2020.06.01.txt

==========New Papers==========
1, TITLE:       Controlling Length in Image Captioning
http://arxiv.org/abs/2005.14386
AUTHORS:        Ruotian Luo ; Greg Shakhnarovich
HIGHLIGHT:      We develop and evaluate captioning models that allow control of caption length.

2, TITLE:       Bayesian network structure learning with causal effects in the presence of latent variables
http://arxiv.org/abs/2005.14381
AUTHORS:        Kiattikun Chobtham ; Anthony C. Constantinou
HIGHLIGHT:      This paper describes a hybrid structure learning algorithm, called CCHM, which combines the constraint-based part of cFCI with hill-climbing score-based learning.

3, TITLE:       Privacy-Protection Drone Patrol System based on Face Anonymization
http://arxiv.org/abs/2005.14390
AUTHORS:        Harim Lee ; Myeung Un Kim ; Yeongjun Kim ; Hyeonsu Lyu ; Hyun Jong Yang
HIGHLIGHT:      To tackle the privacy infringement, this work proposes face-anonymizing drone patrol system.

4, TITLE:       Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models
http://arxiv.org/abs/2005.14709
AUTHORS:        Viktor Schlegel ; Goran Nenadic ; Riza Batista-Navarro
COMMENTS:       10 Pages
HIGHLIGHT:      This structured survey provides an overview of the evolving research area by categorising reported weaknesses in models and datasets and the methods proposed to reveal and alleviate those weaknesses for the English language.

5, TITLE:       The Importance of Suppressing Domain Style in Authorship Analysis
http://arxiv.org/abs/2005.14714
AUTHORS:        Sebastian Bischoff ; Niklas Deckers ; Marcel Schliebs ; Ben Thies ; Matthias Hagen ; Efstathios Stamatatos ; Benno Stein ; Martin Potthast
HIGHLIGHT:      We address this shortcoming for the first time in a novel experimental setup of fixed authors but swapped domains between training and testing.

6, TITLE:       Prosody leaks into the memories of words
http://arxiv.org/abs/2005.14716
AUTHORS:        Kevin Tang ; Jason A. Shaw
COMMENTS:       38 pages, 1 figure
HIGHLIGHT:      Other research has argued that predictability effects are tied to prosodic structure in integral ways.

7, TITLE:       PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
http://arxiv.org/abs/2005.14711
AUTHORS:        Ming Liang ; Bin Yang ; Wenyuan Zeng ; Yun Chen ; Rui Hu ; Sergio Casas ; Raquel Urtasun
COMMENTS:       CVPR2020
HIGHLIGHT:      Towards this goal we propose PnPNet, an end-to-end model that takes as input sequential sensor data, and outputs at each time step object tracks and their future trajectories.

8, TITLE:       A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers
http://arxiv.org/abs/2005.14501
AUTHORS:        Kevin Fauvel ; Véronique Masson ; Élisa Fromont
HIGHLIGHT:      Our research aims to propose a new performance-explainability analytical framework to assess and benchmark machine learning methods.

9, TITLE:       Unconstrained Matching of 2D and 3D Descriptors for 6-DOF Pose Estimation
http://arxiv.org/abs/2005.14502
AUTHORS:        Uzair Nadeem ; Mohammed Bennamoun ; Roberto Togneri ; Ferdous Sohel
HIGHLIGHT:      This paper proposes a novel concept to directly match feature descriptors extracted from 2D images with feature descriptors extracted from 3D point clouds. We generate a dataset of matching 2D and 3D points and their corresponding feature descriptors, which is used to learn a Descriptor-Matcher classifier.

10, TITLE:      NuClick: A Deep Learning Framework for Interactive Segmentation of Microscopy Images
http://arxiv.org/abs/2005.14511
AUTHORS:        Navid Alemi Koohbanani ; Mostafa Jahanifar ; Neda Zamani Tajadin ; Nasir Rajpoot
HIGHLIGHT:      As nuclei, cells and glands are fundamental objects for downstream analysis in histology, in this paper we propose a simple CNN-based approach to speed up collecting segmentation annotation for these objects by utilizing minimum input from an annotator.

11, TITLE:      Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning
http://arxiv.org/abs/2005.14310
AUTHORS:        Zhibo Yang ; Lihan Huang ; Yupei Chen ; Zijun Wei ; Seoyoung Ahn ; Gregory Zelinsky ; Dimitris Samaras ; Minh Hoai
COMMENTS:       16 pages, 13 figures, CVPR 2020
HIGHLIGHT:      We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search.

12, TITLE:      Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy Detection
http://arxiv.org/abs/2005.14308
AUTHORS:        Muhammad Naseer Bajwa ; Yoshinobu Taniguchi ; Muhammad Imran Malik ; Wolfgang Neumeier ; Andreas Dengel ; Sheraz Ahmed
COMMENTS:       Pages 12, Figures 5
HIGHLIGHT:      Therefore, getting inspiration from ophthalmologist, we propose to combine coarse-grained classifiers that detect discriminating features from the whole images, with a recent breed of fine-grained classifiers that discover and pay particular attention to pathologically significant regions.

13, TITLE:      Monocular Depth Estimators: Vulnerabilities and Attacks
http://arxiv.org/abs/2005.14302
AUTHORS:        Alwyn Mathew ; Aditya Prakash Patra ; Jimson Mathew
HIGHLIGHT:      In this paper, we investigate the robustness of the most state-of-the-art monocular depth estimation networks against adversarial attacks.

14, TITLE:      Probabilistic Object Classification using CNN ML-MAP layers
http://arxiv.org/abs/2005.14565
AUTHORS:        G. Melotti ; C. Premebida ; J. J. Bird ; D. R. Faria ; N. Gonçalves
COMMENTS:       18 figures, submitted to ECCV'20 WS (PAD: Perception for Autonomous Driving)
HIGHLIGHT:      To reduce the overconfidence without compromising the classification performance, we introduce a CNN probabilistic approach based on distributions calculated in the network's Logit layer.

15, TITLE:      Self-Attention Dense Depth Estimation Network for Unrectified Video Sequences
http://arxiv.org/abs/2005.14313
AUTHORS:        Alwyn Mathew ; Aditya Prakash Patra ; Jimson Mathew
HIGHLIGHT:      We propose a self-attention based depth and ego-motion network for unrectified images.

16, TITLE:      On Incorporating Structural Information to improve Dialogue Response Generation
http://arxiv.org/abs/2005.14315
AUTHORS:        Nikita Moghe ; Priyesh Vijayan ; Balaraman Ravindran ; Mitesh M. Khapra
HIGHLIGHT:      We propose a new architecture that uses the ability of BERT to capture deep contextualized representations in conjunction with explicit structure and sequence information.

17, TITLE:      Harbsafe-162. A Domain-Specific Data Set for the Intrinsic Evaluation of Semantic Representations for Terminological Data
http://arxiv.org/abs/2005.14576
AUTHORS:        Susanne Arndt ; Dieter Schnäpp
HIGHLIGHT:      One objective of the project is to apply distributional semantic models to terminological entries, that is, complex lexical data comprising of at least one or several terms, term phrases and a definition.

18, TITLE:      Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images
http://arxiv.org/abs/2005.14330
AUTHORS:        Abdullah-Al-Zubaer Imran ; Chao Huang ; Hui Tang ; Wei Fan ; Kenneth M. C. Cheung ; Michael To ; Zhen Qian ; Demetri Terzopoulos
COMMENTS:       Presented at Med-NeurIPS 2019
HIGHLIGHT:      To guide a CNN in the learning of spinal shape while detecting landmarks in X-ray images, we propose a novel loss based on a bipartite distance (BPD) measure, and show that it consistently improves landmark detection performance.

19, TITLE:      On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition
http://arxiv.org/abs/2005.14327
AUTHORS:        Jinyu Li ; Yu Wu ; Yashesh Gaur ; Chengyi Wang ; Rui Zhao ; Shujie Liu
COMMENTS:       submitted to Interspeech 2020
HIGHLIGHT:      In this study, we conduct an empirical comparison of RNN-T, RNN-AED, and Transformer-AED models, in both non-streaming and streaming modes.

20, TITLE:      Improving Unsupervised Sparsespeech Acoustic Models with Categorical Reparameterization
http://arxiv.org/abs/2005.14578
AUTHORS:        Benjamin Milde ; Chris Biemann
HIGHLIGHT:      We extend the Sparsespeech model to allow for sampling over a random discrete variable, yielding pseudo-posteriorgrams.

21, TITLE:      UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
http://arxiv.org/abs/2005.14354
AUTHORS:        Zhengzhong Tu ; Yilin Wang ; Neil Birkbeck ; Balu Adsumilli ; Alan C. Bovik
COMMENTS:       13 pages, 11 figures, 11 tables
HIGHLIGHT:      Here we contribute to advancing the UGC-VQA problem by conducting a comprehensive evaluation of leading no-reference/blind VQA (BVQA) features and models on a fixed evaluation architecture, yielding new empirical insights on both subjective video quality studies and VQA model design.

22, TITLE:      Enhancing Foreground Boundaries for Medical Image Segmentation
http://arxiv.org/abs/2005.14355
AUTHORS:        Dong Yang ; Holger Roth ; Xiaosong Wang ; Ziyue Xu ; Andriy Myronenko ; Daguang Xu
HIGHLIGHT:      To further improve the segmentation quality of boundary areas, we propose a boundary enhancement loss to enforce additional constraints on optimizing machine learning models.

23, TITLE:      Extracting low-dimensional psychological representations from convolutional neural networks
http://arxiv.org/abs/2005.14363
AUTHORS:        Aditi Jha ; Joshua Peterson ; Thomas L. Griffiths
COMMENTS:       Accepted to CogSci 2020
HIGHLIGHT:      Here we present a method for reducing these representations to a low-dimensional space which is still predictive of similarity judgments.

24, TITLE:      A Light-Weighted Convolutional Neural Network for Bitemporal SAR Image Change Detection
http://arxiv.org/abs/2005.14376
AUTHORS:        Rongfang Wang ; Fan Ding ; Licheng Jiao ; Jia-Wei Chen ; Bo Liu ; Wenping Ma ; Mi Wang
HIGHLIGHT:      Motivated by this, in this paper, we propose a lightweight neural network to reduce the computational and spatial complexity and facilitate the change detection on an edge device.

25, TITLE:      Uncertainty Evaluation Metric for Brain Tumour Segmentation
http://arxiv.org/abs/2005.14262
AUTHORS:        Raghav Mehta ; Angelos Filos ; Yarin Gal ; Tal Arbel
HIGHLIGHT:      In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in the BraTS 2019 sub-challenge on uncertainty quantification.

26, TITLE:      LR-CNN: Local-aware Region CNN for Vehicle Detection in Aerial Imagery
http://arxiv.org/abs/2005.14264
AUTHORS:        Wentong Liao ; Xiang Chen ; Jingfeng Yang ; Stefan Roth ; Michael Goesele ; Michael Ying Yang ; Bodo Rosenhahn
COMMENTS:       8 pages
HIGHLIGHT:      We present the Local-aware Region Convolutional Neural Network (LR-CNN), a novel two-stage approach for vehicle detection in aerial imagery.

27, TITLE:      Overview: Computer vision and machine learning for microstructural characterization and analysis
http://arxiv.org/abs/2005.14260
AUTHORS:        Elizabeth A. Holm ; Ryan Cohn ; Nan Gao ; Andrew R. Kitahara ; Thomas P. Matson ; Bo Lei ; Srujana Rao Yarasi
COMMENTS:       submitted to Materials and Metallurgical Transactions A
HIGHLIGHT:      These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.

28, TITLE:      Intelligent Residential Energy Management System using Deep Reinforcement Learning
http://arxiv.org/abs/2005.14259
AUTHORS:        Alwyn Mathew ; Abhijit Roy ; Jimson Mathew
HIGHLIGHT:      This paper proposes a Deep Reinforcement Learning (DRL) model for demand response where the virtual agent learns the task like humans do.

29, TITLE:      ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification
http://arxiv.org/abs/2005.14288
AUTHORS:        Naoto Usuyama ; Natalia Larios Delgado ; Amanda K. Hall ; Jessica Lundin
COMMENTS:       CVPR 2020 VL3. Project Page: https://github.com/usuyama/ePillID-benchmark
HIGHLIGHT:      In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 8184 appearance classes (two sides for 4092 pill types).

30, TITLE:      Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning
http://arxiv.org/abs/2005.14284
AUTHORS:        Muhammad Naseer Bajwa ; Muhammad Imran Malik ; Shoaib Ahmed Siddiqui ; Andreas Dengel ; Faisal Shafait ; Wolfgang Neumeier ; Sheraz Ahmed
COMMENTS:       16 Pages, 10 Figures
HIGHLIGHT:      Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous.

31, TITLE:      What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP
http://arxiv.org/abs/2005.14299
AUTHORS:        Oskar Wysocki ; Malina Florea ; Andre Freitas
COMMENTS:       12 pages, 6 figures
HIGHLIGHT:      By understanding the distribution of task types, metrics, architectures, participation and citations over time we aim to answer the question on what is being evaluated by SemEval.

32, TITLE:      Fixed-size Objects Encoding for Visual Relationship Detection
http://arxiv.org/abs/2005.14600
AUTHORS:        Hengyue Pan ; Xin Niu ; Rongchun Li ; Siqi Shen ; Yong Dou
COMMENTS:       14 pages
HIGHLIGHT:      In this paper, we propose a fixed-size object encoding method (FOE-VRD) to improve performance of visual relationship detection tasks.

33, TITLE:      Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization
http://arxiv.org/abs/2005.14601
AUTHORS:        Jingfan Chen ; Guanghui Zhu ; Rong Gu ; Chunfeng Yuan ; Yihua Huang
HIGHLIGHT:      To alleviate this problem, we propose a novel Bayesian optimization framework (termed SILBO), which finds a low-dimensional space to perform Bayesian optimization iteratively through semi-supervised dimension reduction.

34, TITLE:      Using Large Pretrained Language Models for Answering User Queries from Product Specifications
http://arxiv.org/abs/2005.14613
AUTHORS:        Kalyani Roy ; Smit Shah ; Nithish Pai ; Jaidam Ramtej ; Prajit Prashant Nadkarn ; Jyotirmoy Banerjee ; Pawan Goyal ; Surender Kumar
COMMENTS:       5 pages
HIGHLIGHT:      We propose an approach to automatically create a training dataset for this problem.

35, TITLE:      Detection of Bangla Fake News using MNB and SVM Classifier
http://arxiv.org/abs/2005.14627
AUTHORS:        Md Gulzar Hussain ; Md Rashidul Hasan ; Mahmuda Rahman ; Joy Protim ; Sakib Al Hasan
HIGHLIGHT:      In this research work, we have used two supervised machine learning algorithms, Multinomial Naive Bayes (MNB) and Support Vector Machine (SVM) classifiers to detect Bangla fake news with CountVectorizer and Term Frequency - Inverse Document Frequency Vectorizer as feature extraction.

36, TITLE:      Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
http://arxiv.org/abs/2005.14400
AUTHORS:        Jin-Fan Hu ; Ting-Zhu Huang ; Liang-Jian Deng ; Tai-Xiang Jiang ; Gemine Vivone ; Jocelyn Chanussot
HIGHLIGHT:      In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI).

37, TITLE:      Federated Face Anti-spoofing
http://arxiv.org/abs/2005.14638
AUTHORS:        Rui Shao ; Pramuditha Perera ; Pong C. Yuen ; Vishal M. Patel
HIGHLIGHT:      In this paper, with the motivation of circumventing this challenge, we propose Federated Face Anti-spoofing (FedFAS) framework.

38, TITLE:      WhylSon: Proving your Michelson Smart Contracts in Why3
http://arxiv.org/abs/2005.14650
AUTHORS:        Luís Pedro Arrojado da Horta ; João Santos Reis ; Mário Pereira ; Simão Melo de Sousa
HIGHLIGHT:      This paper introduces WhylSon, a deductive verification tool for smart contracts written in Michelson, which is the low-level language of the Tezos blockchain.

39, TITLE:      AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments
http://arxiv.org/abs/2005.14410
AUTHORS:        Lucia Schuler ; Somaya Jamil ; Niklas Kühl
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      As we show in a baseline experiment, this predefined concurrency level can strongly influence the performance of a serverless application.

40, TITLE:      Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
http://arxiv.org/abs/2005.14405
AUTHORS:        Komal Chugh ; Parul Gupta ; Abhinav Dhall ; Ramanathan Subramanian
HIGHLIGHT:      We propose detection of deepfake videos based on the dissimilarity between the audio and visual modalities, termed as the Modality Dissonance Score (MDS).

41, TITLE:      Noise-robust Named Entity Understanding for Virtual Assistants
http://arxiv.org/abs/2005.14408
AUTHORS:        Deepak Muralidharan ; Joel Ruben Antony Moniz ; Sida Gao ; Xiao Yang ; Lin Li ; Justine Kao ; Stephen Pulman ; Atish Kothari ; Ray Shen ; Yinying Pan ; Vivek Kaul ; Mubarak Seyed Ibrahim ; Gang Xiang ; Nan Dun ; Yidan Zhou ; Andy O ; Yuan Zhang ; Pooja Chitkara Xuan Wang ; Alkesh Patel ; Kushal Tayal ; Roger Zheng ; Peter Grasch ; Jason Williams
COMMENTS:       9 pages
HIGHLIGHT:      In this paper, we propose an architecture with novel features that jointly solves the recognition of named entities (a.k.a. Named Entity Recognition, or NER) and the resolution to their canonical forms (a.k.a. Entity Linking, or EL).

42, TITLE:      Deep graph learning for semi-supervised classification
http://arxiv.org/abs/2005.14403
AUTHORS:        Guangfeng Lin ; Xiaobing Kang ; Kaiyang Liao ; Fan Zhao ; Yajun Chen
HIGHLIGHT:      Existing methods mostly combine the computational layer and the related losses into GCN for exploring the global graph(measuring graph structure from all data samples) or local graph (measuring graph structure from local data samples).

43, TITLE:      SLAM-Inspired Simultaneous Contextualization and Interpreting for Incremental Conversation Sentences
http://arxiv.org/abs/2005.14662
AUTHORS:        Yusuke Takimoto ; Yosuke Fukuchi ; Shoya Matsumori ; Michita Imai
HIGHLIGHT:      Hence, to dynamically estimate the conversation context and interpretations of polysemous words in sequential sentences, we propose a method of Simultaneous Contextualization And INterpreting (SCAIN) based on the traditional Simultaneous Localization And Mapping (SLAM) algorithm. For experimental evaluation, we created two datasets: one from Wikipedia's disambiguation pages and the other from real conversations.

44, TITLE:      First Neural Conjecturing Datasets and Experiments
http://arxiv.org/abs/2005.14664
AUTHORS:        Josef Urban ; Jan Jakubův
COMMENTS:       Accepted to CICM 2020
HIGHLIGHT:      We describe several datasets and first experiments with creating conjectures by neural methods.

45, TITLE:      High-order structure preserving graph neural network for few-shot learning
http://arxiv.org/abs/2005.14415
AUTHORS:        Guangfeng Lin ; Ying Yang ; Yindi Fan ; Xiaobing Kang ; Kaiyang Liao ; Fan Zhao
HIGHLIGHT:      Few-shot learning can find the latent structure information between the prior knowledge and the queried data by the similarity metric of meta-learning to construct the discriminative model for recognizing the new categories with the rare labeled samples.

46, TITLE:      Massive Choice, Ample Tasks (MaChAmp):A Toolkit for Multi-task Learning in NLP
http://arxiv.org/abs/2005.14672
AUTHORS:        Rob van der Goot ; Ahmet Üstün ; Alan Ramponi ; Barbara Plank
HIGHLIGHT:      In this paper we present MaChAmp, a toolkit for easy use of fine-tuning BERT-like models in multi-task settings.

47, TITLE:      SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions
http://arxiv.org/abs/2005.14424
AUTHORS:        Mao Ye ; Chengyue Gong ; Qiang Liu
COMMENTS:       ACL 2020
HIGHLIGHT:      In this work, we propose a certified robust method based on a new randomized smoothing technique, which constructs a stochastic ensemble by applying random word substitutions on the input sentences, and leverage the statistical properties of the ensemble to provably certify the robustness.

48, TITLE:      Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
http://arxiv.org/abs/2005.14684
AUTHORS:        Fei Shen ; Jianqing Zhu ; Xiaobin Zhu ; Yi Xie ; Jingchang Huang
HIGHLIGHT:      Existing vehicle re-identification methods commonly use spatial pooling operations to aggregate feature maps extracted via off-the-shelf backbone networks.

49, TITLE:      SNR-based teachers-student technique for speech enhancement
http://arxiv.org/abs/2005.14441
AUTHORS:        Xiang Hao ; Xiangdong Su ; Zhiyu Wang ; Qiang Zhang ; Huali Xu ; Guanglai Gao
COMMENTS:       Accepted to 2020 IEEE International Conference on Multimedia and Expo (ICME 2020)
HIGHLIGHT:      In this paper, we propose a method that integrates an SNR-based teachers-student technique and time-domain U-Net to deal with this problem. To evaluate the proposed method, we constructed a dataset with an SNR ranging from -20dB to 20dB based on the public dataset.

50, TITLE:      Dynamic Routing with Path Diversity and Consistency for Compact Network Learning
http://arxiv.org/abs/2005.14439
AUTHORS:        Huanyu Wang ; Zequn Qin ; Xi Li
HIGHLIGHT:      In this paper, we propose a novel dynamic routing inference method with diversity and consistency that better takes advantage of the network capacity.

51, TITLE:      Sub-band Knowledge Distillation Framework for Speech Enhancement
http://arxiv.org/abs/2005.14435
AUTHORS:        Xiang Hao ; Shixue Wen ; Xiangdong Su ; Yun Liu ; Guanglai Gao ; Xiaofei Li
HIGHLIGHT:      In this paper, we explore a knowledge distillation framework based on sub-band spectral mapping for single-channel speech enhancement.

52, TITLE:      Improving Community Resiliency and Emergency Response With Artificial Intelligence
http://arxiv.org/abs/2005.14212
AUTHORS:        Ben Ortiz ; Laura Kahn ; Marc Bosch ; Philip Bogden ; Viveca Pavon-Harr ; Onur Savas ; Ian McCulloh
HIGHLIGHT:      Improving Community Resiliency and Emergency Response With Artificial Intelligence

53, TITLE:      Investigating Deep Learning Approaches for Hate Speech Detection in Social Media
http://arxiv.org/abs/2005.14690
AUTHORS:        Prashant Kapil ; Asif Ekbal ; Dipankar Das
COMMENTS:       12 pages, 2 figures, 8 tables. Accepted in CICLing: International Conference on Computational Linguistics and Intelligent Text Processing, 2019. Modified after reviewer comments
HIGHLIGHT:      In this paper, we proposed deep learning approaches utilizing various embeddings for detecting various types of hate speeches in social media.

54, TITLE:      HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
http://arxiv.org/abs/2005.14446
AUTHORS:        Zhaohui Yang ; Yunhe Wang ; Dacheng Tao ; Xinghao Chen ; Jianyuan Guo ; Chunjing Xu ; Chao Xu ; Chang Xu
HIGHLIGHT:      We propose an hourglass-inspired approach (HourNAS) for this problem that is motivated by the fact that the effects of the architecture often proceed from the vital few blocks.

55, TITLE:      Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
http://arxiv.org/abs/2005.14464
AUTHORS:        Xiaoya Li ; Mingxin Zhou ; Jiawei Wu ; Arianna Yuan ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we perform a comprehensive analysis on the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020.

56, TITLE:      Empathic AI Painter: A Computational Creativity System with Embodied Conversational Interaction
http://arxiv.org/abs/2005.14223
AUTHORS:         Ozge Nilay Yalcin ; Nouf Abukhodair ; Steve DiPaola
COMMENTS:       In press. NeurIPS 2019 Competition and Demonstration Track, Proceedings of Machine Learning Research Vol. 123, 2020
HIGHLIGHT:      This paper documents our attempt to computationally model the creative process of a portrait painter, who relies on understanding human traits (i.e., personality and emotions) to inform their art.

57, TITLE:      WaveSNet: Wavelet Integrated Deep Networks for Image Segmentation
http://arxiv.org/abs/2005.14461
AUTHORS:        Qiufu Li ; Linlin Shen
COMMENTS:       7 pages, 7 figures
HIGHLIGHT:      In this paper, we propose to apply Discrete Wavelet Transform (DWT) to extract the data details during feature map down-sampling, and adopt Inverse DWT (IDWT) with the extracted details during the up-sampling to recover the details.

58, TITLE:      Depth-aware Blending of Smoothed Images for Bokeh Effect Generation
http://arxiv.org/abs/2005.14214
AUTHORS:        Saikat Dutta
HIGHLIGHT:      In this paper, an end-to-end deep learning framework is proposed to generate high-quality bokeh effect from images.

59, TITLE:      Algorithm Selection Framework for Cyber Attack Detection
http://arxiv.org/abs/2005.14230
AUTHORS:        Marc Chalé ; Nathaniel D. Bastian ; Jeffery Weir
COMMENTS:       6 pages, 7 figures, 1 table, accepted to WiseML '20
HIGHLIGHT:      In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented.

60, TITLE:      FCN+RL: A Fully Convolutional Network followed by Refinement Layers to Offline Handwritten Signature Segmentation
http://arxiv.org/abs/2005.14229
AUTHORS:        Celso A. M. Lopes Junior ; Matheus Henrique M. da Silva ; Byron Leite Dantas Bezerra ; Bruno Jose Torres Fernandes ; Donato Impedovo
COMMENTS:       7 pages, 6 figures, Accepted at IJCNN 2020: International Joint Conference on Neural Networks
HIGHLIGHT:      In this work, we propose an approach to locate and extract the pixels of handwritten signatures on identification documents, without any prior information on the location of the signatures.

61, TITLE:      Weakly Supervised Lesion Localization With Probabilistic-CAM Pooling
http://arxiv.org/abs/2005.14480
AUTHORS:        Wenwu Ye ; Jin Yao ; Hui Xue ; Yi Li
HIGHLIGHT:      We present Probabilistic Class Activation Map (PCAM) pooling, a novel global pooling operation for lesion localization with only image-level supervision.

62, TITLE:      Fuzziness-based Spatial-Spectral Class Discriminant Information Preserving Active Learning for Hyperspectral Image Classification
http://arxiv.org/abs/2005.14236
AUTHORS:        Muhammad Ahmad
COMMENTS:       13 pages, 7 figures
HIGHLIGHT:      Therefore, this work proposes a novel fuzziness-based spatial-spectral within and between for both local and global class discriminant information preserving (FLG) method.

63, TITLE:      Human Recognition Using Face in Computed Tomography
http://arxiv.org/abs/2005.14238
AUTHORS:        Jiuwen Zhu ; Hu Han ; S. Kevin Zhou
HIGHLIGHT:      In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features.

64, TITLE:      Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking
http://arxiv.org/abs/2005.14253
AUTHORS:        Thibault Févry ; Nicholas FitzGerald ; Livio Baldini Soares ; Tom Kwiatkowski
COMMENTS:       11 pages, 8 figures, appearing at AKBC 2020
HIGHLIGHT:      In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links.

65, TITLE:      Neural Simultaneous Speech Translation Using Alignment-Based Chunking
http://arxiv.org/abs/2005.14489
AUTHORS:        Patrick Wilken ; Tamer Alkhouli ; Evgeny Matusov ; Pavel Golik
COMMENTS:       IWSLT 2020
HIGHLIGHT:      We propose a neural machine translation (NMT) model that makes dynamic decisions when to continue feeding on input or generate output words.

66, TITLE:      Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
http://arxiv.org/abs/2005.14247
AUTHORS:        Yaël Balbastre ; Mikael Brudfors ; Michela Azzarito ; Christian Lambert ; Martina F. Callaghan ; John Ashburner
COMMENTS:       11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI 2020
HIGHLIGHT:      In this paper, we extend this model in two ways: (1) by introducing a joint total variation (JTV) prior on the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a posteriori} estimate.



==========Updates to Previous Papers==========
1, TITLE:       CA-EHN: Commonsense Analogy from E-HowNet
http://arxiv.org/abs/1908.07218
AUTHORS:        Peng-Hsuan Li ; Tsan-Yu Yang ; Wei-Yun Ma
COMMENTS:       In proceedings of LREC 2020
HIGHLIGHT:      In this work, we model commonsense knowledge down to word-level analogical reasoning by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations.

2, TITLE:       FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval
http://arxiv.org/abs/2005.09801
AUTHORS:        Dehong Gao ; Linbo Jin ; Ben Chen ; Minghui Qiu ; Peng Li ; Yi Wei ; Yi Hu ; Hao Wang
COMMENTS:       10 pages, to be published in SIGIR20 Industry Track
HIGHLIGHT:      In this paper, we address the text and image matching in cross-modal retrieval of the fashion industry.

3, TITLE:       Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods
http://arxiv.org/abs/1908.10623
AUTHORS:        Fasih Haider ; Senja Pollak ; Pierre Albert ; Saturnino Luz
HIGHLIGHT:      In this study, we evaluate three different state-of-the-art feature selection methods: Infinite Latent Feature Selection (ILFS), ReliefF and Fisher (generalized Fisher score), and compare them to our recently proposed feature selection method named `Active Feature Selection' (AFS).

4, TITLE:       Exploration of Input Patterns for Enhancing the Performance of Liquid State Machines
http://arxiv.org/abs/2004.02540
AUTHORS:        Shasha Guo ; Lianhua Qu ; Lei Wang ; Shuo Tian ; Shiming Li ; Weixia Xu
HIGHLIGHT:      We explore the influence of input scale reduction on LSM instead.

5, TITLE:       FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline
http://arxiv.org/abs/1910.03866
AUTHORS:        Leonie Henschel ; Sailesh Conjeti ; Santiago Estrada ; Kersten Diers ; Bruce Fischl ; Martin Reuter
COMMENTS:       Submitted to NeuroImage
HIGHLIGHT:      In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer's anatomical segmentation including surface reconstruction and cortical parcellation.

6, TITLE:       Automated Inline Analysis of Myocardial Perfusion MRI with Deep Learning
http://arxiv.org/abs/1911.00625
AUTHORS:        Hui Xue ; Rhodri Davies ; Louis AE Brown ; Kristopher D Knott ; Tushar Kotecha ; Marianna Fontana ; Sven Plein ; James C Moon ; Peter Kellman
COMMENTS:       This work has been submitted to Radiology: Artificial Intelligence for possible publication
HIGHLIGHT:      This paper proposes a deep neural network based computational workflow for inline myocardial perfusion analysis.

7, TITLE:       Learning to grow: control of material self-assembly using evolutionary reinforcement learning
http://arxiv.org/abs/1912.08333
AUTHORS:        Stephen Whitelam ; Isaac Tamblyn
HIGHLIGHT:      We show that neural networks trained by evolutionary reinforcement learning can enact efficient molecular self-assembly protocols.

8, 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.

9, TITLE:       $P\neq NP$
http://arxiv.org/abs/2003.09791
AUTHORS:        Tianrong Lin
COMMENTS:       v9: the new added footnote 6 is to fill a logic gap (v8: Proof of Lemma 5.1 in Case 2 further changed) comments are welcome. arXiv admin note: text overlap with arXiv:1305.4029 by other authors. (The author note: no text overlap with arXiv:1305.4029, see references [2] and [4], but text overlap with refs [2] and [4], the author has never read arXiv:1305.4029)
HIGHLIGHT:      The main contribution of the paper is that a series of results are obtained.

10, TITLE:      AutoGrow: Automatic Layer Growing in Deep Convolutional Networks
http://arxiv.org/abs/1906.02909
AUTHORS:        Wei Wen ; Feng Yan ; Yiran Chen ; Hai Li
COMMENTS:       KDD 2020
HIGHLIGHT:      We propose AutoGrow to automate depth discovery in DNNs: starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy; otherwise, stops growing and thus discovers the depth.

11, TITLE:      Predicting Declension Class from Form and Meaning
http://arxiv.org/abs/2005.00626
AUTHORS:        Adina Williams ; Tiago Pimentel ; Arya D. McCarthy ; Hagen Blix ; Eleanor Chodroff ; Ryan Cotterell
COMMENTS:       14 pages, 2 figures, the is the camera-ready version accepted at the 2020 Annual Conference of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      Here, we investigate the strength of those clues.

12, TITLE:      Vispi: Automatic Visual Perception and Interpretation of Chest X-rays
http://arxiv.org/abs/1906.05190
AUTHORS:        Xin Li ; Rui Cao ; Dongxiao Zhu
COMMENTS:       In the proceeding of Medical Imaging with Deep Learning (MIDL-20)
HIGHLIGHT:      To tackle these challenges, we present Vispi, an automatic medical image interpretation system, which first annotates an image via classifying and localizing common thoracic diseases with visual support and then followed by report generation from an attentive LSTM model.

13, TITLE:      Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis
http://arxiv.org/abs/2005.10434
AUTHORS:        Yu Song ; Zilong Huang ; Chuanyue Shen ; Humphrey Shi ; David A Lange
COMMENTS:       Accepted as a journal publication by Cement & Concrete Research
HIGHLIGHT:      In this study, we investigated the feasibility of using CNN to conduct concrete segmentation without the use of color treatment.

14, TITLE:      Learning from Noisy Anchors for One-stage Object Detection
http://arxiv.org/abs/1912.05086
AUTHORS:        Hengduo Li ; Zuxuan Wu ; Chen Zhu ; Caiming Xiong ; Richard Socher ; Larry S. Davis
COMMENTS:       CVPR 2020 camera ready
HIGHLIGHT:      In this paper, we propose to mitigate noise incurred by imperfect label assignment such that the contributions of anchors are dynamically determined by a carefully constructed cleanliness score associated with each anchor.

15, TITLE:      Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
http://arxiv.org/abs/1805.07509
AUTHORS:        Jichao Zhang ; Yezhi Shu ; Songhua Xu ; Gongze Cao ; Fan Zhong ; Meng Liu ; Xueying Qin
COMMENTS:       Accepted by ACMMM2018
HIGHLIGHT:      To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.

16, TITLE:      Human-like general language processing
http://arxiv.org/abs/2005.09175
AUTHORS:        Feng Qi ; Guanjun Jiang
HIGHLIGHT:      To let machines understand, learn, and use language flexibly, we propose a human-like general language processing (HGLP) architecture, which contains sensorimotor, association, and cognitive systems.

17, TITLE:      GECToR -- Grammatical Error Correction: Tag, Not Rewrite
http://arxiv.org/abs/2005.12592
AUTHORS:        Kostiantyn Omelianchuk ; Vitaliy Atrasevych ; Artem Chernodub ; Oleksandr Skurzhanskyi
COMMENTS:       Accepted for publication in BEA workshop (15th Workshop on Innovative Use of NLP for Building Educational Applications; co-located with ACL)
HIGHLIGHT:      In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder.

18, TITLE:      Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
http://arxiv.org/abs/1912.07538
AUTHORS:        Vedika Agarwal ; Rakshith Shetty ; Mario Fritz
COMMENTS:       16 pages
HIGHLIGHT:      In this paper, we propose a novel way to analyze and measure the robustness of the state of the art models w.r.t semantic visual variations as well as propose ways to make models more robust against spurious correlations.

19, TITLE:      Feature-Based Diversity Optimization for Problem Instance Classification
http://arxiv.org/abs/1510.08568
AUTHORS:        Wanru Gao ; Samadhi Nallaperuma ; Frank Neumann
COMMENTS:       20 pages, 18 figures
HIGHLIGHT:      In this paper, we present a general framework that is able to construct a diverse set of instances that are hard or easy for a given search heuristic.

20, TITLE:      Poly-YOLO: higher speed, more precise detection and instance segmentation for YOLOv3
http://arxiv.org/abs/2005.13243
AUTHORS:        Petr Hurtik ; Vojtech Molek ; Jan Hula ; Marek Vajgl ; Pavel Vlasanek ; Tomas Nejezchleba
COMMENTS:       18 pages, 15 figures, submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (under review), Source code is available at https://gitlab.com/irafm-ai/poly-yolo
HIGHLIGHT:      We present a new version of YOLO with better performance and extended with instance segmentation called Poly-YOLO.

21, TITLE:      The surprising little effectiveness of cooperative algorithms in parallel problem solving
http://arxiv.org/abs/1912.03347
AUTHORS:        Sandro M. Reia ; Larissa F. Aquino ; José F. Fontanari
HIGHLIGHT:      Here we study the performances of a cultural-inspired algorithm -- the imitative learning search -- as well as of asexual and sexual variants of evolutionary algorithms in finding the global maxima of NK-fitness landscapes.

22, TITLE:      Cats climb entails mammals move: preserving hyponymy in compositional distributional semantics
http://arxiv.org/abs/2005.14134
AUTHORS:        Gemma De las Cuevas ; Andreas Klingler ; Martha Lewis ; Tim Netzer
COMMENTS:       Submitted to SemSpace 2020
HIGHLIGHT:      In this paper, we introduce a generic way of composing the psd matrices corresponding to words.

23, TITLE:      Prediction and Description of Near-Future Activities in Video
http://arxiv.org/abs/1908.00943
AUTHORS:        Tahmida Mahmud ; Mohammad Billah ; Mahmudul Hasan ; Amit K. Roy-Chowdhury
COMMENTS:       14 pages, 4 figures, 14 tables
HIGHLIGHT:      In this work, we propose a system that can infer the labels and the captions of a sequence of future activities.

24, TITLE:      A Normalized Fully Convolutional Approach to Head and Neck Cancer Outcome Prediction
http://arxiv.org/abs/2005.14017
AUTHORS:        William Le ; Francisco Perdigón Romero ; Samuel Kadoury
COMMENTS:       6 pages, 1 figure, 1 table, Medical Imaging with Deep Learning 2020 conference
HIGHLIGHT:      In this work, we apply a CNN classification network augmented with a FCN preprocessor sub-network to a public TCIA head and neck cancer dataset.

25, TITLE:      Evolution of Image Segmentation using Deep Convolutional Neural Network: A Survey
http://arxiv.org/abs/2001.04074
AUTHORS:        Farhana Sultana ; Abu Sufian ; Paramartha Dutta
COMMENTS:       38 pages, 29 figures, 8 tables
HIGHLIGHT:      In this survey, we are going to take a glance at the evolution of both semantic and instance segmentation work based on CNN.

26, TITLE:      Language (Technology) is Power: A Critical Survey of "Bias" in NLP
http://arxiv.org/abs/2005.14050
AUTHORS:        Su Lin Blodgett ; Solon Barocas ; Hal Daumé III ; Hanna Wallach
HIGHLIGHT:      Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems.

27, TITLE:      Model-View-Update-Communicate: Session Types meet the Elm Architecture
http://arxiv.org/abs/1910.11108
AUTHORS:        Simon Fowler
COMMENTS:       Extended version of paper to appear at ECOOP 2020
HIGHLIGHT:      In this paper, we propose the first principled integration of session typing and GUI development by building upon the Model-View-Update (MVU) architecture, pioneered by the Elm programming language.

28, TITLE:      Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
http://arxiv.org/abs/2005.08874
AUTHORS:        Tobias Huber ; Katharina Weitz ; Elisabeth André ; Ofra Amir
HIGHLIGHT:      In this paper, we combine global and local explanation methods, and evaluate their joint and separate contributions, providing (to the best of our knowledge) the first user study of combined local and global explanations for RL agents.

29, TITLE:      Optimal coding and the origins of Zipfian laws
http://arxiv.org/abs/1906.01545
AUTHORS:        Ramon Ferrer-i-Cancho ; Christian Bentz ; Caio Seguin
COMMENTS:       in press in the Journal of Quantitative Linguistics; definition of concordant pair corrected, proofs polished, references updated
HIGHLIGHT:      Here we consider the problem of optimal coding -- under an arbitrary coding scheme -- and show that it predicts Zipf's law of abbreviation, namely a tendency in natural languages for more frequent words to be shorter.

30, TITLE:      Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
http://arxiv.org/abs/1910.11111
AUTHORS:        Dimitrios Kollias ; Viktoriia Sharmanska ; Stefanos Zafeiriou
COMMENTS:       filed as a patent
HIGHLIGHT:      We present the first and the largest study of all facial behaviour tasks learned jointly in a single multi-task, multi-domain and multi-label network, which we call FaceBehaviorNet.

31, TITLE:      Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning
http://arxiv.org/abs/2002.06703
AUTHORS:        Guy Davidson ; Brenden M. Lake
HIGHLIGHT:      We discover that providing the Rainbow model (Hessel et al.,2018) with simple, feature-engineered object representations substantially boosts its performance on the Frostbite game from Atari 2600.

32, TITLE:      Parallelizing Machine Learning as a Service for the End-User
http://arxiv.org/abs/2005.14080
AUTHORS:        Daniela Loreti ; Marco Lippi ; Paolo Torroni
HIGHLIGHT:      In this paper, we present a distributed architecture that could be exploited to parallelize a typical ML system pipeline.

33, TITLE:      Neural Pose Transfer by Spatially Adaptive Instance Normalization
http://arxiv.org/abs/2003.07254
AUTHORS:        Jiashun Wang ; Chao Wen ; Yanwei Fu ; Haitao Lin ; Tianyun Zou ; Xiangyang Xue ; Yinda Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      Particularly in this paper, we are interested in transferring the pose of source human mesh to deform the target human mesh, while the source and target meshes may have different identity information.

34, TITLE:      Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study
http://arxiv.org/abs/2005.11619
AUTHORS:        Himanshu Sharma ; Elise Jennings
HIGHLIGHT:      We present a performance and scalability comparison of training the VGG-16 and Resnet-18 models on a Cray-XC40 cluster.

35, TITLE:      Ants can orienteer a thief in their robbery
http://arxiv.org/abs/2004.07017
AUTHORS:        Jonatas B. C. Chagas ; Markus Wagner
HIGHLIGHT:      We propose a two-phase, swarm-intelligence based approach together with a new randomized packing heuristic.

36, TITLE:      Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
http://arxiv.org/abs/2004.01980
AUTHORS:        Di Jin ; Zhijing Jin ; Joey Tianyi Zhou ; Lisa Orii ; Peter Szolovits
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers.

37, TITLE:      Explainable Deep Convolutional Candlestick Learner
http://arxiv.org/abs/2001.02767
AUTHORS:        Jun-Hao Chen ; Samuel Yen-Chi Chen ; Yun-Cheng Tsai ; Chih-Shiang Shur
COMMENTS:       Accepted by The 32nd International Conference on Software Engineering & Knowledge Engineering (SEKE 2020), KSIR Virtual Conference Cener, Pittsburgh, USA, July 9--July 19, 2020
HIGHLIGHT:      In this contribution, we provide a framework which is to explain the reasoning of the learned model determining the specific candlestick patterns of time series.

38, TITLE:      miniKanren as a Tool for Symbolic Computation in Python
http://arxiv.org/abs/2005.11644
AUTHORS:        Brandon T. Willard
HIGHLIGHT:      In this article, we give a brief overview of the current state and future potential of symbolic computation within the Python statistical modeling and machine learning community.

39, TITLE:      Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
http://arxiv.org/abs/2005.13837
AUTHORS:        Dong Bok Lee ; Seanie Lee ; Woo Tae Jeong ; Donghwan Kim ; Sung Ju Hwang
COMMENTS:       ACL 2020
HIGHLIGHT:      In this work, we propose a hierarchical conditional variational autoencoder(HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency.

40, TITLE:      DR$\vert$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
http://arxiv.org/abs/1910.11777
AUTHORS:        Teresa Araújo ; Guilherme Aresta ; Luís Mendonça ; Susana Penas ; Carolina Maia ; Ângela Carneiro ; Ana Maria Mendonça ; Aurélio Campilho
COMMENTS:       Published at Medical Image Analysis (Elsevier). Publication licensed under the Creative Commons CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. Figures are compressed due to file size constraints
HIGHLIGHT:      We propose DR$\vert$GRADUATE, a novel deep learning-based DR grading CAD system that supports its decision by providing a medically interpretable explanation and an estimation of how uncertain that prediction is, allowing the ophthalmologist to measure how much that decision should be trusted.

41, TITLE:      What am I Searching for: Zero-shot Target Identity Inference in Visual Search
http://arxiv.org/abs/2005.12741
AUTHORS:        Mengmi Zhang ; Gabriel Kreiman
COMMENTS:       this was a mistaken new submission and a pointer to arXiv:1807.11926
HIGHLIGHT:      As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior.

42, TITLE:      von Neumann-Morgenstern and Savage Theorems for Causal Decision Making
http://arxiv.org/abs/1907.11752
AUTHORS:        Mauricio Gonzalez-Soto ; Luis E. Sucar ; Hugo J. Escalante
COMMENTS:       Submitted to Minds and Machines
HIGHLIGHT:      von Neumann-Morgenstern and Savage Theorems for Causal Decision Making

43, TITLE:      Gradual System F
http://arxiv.org/abs/1807.04596
AUTHORS:        Elizabeth Labrada ; Matías Toro ; Éric Tanter
COMMENTS:       Journal submission, extends and subsumes POPL'19
HIGHLIGHT:      Starting from a detailed review of the challenges and tensions that affect the design of gradual parametric languages, this work presents an extensive account of the semantics and metatheory of GSF, a gradual counterpart of System F.

44, TITLE:      Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
http://arxiv.org/abs/1811.12823
AUTHORS:        Daniil Polykovskiy ; Alexander Zhebrak ; Benjamin Sanchez-Lengeling ; Sergey Golovanov ; Oktai Tatanov ; Stanislav Belyaev ; Rauf Kurbanov ; Aleksey Artamonov ; Vladimir Aladinskiy ; Mark Veselov ; Artur Kadurin ; Simon Johansson ; Hongming Chen ; Sergey Nikolenko ; Alan Aspuru-Guzik ; Alex Zhavoronkov
HIGHLIGHT:      In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to support research on generative models for drug discovery.

45, TITLE:      Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
http://arxiv.org/abs/2005.13983
AUTHORS:        Weixia Zhang ; Kede Ma ; Guangtao Zhai ; Xiaokang Yang
COMMENTS:       Under review
HIGHLIGHT:      To confront the cross-distortion-scenario challenge, we develop a unified BIQA model and an effective approach of training it for both synthetic and realistic distortions.

46, TITLE:      Precision Gating: Improving Neural Network Efficiency with Dynamic Dual-Precision Activations
http://arxiv.org/abs/2002.07136
AUTHORS:        Yichi Zhang ; Ritchie Zhao ; Weizhe Hua ; Nayun Xu ; G. Edward Suh ; Zhiru Zhang
COMMENTS:       Published as a conference paper at ICLR 2020
HIGHLIGHT:      We propose precision gating (PG), an end-to-end trainable dynamic dual-precision quantization technique for deep neural networks.

47, TITLE:      Pattern Denoising in Molecular Associative Memory using Pairwise Markov Random Field Models
http://arxiv.org/abs/2005.13780
AUTHORS:        Dharani Punithan ; Byoung-Tak Zhang
HIGHLIGHT:      We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model.

48, TITLE:      Generating Correctness Proofs with Neural Networks
http://arxiv.org/abs/1907.07794
AUTHORS:        Alex Sanchez-Stern ; Yousef Alhessi ; Lawrence Saul ; Sorin Lerner
COMMENTS:       Condensed version to be published at MAPL 2020
HIGHLIGHT:      In this paper we present Proverbot9001,a proof search system using machine learning techniques to produce proofs of software correctness in interactive theorem provers.

49, TITLE:      Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
http://arxiv.org/abs/2005.13109
AUTHORS:        Shushman Choudhury ; Jayesh K. Gupta ; Mykel J. Kochenderfer ; Dorsa Sadigh ; Jeannette Bohg
COMMENTS:       Robotics Science and Systems (RSS) 2020; Source code at https://github.com/sisl/SCoBA.jl
HIGHLIGHT:      We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination and addresses them in a hierarchical manner.

50, TITLE:      Neural Topological SLAM for Visual Navigation
http://arxiv.org/abs/2005.12256
AUTHORS:        Devendra Singh Chaplot ; Ruslan Salakhutdinov ; Abhinav Gupta ; Saurabh Gupta
COMMENTS:       Published in CVPR 2020. See the project webpage at https://devendrachaplot.github.io/projects/Neural-Topological-SLAM
HIGHLIGHT:      We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.

51, TITLE:      Automatic Curriculum Learning For Deep RL: A Short Survey
http://arxiv.org/abs/2003.04664
AUTHORS:        Rémy Portelas ; Cédric Colas ; Lilian Weng ; Katja Hofmann ; Pierre-Yves Oudeyer
COMMENTS:       Accepted at IJCAI2020
HIGHLIGHT:      Automatic Curriculum Learning For Deep RL: A Short Survey

52, TITLE:      Intermediate problems in modular circuits satisfiability
http://arxiv.org/abs/2002.08626
AUTHORS:        Paweł M. Idziak ; Piotr Kawałek ; Jacek Krzaczkowski
HIGHLIGHT:      In this paper we provide a broad class of examples, lying in this grey area, and show that, under the Exponential Time Hypothesis and Strong Exponential Size Hypothesis (saying that Boolean circuits need exponentially many modular counting gates to produce boolean conjunctions of any arity), satisfiability over these algebras have intermediate complexity between $\Omega(2^{c\log^{h-1} n})$ and $O(2^{c\log^h n})$, where $h$ measures how much a nilpotent algebra fails to be supernilpotent.

53, TITLE:      Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets
http://arxiv.org/abs/1907.10087
AUTHORS:        Naima Otberdout ; Mohamed Daoudi ; Anis Kacem ; Lahoucine Ballihi ; Stefano Berretti
HIGHLIGHT:      In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image.