning

http://arxiv.org/abs/1906.09313

AUTHORS:        Jiawei Chen ; Janusz Konrad ; Prakash Ishwar

HIGHLIGHT:      Specifically, we propose a cyclically-trained 
adversarial network to learn a mapping from image space to latent 
representation space and back such that the latent representation is 
invariant to a specified factor of variation (e.g., identity).



16, TITLE:      Beyond Notations: Hygienic Macro Expansion for Theorem Proving Languages

http://arxiv.org/abs/2001.10490

AUTHORS:        Sebastian Ullrich ; Leonardo de Moura

COMMENTS:       accepted to IJCAR 2020

HIGHLIGHT:      We take ideas from the Scheme family of programming 
languages and solve these two problems simultaneously by proposing a 
novel hygienic macro system custom-built for ITPs.



17, TITLE:      Self-Supervised Monocular Scene Flow Estimation

http://arxiv.org/abs/2004.04143

AUTHORS:        Junhwa Hur ; Stefan Roth

COMMENTS:       To appear at CVPR 2020 (Oral); a typo corrected in the reference section

HIGHLIGHT:      We propose a novel monocular scene flow method that yields competitive accuracy and real-time performance.



18, TITLE:      Pretrained Transformers Improve Out-of-Distribution Robustness

http://arxiv.org/abs/2004.06100

AUTHORS:        Dan Hendrycks ; Xiaoyuan Liu ; Eric Wallace ; Adam Dziedzic ; Rishabh Krishnan ; Dawn Song

COMMENTS:       ACL 2020

HIGHLIGHT:      We measure the generalization of previous models 
including bag-of-words models, ConvNets, and LSTMs, and we show that 
pretrained Transformers' performance declines are substantially smaller.



19, TITLE:      Generating Emotionally Aligned Responses in Dialogues using Affect Control Theory

http://arxiv.org/abs/2003.03645

AUTHORS:        Nabiha Asghar ; Ivan Kobyzev ; Jesse Hoey ; Pascal Poupart ; Muhammad Bilal Sheikh

HIGHLIGHT:      In this work, we bring Affect Control Theory (ACT), a 
socio-mathematical model of emotions for human-human interactions, to 
the neural dialogue generation setting.



20, TITLE:      Learning to Contextually Aggregate Multi-Source Supervision for Sequence Labeling

http://arxiv.org/abs/1910.04289

AUTHORS:        Ouyu Lan ; Xiao Huang ; Bill Yuchen Lin ; He Jiang ; Liyuan Liu ; Xiang Ren

COMMENTS:       Accepted to the ACL 2020, code: https://github.com/INK-USC/ConNet

HIGHLIGHT:      In this paper, we propose a novel framework Consensus 
Network (ConNet) that can be trained on annotations from multiple 
sources (e.g., crowd annotation, cross-domain data...).



21, TITLE:      Bootstrapping a Crosslingual Semantic Parser

http://arxiv.org/abs/2004.02585

AUTHORS:        Tom Sherborne ; Yumo Xu ; Mirella Lapata

COMMENTS:       Fixing a LaTeX error which swapped the results for DE/ZH

HIGHLIGHT:      In this work, we propose to adapt a semantic parser 
trained on a single language, such as English, to new languages and 
multiple domains with minimal annotation.



22, TITLE:      D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks

http://arxiv.org/abs/2004.04788

AUTHORS:        Bekir Z Demiray ; Muhammed Sit ; Ibrahim Demir

COMMENTS:       8 pages, 8 figures, 2 tables

HIGHLIGHT:      In this paper, a GAN based model is developed and 
evaluated, inspired by single image super-resolution methods, to 
increase the spatial resolution of a given DEM dataset up to 4 times 
without additional information related to data.



23, TITLE:      Balancing Training for Multilingual Neural Machine Translation

http://arxiv.org/abs/2004.06748

AUTHORS:        Xinyi Wang ; Yulia Tsvetkov ; Graham Neubig

COMMENTS:       Accepted at ACL 2020

HIGHLIGHT:      In this paper, we propose a method that instead 
automatically learns how to weight training data through a data scorer 
that is optimized to maximize performance on all test languages.



24, TITLE:      Motion-supervised Co-Part Segmentation

http://arxiv.org/abs/2004.03234

AUTHORS:        Aliaksandr Siarohin ; Subhankar Roy ; Stéphane Lathuilière ; Sergey Tulyakov ; Elisa Ricci ; Nicu Sebe

HIGHLIGHT:      To overcome this limitation, we propose a self-supervised deep learning method for co-part segmentation.



25, TITLE:      Contrastive Video Representation Learning via Adversarial Perturbations

http://arxiv.org/abs/1807.09380

AUTHORS:        Jue Wang ; Anoop Cherian

COMMENTS:       Revised version of ECCV 2018 Paper: Learning 
Discriminative Video Representations Using Adversarial Perturbations

HIGHLIGHT:      In this paper, we propose to use such perturbations 
within a novel contrastive learning setup to build negative samples, 
which are then used to produce improved video representations. Unlike 
the classic contrastive learning methods, we develop a binary 
classification problem that learns a set of discriminative hyperplanes 
-- as a subspace -- that will separate the two bags from each other.



26, TITLE:      Attention-based Multi-modal Fusion Network for Semantic Scene Completion

http://arxiv.org/abs/2003.13910

AUTHORS:        Siqi Li ; Changqing Zou ; Yipeng Li ; Xibin Zhao ; Yue Gao

COMMENTS:       Accepted by AAAI 2020

HIGHLIGHT:      This paper presents an end-to-end 3D convolutional 
network named attention-based multi-modal fusion network (AMFNet) for 
the semantic scene completion (SSC) task of inferring the occupancy and 
semantic labels of a volumetric 3D scene from single-view RGB-D images.



27, TITLE:      BLiMP: The Benchmark of Linguistic Minimal Pairs for English

http://arxiv.org/abs/1912.00582

AUTHORS:        Alex Warstadt ; Alicia Parrish ; Haokun Liu ; Anhad Mohananey ; Wei Peng ; Sheng-Fu Wang ; Samuel R. Bowman

COMMENTS:       To appear in TACL

HIGHLIGHT:      We introduce The Benchmark of Linguistic Minimal Pairs 
(shortened to BLiMP), a challenge set for evaluating what language 
models (LMs) know about major grammatical phenomena in English.



28, TITLE:      COSTRA 1.0: A Dataset of Complex Sentence Transformations

http://arxiv.org/abs/1912.01673

AUTHORS:        Petra Barancikova ; Ondrej Bojar

HIGHLIGHT:      We present COSTRA 1.0, a dataset of complex sentence transformations.



29, TITLE:      Universal Self-Attention Network for Graph Classification

http://arxiv.org/abs/1909.11855

AUTHORS:        Dai Quoc Nguyen ; Tu Dinh Nguyen ; Dinh Phung

COMMENTS:       Our updated implementation is available at: https://github.com/daiquocnguyen/U2GNN

HIGHLIGHT:      To this end, we present U2GNN -- a novel embedding model
 leveraging the strength of the transformer self-attention network -- to
 learn low-dimensional embeddings of graphs.



30, TITLE:      3D Morphable Face Models -- Past, Present and Future

http://arxiv.org/abs/1909.01815

AUTHORS:        Bernhard Egger ; William A. P. Smith ; Ayush Tewari ; 
Stefanie Wuhrer ; Michael Zollhoefer ; Thabo Beeler ; Florian Bernard ; 
Timo Bolkart ; Adam Kortylewski ; Sami Romdhani ; Christian Theobalt ; 
Volker Blanz ; Thomas Vetter

COMMENTS:       ACM Transactions on Graphics (TOG)

HIGHLIGHT:      In this paper, we provide a detailed survey of 3D 
Morphable Face Models over the 20 years since they were first proposed.



31, TITLE:      Cycle In Cycle Generative Adversarial Networks for Keypoint-Guided Image Generation

http://arxiv.org/abs/1908.00999

AUTHORS:        Hao Tang ; Dan Xu ; Gaowen Liu ; Wei Wang ; Nicu Sebe ; Yan Yan

COMMENTS:       9 pages, 8 figures, accepted to ACM MM 2019

HIGHLIGHT:      In this work, we propose a novel Cycle In Cycle 
Generative Adversarial Network (C$^2$GAN) for the task of 
keypoint-guided image generation.



32, TITLE:      Face Recognition: Too Bias, or Not Too Bias?

http://arxiv.org/abs/2002.06483

AUTHORS:        Joseph P Robinson ; Gennady Livitz ; Yann Henon ; Can Qin ; Yun Fu ; Samson Timoner

COMMENTS:       for associated image data, see https://forms.office.com/Pages/ResponsePage.aspx?id=DQSIkWdsW0yxEjajBLZtrQAAAAAAAAAAAAMAAMDJhXxUMElHQ0tVSDFSNDZTMVBPSVpXMkxJTkY4Ny4u

HIGHLIGHT:      We reveal critical insights into problems of bias in 
state-of-the-art facial recognition (FR) systems using a novel Balanced 
Faces In the Wild (BFW) dataset: data balanced for gender and ethnic 
groups.



33, TITLE:      Comprehensive Named Entity Recognition on CORD-19 with Distant or Weak Supervision

http://arxiv.org/abs/2003.12218

AUTHORS:        Xuan Wang ; Xiangchen Song ; Bangzheng Li ; Yingjun Guan ; Jiawei Han

HIGHLIGHT:      We created this CORD-NER dataset with comprehensive 
named entity recognition (NER) on the COVID-19 Open Research Dataset 
Challenge (CORD-19) corpus (2020-03-13).



34, TITLE:      Controlling Computation versus Quality for Neural Sequence Models

http://arxiv.org/abs/2002.07106

AUTHORS:        Ankur Bapna ; Naveen Arivazhagan ; Orhan Firat

HIGHLIGHT:      In this work, we utilize conditional computation to make
 neural sequence models (Transformer) more efficient and 
computation-aware during inference.



35, TITLE:      Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation

http://arxiv.org/abs/1911.03842

AUTHORS:        Emily Dinan ; Angela Fan ; Adina Williams ; Jack Urbanek ; Douwe Kiela ; Jason Weston

HIGHLIGHT:      We consider three techniques to mitigate gender bias: 
counterfactual data augmentation, targeted data collection, and bias 
controlled training.



36, TITLE:      Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding

http://arxiv.org/abs/1911.04910

AUTHORS:        Yun Tang ; Jing Huang ; Guangtao Wang ; Xiaodong He ; Bowen Zhou

COMMENTS:       Accepted by ACL 2020

HIGHLIGHT:      In this work, we propose a novel translational distance-based approach for knowledge graph link prediction.



37, TITLE:      The role of formalism in system requirements (full version)

http://arxiv.org/abs/1911.02564

AUTHORS:        Jean-Michel Bruel ; Sophie Ebersold ; Florian Galinier ; Alexandr Naumchev ; Manuel Mazzara ; Bertrand Meyer

COMMENTS:       Fourth version (15 April 2020). This is the full 
version, including some sections and 2 appendices not appearing in the 
short version

HIGHLIGHT:      It presents approaches in all of these categories, 
altogether 22 different ones, including for example SysML, Relax, 
Eiffel, Event-B, Alloy.



38, TITLE:      Revisiting Training Strategies and Generalization Performance in Deep Metric Learning

http://arxiv.org/abs/2002.08473

AUTHORS:        Karsten Roth ; Timo Milbich ; Samarth Sinha ; Prateek Gupta ; Björn Ommer ; Joseph Paul Cohen

HIGHLIGHT:      Exploiting these insights, we propose a simple, yet 
effective, training regularization to reliably boost the performance of 
ranking-based DML models on various standard benchmark datasets.



39, TITLE:      Hierarchical Graph Network for Multi-hop Question Answering

http://arxiv.org/abs/1911.03631

AUTHORS:        Yuwei Fang ; Siqi Sun ; Zhe Gan ; Rohit Pillai ; Shuohang Wang ; Jingjing Liu

HIGHLIGHT:      In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering.



40, TITLE:      Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

http://arxiv.org/abs/1907.12461

AUTHORS:        Sascha Rothe ; Shashi Narayan ; Aliaksei Severyn

COMMENTS:       To be published in Transactions of the Association for Computational Linguistics (TACL)

HIGHLIGHT:      In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation.



41, TITLE:      Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification

http://arxiv.org/abs/2003.02244

AUTHORS:        Hsin-Ping Huang ; Junyi Jessy Li

COMMENTS:       CoNLL 2019

HIGHLIGHT:      We present an unsupervised adversarial domain adaptive network equipped with a reconstruction component.



42, TITLE:      Low Rank Approximation with Entrywise $\ell_1$-Norm Error

http://arxiv.org/abs/1611.00898

AUTHORS:        Zhao Song ; David P. Woodruff ; Peilin Zhong

COMMENTS:       STOC 2017

HIGHLIGHT:      We study the $\ell_1$-low rank approximation problem, 
where for a given $n \times d$ matrix $A$ and approximation factor 
$\alpha \geq 1$, the goal is to output a rank-$k$ matrix $\widehat{A}$ 
for which $$\|A-\widehat{A}\|_1 \leq \alpha \cdot 
\min_{\textrm{rank-}k\textrm{ matrices}~A'}\|A-A'\|_1,$$ where for an $n
 \times d$ matrix $C$, we let $\|C\|_1 = \sum_{i=1}^n \sum_{j=1}^d 
|C_{i,j}|$.



43, TITLE:      Revisiting Metric Learning for Few-Shot Image Classification

http://arxiv.org/abs/1907.03123

AUTHORS:        Xiaomeng Li ; Lequan Yu ; Chi-Wing Fu ; Meng Fang ; Pheng-Ann Heng

COMMENTS:       Accept at Neurocomputing

HIGHLIGHT:      In this work, we present a simple yet powerful baseline 
for few-shot classification by emphasizing the importance of feature 
embedding.



44, TITLE:      How do Data Science Workers Collaborate? Roles, Workflows, and Tools

http://arxiv.org/abs/2001.06684

AUTHORS:        Amy X. Zhang ; Michael Muller ; Dakuo Wang

COMMENTS:       CSCW'2020

HIGHLIGHT:      In this work, we conducted an online survey with 183 participants who work in various aspects of data science.