2020.06.23.txt

==========New Papers==========
1, TITLE:       Always-On, Sub-300-nW, Event-Driven Spiking Neural Network based on Spike-Driven Clock-Generation and Clock- and Power-Gating for an Ultra-Low-Power Intelligent Device
http://arxiv.org/abs/2006.12314
AUTHORS:        Dewei Wang ; Pavan Kumar Chundi ; Sung Justin Kim ; Minhao Yang ; Joao Pedro Cerqueira ; Joonsung Kang ; Seunchul Jung ; Sangjoon Kim ; Mingoo Seok
HIGHLIGHT:      Toward this goal, we present a novel SNN classifier architecture for always-on functions, demonstrating sub-300nW power consumption at the competitive inference accuracy for a KWS and other always-on classification workloads.

2, TITLE:       Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators
http://arxiv.org/abs/2006.11469
AUTHORS:        Takeshi Teshima ; Isao Ishikawa ; Koichi Tojo ; Kenta Oono ; Masahiro Ikeda ; Masashi Sugiyama
COMMENTS:       28 pages, 3 figures
HIGHLIGHT:      As its corollary, we can affirmatively resolve a previously unsolved problem: whether normalizing flow models based on affine coupling can be universal distributional approximators.

3, TITLE:       On the Ability of a CNN to Realize Image-to-Image Language Conversion
http://arxiv.org/abs/2006.12316
AUTHORS:        Kohei Baba ; Seiichi Uchida ; Brian Kenji Iwana
COMMENTS:       Published at ICDAR 2019
HIGHLIGHT:      The purpose of this paper is to reveal the ability that Convolutional Neural Networks (CNN) have on the novel task of image-to-image language conversion.

4, TITLE:       Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
http://arxiv.org/abs/2006.12323
AUTHORS:        Xu Ji ; Joao Henriques ; Tinne Tuytelaars ; Andrea Vedaldi
HIGHLIGHT:      We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective, without extraneous buffers or generator networks.

5, TITLE:       Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning
http://arxiv.org/abs/2006.11476
AUTHORS:        Yuan Yao ; Chang Liu ; Dezhao Luo ; Yu Zhou ; Qixiang Ye
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we propose a novel self-supervised method, referred to as video Playback Rate Perception (PRP), to learn spatio-temporal representation in a simple-yet-effective way.

6, TITLE:       Patch Based Classification of Remote Sensing Data: A Comparison of 2D-CNN, SVM and NN Classifiers
http://arxiv.org/abs/2006.11767
AUTHORS:        Mahesh Pal ;  Akshay ; Himanshu Rohilla ; B. Charan Teja
COMMENTS:       8 pages, 2 Figures
HIGHLIGHT:      In this paper, we compare performance of patch based SVM and NN with that of a deep learning algorithms comprising of 2D-CNN and fully connected layers.

7, TITLE:       Fully-parallel Convolutional Neural Network Hardware
http://arxiv.org/abs/2006.12439
AUTHORS:        Christiam F. Frasser ; Pablo Linares-Serrano ; V. Canals ; Miquel Roca ; T. Serrano-Gotarredona ; Josep L. Rossello
COMMENTS:       8 pages, 6 figures, to be submitted to an IEEE journal
HIGHLIGHT:      In this work, we propose a new power-and-area-efficient architecture for implementing Articial Neural Networks (ANNs) in hardware, based on the exploitation of correlation phenomenon in Stochastic Computing (SC) systems.

8, TITLE:       Compiler Directed Speculative Intermittent Computation
http://arxiv.org/abs/2006.11479
AUTHORS:        Jongouk Choi ; Qingrui Liu ; Changhee Jung
HIGHLIGHT:      This paper presents CoSpec, a new architecture/compiler co-design scheme that works for commodity in-order processors used in energy-harvesting systems.

9, TITLE:       Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
http://arxiv.org/abs/2006.12328
AUTHORS:        Joeran Beel ; Bryan Tyrell ; Edward Bergman ; Andrew Collins ; Shahad Nagoor
HIGHLIGHT:      We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features.

10, TITLE:      wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations
http://arxiv.org/abs/2006.11477
AUTHORS:        Alexei Baevski ; Henry Zhou ; Abdelrahman Mohamed ; Michael Auli
HIGHLIGHT:      We show for the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.

11, TITLE:      Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision
http://arxiv.org/abs/2006.11481
AUTHORS:        Haojie Liu ; Kang Liao ; Chunyu Lin ; Yao Zhao ; Yulan Guo
COMMENTS:       10 pages, 6 figures
HIGHLIGHT:      To solve the above problems, we propose a Pseudo-LiDAR point cloud interpolation network to generates temporally and spatially high-quality point cloud sequences.

12, TITLE:      Constant-Space, Constant-Randomness Verifiers with Arbitrarily Small Error
http://arxiv.org/abs/2006.12330
AUTHORS:        M. Utkan Gezer ; A. C. Cem Say
HIGHLIGHT:      We study the capabilities of probabilistic finite-state machines that act as verifiers for certificates of language membership for input strings, in the regime where the verifiers are restricted to toss some fixed nonzero number of coins regardless of the input size.

13, TITLE:      Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition
http://arxiv.org/abs/2006.11593
AUTHORS:        Yongming Li ; Lang Zhou ; Lingyun Qin ; Yuwei Zeng ; Yuchuan Liu ; Yan Lei ; Pin Wang ; Fan Li
COMMENTS:       15 pages, 4 figures
HIGHLIGHT:      To solve these two problems, based on the existing Parkinson speech feature data set, a deep double-side learning ensemble model is designed in this paper that can reconstruct speech features and samples deeply and simultaneously.

14, TITLE:      Unsupervised Image Classification for Deep Representation Learning
http://arxiv.org/abs/2006.11480
AUTHORS:        Weijie Chen ; Shiliang Pu ; Di Xie ; Shicai Yang ; Yilu Guo ; Luojun Lin
COMMENTS:       submitted to ECCV 2020
HIGHLIGHT:      In this work, we aim to make this framework more simple and elegant without performance decline.

15, TITLE:      A Baseline Approach for AutoImplant: the MICCAI 2020 Cranial Implant Design Challenge
http://arxiv.org/abs/2006.12449
AUTHORS:        Jianning Li ; Antonio Pepe ; Christina Gsaxner ; Gord von Campe ; Jan Egger
COMMENTS:       12 pages
HIGHLIGHT:      In this study, we present a baseline approach for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge, which, as suggested by the organizers, can be formulated as a volumetric shape learning task.

16, TITLE:      Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
http://arxiv.org/abs/2006.12442
AUTHORS:        Stephen Roller ; Y-Lan Boureau ; Jason Weston ; Antoine Bordes ; Emily Dinan ; Angela Fan ; David Gunning ; Da Ju ; Margaret Li ; Spencer Poff ; Pratik Ringshia ; Kurt Shuster ; Eric Michael Smith ; Arthur Szlam ; Jack Urbanek ; Mary Williamson
HIGHLIGHT:      We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.

17, TITLE:      FaceHack: Triggering backdoored facial recognition systems using facial characteristics
http://arxiv.org/abs/2006.11623
AUTHORS:        Esha Sarkar ; Hadjer Benkraouda ; Michail Maniatakos
HIGHLIGHT:      In this work, we demonstrate that specific changes to facial characteristics may also be used to trigger malicious behavior in an ML model.

18, TITLE:      Defense against Adversarial Attacks in NLP via Dirichlet Neighborhood Ensemble
http://arxiv.org/abs/2006.11627
AUTHORS:        Yi Zhou ; Xiaoqing Zheng ; Cho-Jui Hsieh ; Kai-wei Chang ; Xuanjing Huang
HIGHLIGHT:      In this paper, we propose Dirichlet Neighborhood Ensemble (DNE), a randomized smoothing method for training a robust model to defense substitution-based attacks.

19, TITLE:      Quickest Intruder Detection for Multiple User Active Authentication
http://arxiv.org/abs/2006.11921
AUTHORS:        Pramuditha Perera ; Julian Fierrez ; Vishal M. Patel
COMMENTS:       Accepted for publication in ICIP 2020
HIGHLIGHT:      In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users.

20, TITLE:      G2D: Generate to Detect Anomalies
http://arxiv.org/abs/2006.11629
AUTHORS:        Masoud Pourreza ; Bahram Mohammadi ; Mostafa Khaki ; Samir Bouindour ; Hichem Snoussi ; Mohammad Sabokrou
HIGHLIGHT:      In this paper, we propose a novel method for irregularity detection.

21, TITLE:      Lyric Video Analysis Using Text Detection and Tracking
http://arxiv.org/abs/2006.11933
AUTHORS:        Shota Sakaguchi ; Jun Kato ; Masataka Goto ; Seiichi Uchida
COMMENTS:       15 pages, 8 figures, DAS 2020
HIGHLIGHT:      The purpose of this paper is to analyze the motion of the lyric words in lyric videos, as the first step of automatic lyric video generation.

22, TITLE:      A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems
http://arxiv.org/abs/2006.11630
AUTHORS:        Junqi Tang ; Mike Davies
HIGHLIGHT:      In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems.

23, TITLE:      FilterNet: A Neighborhood Relationship Enhanced Fully Convolutional Network for Calf Muscle Compartment Segmentation
http://arxiv.org/abs/2006.11930
AUTHORS:        Zhihui Guo ; Honghai Zhang ; Zhi Chen ; Ellen van der Plas ; Laurie Gutmann ; Daniel Thedens ; Peggy Nopoulos ; Milan Sonka
HIGHLIGHT:      We present a novel fully convolutional network (FCN), called FilterNet, that utilizes contextual information in a large neighborhood and embeds edge-aware constraints for individual calf muscle compartment segmentations.

24, TITLE:      Estimating Model Uncertainty of Neural Networks in Sparse Information Form
http://arxiv.org/abs/2006.11631
AUTHORS:        Jongseok Lee ; Matthias Humt ; Jianxiang Feng ; Rudolph Triebel
COMMENTS:       Accepted to the Thirty-seventh International Conference on Machine Learning (ICML) 2020
HIGHLIGHT:      The key insight of our work is that the information matrix, i.e. the inverse of the covariance matrix tends to be sparse in its spectrum.

25, TITLE:      Dynamic Symbolic Execution of Higher-Order Functions
http://arxiv.org/abs/2006.11639
AUTHORS:        Shu-Hung You ; Robert Bruce Findler ; Christos Dimoulas
HIGHLIGHT:      In this paper, we take the first step towards solving the problem; we offer a design, semantics, and prototype for concolic testing of higher-order functions.

26, TITLE:      End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction
http://arxiv.org/abs/2006.11958
AUTHORS:        Abdullah M. Zyarah ; Kevin Gomez ; Dhireesha Kudithipudi
HIGHLIGHT:      In this paper, a neuromorphic system that processes spatio-temporal information on the edge is proposed.

27, TITLE:      Differentiable Rendering: A Survey
http://arxiv.org/abs/2006.12057
AUTHORS:        Hiroharu Kato ; Deniz Beker ; Mihai Morariu ; Takahiro Ando ; Toru Matsuoka ; Wadim Kehl ; Adrien Gaidon
HIGHLIGHT:      This paper reviews existing literature and discusses the current state of differentiable rendering, its applications and open research problems.

28, TITLE:      Object Tracking through Residual and Dense LSTMs
http://arxiv.org/abs/2006.12061
AUTHORS:        Fabio Garcea ; Alessandro Cucco ; Lia Morra ; Fabrizio Lamberti
HIGHLIGHT:      Inspired by the success of residual and dense networks in image recognition, we propose here to enhance the capabilities of hybrid trackers using residual and/or dense LSTMs.

29, TITLE:      MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
http://arxiv.org/abs/2006.12075
AUTHORS:        Mingyi Shi ; Kfir Aberman ; Andreas Aristidou ; Taku Komura ; Dani Lischinski ; Daniel Cohen-Or ; Baoquan Chen
COMMENTS:       Accepted to Transactions on Graphics (ToG) 2020. Project page: {https://rubbly.cn/publications/motioNet} Video: {https://youtu.be/8YubchlzvFA}
HIGHLIGHT:      We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video.While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.

30, TITLE:      Split to Be Slim: An Overlooked Redundancy in Vanilla Convolution
http://arxiv.org/abs/2006.12085
AUTHORS:        Qiulin Zhang ; Zhuqing Jiang ; Qishuo Lu ; Jia'nan Han ; Zhengxin Zeng ; Shang-hua Gao ; Aidong Men
COMMENTS:       Preprint version. The final version has been accepted to appear at IJCAI20
HIGHLIGHT:      Many effective solutions have been proposed to reduce the redundancy of models for inference acceleration.

31, TITLE:      Kiwifruit detection in challenging conditions
http://arxiv.org/abs/2006.11729
AUTHORS:        Mahla Nejati ; Nicky Penhall ; Henry Williams ; Jamie Bell ; JongYoon Lim ; Ho Seok Ahn ; Bruce MacDonald
COMMENTS:       Accepted to the Australasian conference on robotics and automation (ACRA 2019)
HIGHLIGHT:      This paper presents a semantic segmentation approach with two novel image prepossessing techniques designed to detect kiwifruit under the harsh lighting conditions found in the canopy.

32, TITLE:      Verifying Individual Fairness in Machine Learning Models
http://arxiv.org/abs/2006.11737
AUTHORS:        Philips George John ; Deepak Vijaykeerthy ; Diptikalyan Saha
COMMENTS:       An extended version of the paper accepted at UAI 2020, 12 pages, code is available at https://github.com/philips-george/ifv-uai-2020
HIGHLIGHT:      Our objective is to construct verifiers for proving individual fairness of a given model, and we do so by considering appropriate relaxations of the problem.

33, TITLE:      Progressive Graph Learning for Open-Set Domain Adaptation
http://arxiv.org/abs/2006.12087
AUTHORS:        Yadan Luo ; Zijian Wang ; Zi Huang ; Mahsa Baktashmotlagh
HIGHLIGHT:      More specifically, we introduce an end-to-end Progressive Graph Learning (PGL) framework where a graph neural network with episodic training is integrated to suppress underlying conditional shift and adversarial learning is adopted to close the gap between the source and target distributions.

34, TITLE:      Efficient Integer-Arithmetic-Only Convolutional Neural Networks
http://arxiv.org/abs/2006.11735
AUTHORS:        Hengrui Zhao ; Dong Liu ; Houqiang Li
COMMENTS:       8 pages, 4 figures
HIGHLIGHT:      We analyze this phonomenon and find that the decline is due to activation quantization.

35, TITLE:      Deep Low-rank Prior in Dynamic MR Imaging
http://arxiv.org/abs/2006.12090
AUTHORS:        Ziwen Ke ; Wenqi Huang ; Jing Cheng ; Xin Liu ; Hairong Zheng ; Leslie Ying ; Yanjie Zhu ; Dong Liang
COMMENTS:       12 pages, 7 figures
HIGHLIGHT:      In this paper, we explore deep low-rank prior in dynamic MR imaging to obtain improved reconstruction results.

36, TITLE:      Achieving Better Kinship Recognition Through Better Baseline
http://arxiv.org/abs/2006.11739
AUTHORS:        Andrei Shadrikov
COMMENTS:       Accepted for the 4th Recognizing Families In the Wild Workshop
HIGHLIGHT:      We present a new baseline for an automatic kinship recognition task and relatives search based on RetinaFace[1] for face registration and ArcFace[2] face verification model.

37, TITLE:      Weak Supervision and Referring Attention for Temporal-Textual Association Learning
http://arxiv.org/abs/2006.11747
AUTHORS:        Zhiyuan Fang ; Shu Kong ; Zhe Wang ; Charless Fowlkes ; Yezhou Yang
COMMENTS:       12 pages, 6 figures
HIGHLIGHT:      Therefore we provide a Weak-Supervised alternative with our proposed Referring Attention mechanism to learn temporal-textual association (dubbed WSRA).

38, TITLE:      Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning
http://arxiv.org/abs/2006.11751
AUTHORS:        Aleksei Petrenko ; Zhehui Huang ; Tushar Kumar ; Gaurav Sukhatme ; Vladlen Koltun
COMMENTS:       Paper published in ICML2020. Visualizations of trained policies can be found at https://sites.google.com/view/sample-factory
HIGHLIGHT:      In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.

39, TITLE:      Methodology for Building Synthetic Datasets with Virtual Humans
http://arxiv.org/abs/2006.11757
AUTHORS:        Shubhajit Basak ; Hossein Javidnia ; Faisal Khan ; Rachel McDonnell ; Michael Schukat
COMMENTS:       Conference - ISSC 2020
HIGHLIGHT:      In this work, we explore a framework to synthetically generate facial data to be used as part of a toolchain to generate very large facial datasets with a high degree of control over facial and environmental variations.

40, TITLE:      Examination of community sentiment dynamics due to covid-19 pandemic: a case study from Australia
http://arxiv.org/abs/2006.12185
AUTHORS:        Jianlong Zhou ; Shuiqiao Yang ; Chun Xiao ; Fang Chen
HIGHLIGHT:      In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period.

41, TITLE:      Neural networks adapting to datasets: learning network size and topology
http://arxiv.org/abs/2006.12195
AUTHORS:        Romuald A. Janik ; Aleksandra Nowak
HIGHLIGHT:      We introduce a flexible setup allowing for a neural network to learn both its size and topology during the course of a standard gradient-based training.

42, TITLE:      Flood severity mapping from Volunteered Geographic Information by interpreting water level from images containing people: a case study of Hurricane Harvey
http://arxiv.org/abs/2006.11802
AUTHORS:        Yu Feng ; Claus Brenner ; Monika Sester
HIGHLIGHT:      In this paper, we propose a novel three-step pipeline method to extract and map flood severity information.

43, TITLE:      Sequential Feature Filtering Classifier
http://arxiv.org/abs/2006.11808
AUTHORS:        Minseok Seo ; Jaemin Lee ; Jongchan Park ; Dong-Geol Choi
HIGHLIGHT:      We propose Sequential Feature Filtering Classifier (FFC), a simple but effective classifier for convolutional neural networks (CNNs).

44, TITLE:      Improving Image Captioning with Better Use of Captions
http://arxiv.org/abs/2006.11807
AUTHORS:        Zhan Shi ; Xu Zhou ; Xipeng Qiu ; Xiaodan Zhu
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation.

45, TITLE:      A blindspot of AI ethics: anti-fragility in statistical prediction
http://arxiv.org/abs/2006.11814
AUTHORS:        Michele Loi ; Lonneke van der Plas
COMMENTS:       7th Swiss Conference on Data Science (accepted as Panel)
HIGHLIGHT:      With this paper, we aim to put an issue on the agenda of AI ethics that in our view is overlooked in the current discourse.

46, TITLE:      Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning
http://arxiv.org/abs/2006.11812
AUTHORS:        Giancarlo Paoletti ; Jacopo Cavazza ; Cigdem Beyan ; Alessio Del Bue
HIGHLIGHT:      To this end, we propose a novel subspace clustering method, which exploits covariance matrix to enhance the action's discriminability and a timestamp pruning approach that allow us to better handle the temporal dimension of the data.

47, TITLE:      TreeRNN: Topology-Preserving Deep GraphEmbedding and Learning
http://arxiv.org/abs/2006.11825
AUTHORS:        Yecheng Lyu ; Ming Li ; Xinming Huang ; Ziming Zhang
COMMENTS:       10 pages
HIGHLIGHT:      In contrast to the literature where the graph local patterns are captured by customized graph kernels, in this paper we study the problem of how to effectively and efficiently transfer such graphs into image space so that order-sensitive networks such as recurrent neural networks (RNNs) can better extract local pattern in this regularized forms.

48, TITLE:      Enriching Large-Scale Eventuality Knowledge Graph with Entailment Relations
http://arxiv.org/abs/2006.11824
AUTHORS:        Changlong Yu ; Hongming Zhang ; Yangqiu Song ; Wilfred Ng ; Lifeng Shang
COMMENTS:       Accepted by AKBC 2020
HIGHLIGHT:      In this paper, we propose a scalable approach to model the entailment relations between eventualities ("eat an apple'' entails ''eat fruit'').

49, TITLE:      Refined bounds for algorithm configuration: The knife-edge of dual class approximability
http://arxiv.org/abs/2006.11827
AUTHORS:        Maria-Florina Balcan ; Tuomas Sandholm ; Ellen Vitercik
HIGHLIGHT:      Automating algorithm configuration is growing increasingly necessary as algorithms come with more and more tunable parameters.

50, TITLE:      AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
http://arxiv.org/abs/2006.11834
AUTHORS:        Yong Cheng ; Lu Jiang ; Wolfgang Macherey ; Jacob Eisenstein
COMMENTS:       In ACL 2020
HIGHLIGHT:      In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT).

51, TITLE:      The NYU-CUBoulder Systems for SIGMORPHON 2020 Task 0 and Task 2
http://arxiv.org/abs/2006.11830
AUTHORS:        Assaf Singer ; Katharina Kann
COMMENTS:       8 pages, 2 figures
HIGHLIGHT:      We describe the NYU-CUBoulder systems for the SIGMORPHON 2020 Task 0 on typologically diverse morphological inflection and Task 2 on unsupervised morphological paradigm completion.

52, TITLE:      Unsupervised Learning of Deep-Learned Features from Breast Cancer Images
http://arxiv.org/abs/2006.11843
AUTHORS:        Sanghoon Lee ; Colton Farley ; Simon Shim ; Yanjun Zhao ; Wookjin Choi ; Wook-Sung Yoo
COMMENTS:       7 pages for IEEE BIBE
HIGHLIGHT:      In this paper, we propose an unsupervised learning approach for detecting cancer in breast invasive carcinoma (BRCA) whole slide images.

53, TITLE:      Quanta Burst Photography
http://arxiv.org/abs/2006.11840
AUTHORS:        Sizhuo Ma ; Shantanu Gupta ; Arin C. Ulku ; Claudio Bruschini ; Edoardo Charbon ; Mohit Gupta
COMMENTS:       A version with better-quality images can be found on the project webpage: http://wisionlab.cs.wisc.edu/project/quanta-burst-photography/
HIGHLIGHT:      We present quanta burst photography, a computational photography technique that leverages SPCs as passive imaging devices for photography in challenging conditions, including ultra low-light and fast motion.

54, TITLE:      Emerging Biometrics: Deep Inference and Other Computational Intelligence
http://arxiv.org/abs/2006.11971
AUTHORS:        Svetlana Yanushkevich ; Shawn Eastwood ; Kenneth Lai ; Vlad Shmerko
COMMENTS:       Survey paper
HIGHLIGHT:      This paper aims at identifying emerging computational intelligence trends for the design and modeling of complex biometric-enabled infrastructure and systems.

55, TITLE:      Labeling Explicit Discourse Relations using Pre-trained Language Models
http://arxiv.org/abs/2006.11852
AUTHORS:        Murathan Kurfalı
COMMENTS:       To be presented at TSD 2020
HIGHLIGHT:      We evaluate our model on PDTB 2.0 and report the state-of-the-art results in the extraction of the full relation.

56, TITLE:      Perspective Texture Synthesis Based on Improved Energy Optimization
http://arxiv.org/abs/2006.11851
AUTHORS:        Syed Muhammad Arsalan Bashir ; Farhan Ali Khan Ghouri
COMMENTS:       Published in PLOS One
HIGHLIGHT:      In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures.

57, TITLE:      ELF: An Early-Exiting Framework for Long-Tailed Classification
http://arxiv.org/abs/2006.11979
AUTHORS:        Rahul Duggal ; Scott Freitas ; Sunny Dhamnani ; Duen Horng ;  Chau ; Jimeng Sun
HIGHLIGHT:      To incorporate this notion of hardness into the learning process, we propose the EarLy-exiting Framework(ELF).

58, TITLE:      Functional differentiations in evolutionary reservoir computing networks
http://arxiv.org/abs/2006.11507
AUTHORS:        Yutaka Yamaguti ; Ichiro Tsuda
COMMENTS:       This article has been submitted to Chaos. After it is published, it will be found at https://aip.scitation.org/journal/cha
HIGHLIGHT:      In this paper, we show how specific neuronal units are yielded in the evolutionary reservoir computer.

59, TITLE:      G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Space
http://arxiv.org/abs/2006.11510
AUTHORS:        Cong Wang ; Witold Pedrycz ; ZhiWu Li ; MengChu Zhou ; Shuzhi Sam Ge
COMMENTS:       12 pages, 17 figures, 2 tables
HIGHLIGHT:      This work elaborates a similarity-preserving Fuzzy C-Means (FCM) algorithm for G-image segmentation and aims to develop techniques and tools for segmenting G-images.

60, TITLE:      Computational Enhancement of Molecularly Targeted Contrast-Enhanced Ultrasound: Application to Human Breast Tumor Imaging
http://arxiv.org/abs/2006.11993
AUTHORS:        Andrew A. Berlin ; Mon Young ; Ahmed El Kaffas ; Sam Gambhir ; Amelie Lutz ; Maria Luigia Storto ; Juergen Willmann
HIGHLIGHT:      We have developed computational enhancement techniques for mCEUS tailored to address the unique challenges of imaging contrast accumulation in humans.

61, TITLE:      Global Image Sentiment Transfer
http://arxiv.org/abs/2006.11989
AUTHORS:        Jie An ; Tianlang Chen ; Songyang Zhang ; Jiebo Luo
HIGHLIGHT:      This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer sentiments of images according to a given sentiment tag.

62, TITLE:      COVID-19 Image Data Collection: Prospective Predictions Are the Future
http://arxiv.org/abs/2006.11988
AUTHORS:        Joseph Paul Cohen ; Paul Morrison ; Lan Dao ; Karsten Roth ; Tim Q Duong ; Marzyeh Ghassemi
COMMENTS:       Code for baseline experiments can be found here: https://github.com/mlmed/torchxrayvision/tree/master/scripts/covid-baselines
HIGHLIGHT:      We present multiple possible use cases for the data such as predicting the need for the ICU, predicting patient survival, and understanding a patient's trajectory during treatment.

63, TITLE:      Effective Version Space Reduction for Convolutional Neural Networks
http://arxiv.org/abs/2006.12456
AUTHORS:        Jiayu Liu ; Ioannis Chiotellis ; Rudolph Triebel ; Daniel Cremers
COMMENTS:       22 pages, 8 figures, to be published in the Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2020
HIGHLIGHT:      We identify the connection between two approaches---prior mass reduction and diameter reduction---and propose a new diameter-based querying method---the minimum Gibbs-vote disagreement.

64, TITLE:      Sarcasm Detection in Tweets with BERT and GloVe Embeddings
http://arxiv.org/abs/2006.11512
AUTHORS:        Akshay Khatri ; Pranav P ; Dr. Anand Kumar M
COMMENTS:       5 pages Submitted to ACL 2020 conference
HIGHLIGHT:      In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets.

65, TITLE:      Improving Query Safety at Pinterest
http://arxiv.org/abs/2006.11511
AUTHORS:        Abhijit Mahabal ; Yinrui Li ; Rajat Raina ; Daniel Sun ; Revati Mahajan ; Jure Lescovec
HIGHLIGHT:      We present PinSets, a system for query-set expansion, which applies a simple yet powerful mechanism to search user sessions, expanding a tiny seed set into thousands of related queries at nearly perfect precision, deep into the tail, along with explanations that are easy to interpret.

66, TITLE:      Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems
http://arxiv.org/abs/2006.12453
AUTHORS:        David Bayani ; Stefan Mitsch
COMMENTS:       26 pages, 7 figures
HIGHLIGHT:      We introduce Fanoos, a flexible framework for combining formal verification techniques, heuristic search, and user interaction to explore explanations at the desired level of granularity and fidelity.

67, TITLE:      Students Need More Attention: BERT-based AttentionModel for Small Data with Application to AutomaticPatient Message Triage
http://arxiv.org/abs/2006.11991
AUTHORS:        Shijing Si ; Rui Wang ; Jedrek Wosik ; Hao Zhang ; David Dov ; Guoyin Wang ; Ricardo Henao ; Lawrence Carin
COMMENTS:       20 pages, Machine Learning for Healthcare 2020 (To appear)
HIGHLIGHT:      So motivated, we propose a novel framework based on BioBERT (Bidirectional Encoder Representations from Transformers forBiomedical TextMining).

68, TITLE:      Slimming Neural Networks using Adaptive Connectivity Scores
http://arxiv.org/abs/2006.12463
AUTHORS:        Madan Ravi Ganesh ; Dawsin Blanchard ; Jason J. Corso ; Salimeh Yasaei Sekeh
COMMENTS:       22 pages
HIGHLIGHT:      In this work,we propose Slimming Neural networks using Adaptive Connectivity Measures(SNACS), as an algorithm that uses a probabilistic framework for compression while incorporating weight-based constraints at multiple levels to capitalize on both their strengths and overcome previous issues. To reduce the amount of unnecessary manual effort required to set the upper pruning limit of different layers in a DNN we propose a set of operating constraints to help automatically set them.

69, TITLE:      Modeling Lost Information in Lossy Image Compression
http://arxiv.org/abs/2006.11999
AUTHORS:        Yaolong Wang ; Mingqing Xiao ; Chang Liu ; Shuxin Zheng ; Tie-Yan Liu
HIGHLIGHT:      In this work, we propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.

70, TITLE:      Limits to Depth Efficiencies of Self-Attention
http://arxiv.org/abs/2006.12467
AUTHORS:        Yoav Levine ; Noam Wies ; Or Sharir ; Hofit Bata ; Amnon Shashua
HIGHLIGHT:      In this paper, we theoretically study the interplay between depth and width in self-attention, and shed light on the root of the above phenomenon.

71, TITLE:      Ecological Reinforcement Learning
http://arxiv.org/abs/2006.12478
AUTHORS:        John D. Co-Reyes ; Suvansh Sanjeev ; Glen Berseth ; Abhishek Gupta ; Sergey Levine
COMMENTS:       Preprint. Website at: https://sites.google.com/view/ecological-rl/home
HIGHLIGHT:      In this paper, instead of studying algorithmic improvements that can address such non-episodic and sparse reward settings, we instead study the kinds of environment properties that can make learning under such conditions easier.

72, TITLE:      Self-supervised Video Object Segmentation
http://arxiv.org/abs/2006.12480
AUTHORS:        Fangrui Zhu ; Li Zhang ; Yanwei Fu ; Guodong Guo ; Weidi Xie
HIGHLIGHT:      The objective of this paper is self-supervised representation learning, with the goal of solving semi-supervised video object segmentation (a.k.a. dense tracking).

73, TITLE:      Sample-Efficient Reinforcement Learning of Undercomplete POMDPs
http://arxiv.org/abs/2006.12484
AUTHORS:        Chi Jin ; Sham M. Kakade ; Akshay Krishnamurthy ; Qinghua Liu
HIGHLIGHT:      In particular, we present a sample-efficient algorithm, OOM-UCB, for episodic finite undercomplete POMDPs, where the number of observations is larger than the number of latent states and where exploration is essential for learning, thus distinguishing our results from prior works.

74, TITLE:      DiRS: On Creating Benchmark Datasets for Remote Sensing Image Interpretation
http://arxiv.org/abs/2006.12485
AUTHORS:        Yang Long ; Gui-Song Xia ; Shengyang Li ; Wen Yang ; Michael Ying Yang ; Xiao Xiang Zhu ; Liangpei Zhang ; Deren Li
COMMENTS:       Article submitted to IEEE Geoscience and Remote Sensing Magazine
HIGHLIGHT:      After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image analysis.

75, TITLE:      MaskIt: Masking for efficient utilization of incomplete public datasets for training deep learning models
http://arxiv.org/abs/2006.12004
AUTHORS:        Ankit Kariryaa
COMMENTS:       5 pages, 3 figures
HIGHLIGHT:      In the paper, we present a masking approach for training deep learning models from a publicly available but incomplete dataset.

76, TITLE:      Efficient text generation of user-defined topic using generative adversarial networks
http://arxiv.org/abs/2006.12005
AUTHORS:        Chenhan Yuan ; Yi-chin Huang ; Cheng-Hung Tsai
COMMENTS:       Accepted by CC-NLG 2019 (Workshop on Computational Creativity in Natural Language Generation)
HIGHLIGHT:      Therefore, we propose a User-Defined GAN (UD-GAN) with two-level discriminators to solve this problem.

77, TITLE:      Locally Masked Convolution for Autoregressive Models
http://arxiv.org/abs/2006.12486
AUTHORS:        Ajay Jain ; Pieter Abbeel ; Deepak Pathak
COMMENTS:       Published at Conference on Uncertainty in AI (UAI) 2020
HIGHLIGHT:      To generate data in arbitrary orders, we introduce LMConv: a simple modification to the standard 2D convolution that allows arbitrary masks to be applied to the weights at each location in the image.

78, TITLE:      Feature Alignment and Restoration for Domain Generalization and Adaptation
http://arxiv.org/abs/2006.12009
AUTHORS:        Xin Jin ; Cuiling Lan ; Wenjun Zeng ; Zhibo Chen
HIGHLIGHT:      In this paper, we propose a unified framework termed Feature Alignment and Restoration (FAR) to simultaneously ensure high generalization and discrimination power of the networks for effective DG and UDA.

79, TITLE:      Near-Optimal Reinforcement Learning with Self-Play
http://arxiv.org/abs/2006.12007
AUTHORS:        Yu Bai ; Chi Jin ; Tiancheng Yu
HIGHLIGHT:      This paper closes this gap for the first time: we propose an optimistic variant of the \emph{Nash Q-learning} algorithm with sample complexity $\tilde{\mathcal{O}}(SAB)$, and a new \emph{Nash V-learning} algorithm with sample complexity $\tilde{\mathcal{O}}(S(A+B))$.

80, TITLE:      Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
http://arxiv.org/abs/2006.12015
AUTHORS:        Hujie Pan ; Zining Wang ; Wei Zhan ; Masayoshi Tomizuka
COMMENTS:       ITSC2020
HIGHLIGHT:      In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction.

81, TITLE:      A Self-Attention Network based Node Embedding Model
http://arxiv.org/abs/2006.12100
AUTHORS:        Dai Quoc Nguyen ; Tu Dinh Nguyen ; Dinh Phung
COMMENTS:       Accepted version, ECML-PKDD 2020
HIGHLIGHT:      To this end, we propose SANNE -- a novel unsupervised embedding model -- whose central idea is to employ a transformer self-attention network to iteratively aggregate vector representations of nodes in random walks.

82, TITLE:      Exploiting Non-Taxonomic Relations for Measuring Semantic Similarity and Relatedness in WordNet
http://arxiv.org/abs/2006.12106
AUTHORS:        Mohannad AlMousa ; Rachid Benlamri ; Richard Khoury
HIGHLIGHT:      We propose a holistic poly-relational approach based on a new relation-based information content and non-taxonomic-based weighted paths to devise a comprehensive semantic similarity and relatedness measure.

83, TITLE:      The color out of space: learning self-supervised representations for Earth Observation imagery
http://arxiv.org/abs/2006.12119
AUTHORS:        Stefano Vincenzi ; Angelo Porrello ; Pietro Buzzega ; Marco Cipriano ; Pietro Fronte ; Roberto Cuccu ; Carla Ippoliti ; Annamaria Conte ; Simone Calderara
COMMENTS:       8 pages, 2 figures. Accepted in the 25th International Conference on PATTERN RECOGNITION (ICPR 2020), Milan, Italy
HIGHLIGHT:      In this work, we propose to learn meaningful representations from satellite imagery, leveraging its high-dimensionality spectral bands to reconstruct the visible colors.

84, TITLE:      Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
http://arxiv.org/abs/2006.12117
AUTHORS:        Sheeba Samuel ; Frank Löffler ; Birgitta König-Ries
COMMENTS:       Accepted at ProvenanceWeek 2020 (https://iitdbgroup.github.io/ProvenanceWeek2020/)
HIGHLIGHT:      In this paper, we describe our goals and initial steps in supporting the end-to-end reproducibility of ML pipelines.

85, TITLE:      Automated machine vision enabled detection of movement disorders from hand drawn spirals
http://arxiv.org/abs/2006.12121
AUTHORS:        Nabeel Seedat ; Vered Aharonson ; Ilana Schlesinger
COMMENTS:       Accepted IEEE International Conference on Healthcare Informatics 2020 (ICHI 2020), Upcoming Dec 2020, Copyright IEEE - 978-1-5386-5541-2/18/$31.00 Copyright, 2020 IEEE
HIGHLIGHT:      This study uses a dataset of scanned pen and paper drawings and a convolutional neural network (CNN) to perform classification between PD, ET and control subjects.

86, TITLE:      Learning with AMIGo: Adversarially Motivated Intrinsic Goals
http://arxiv.org/abs/2006.12122
AUTHORS:        Andres Campero ; Roberta Raileanu ; Heinrich Küttler ; Joshua B. Tenenbaum ; Tim Rocktäschel ; Edward Grefenstette
COMMENTS:       17 pages, 5 figures
HIGHLIGHT:      We propose AMIGo, a novel agent incorporating a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals to train a goal-conditioned "student" policy in the absence of (or alongside) environment reward.

87, TITLE:      Efficiently generating ground states is hard for postselected quantum computation
http://arxiv.org/abs/2006.12125
AUTHORS:        Yuki Takeuchi ; Yasuhiro Takahashi ; Seiichiro Tani
COMMENTS:       7 pages, 4 figures
HIGHLIGHT:      In this paper, we give evidence for this impossibility by applying an argument used in the quantum-computational-supremacy approach.

88, TITLE:      Self-Supervised Representations Improve End-to-End Speech Translation
http://arxiv.org/abs/2006.12124
AUTHORS:        Anne Wu ; Changhan Wang ; Juan Pino ; Jiatao Gu
HIGHLIGHT:      In this work, we explore whether self-supervised pre-trained speech representations can benefit the speech translation task in both high- and low-resource settings, whether they can transfer well to other languages, and whether they can be effectively combined with other common methods that help improve low-resource end-to-end speech translation such as using a pre-trained high-resource speech recognition system.

89, TITLE:      Organising a Successful AI Online Conference: Lessons from SoCS 2020
http://arxiv.org/abs/2006.12129
AUTHORS:        Daniel Harabor ; Mauro Vallati
HIGHLIGHT:      This paper describes challenges, approaches and opportunities associated with adapting these many different activities to the online setting.

90, TITLE:      Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features
http://arxiv.org/abs/2006.12127
AUTHORS:        Francisco J. H. Heras ; Gonzalo G. de Polavieja
HIGHLIGHT:      We explore the application of linear discriminant analysis (LDA) to the features obtained in different layers of pretrained deep convolutional neural networks (CNNs).

91, TITLE:      Emergent cooperation through mutual information maximization
http://arxiv.org/abs/2006.11769
AUTHORS:        Santiago Cuervo ; Marco Alzate
HIGHLIGHT:      With this in mind, we propose a decentralized deep reinforcement learning algorithm for the design of cooperative multi-agent systems.

92, TITLE:      Safe Reinforcement Learning via Curriculum Induction
http://arxiv.org/abs/2006.12136
AUTHORS:        Matteo Turchetta ; Andrey Kolobov ; Shital Shah ; Andreas Krause ; Alekh Agarwal
HIGHLIGHT:      This paper presents an alternative approach inspired by human teaching, where an agent learns under the supervision of an automatic instructor that saves the agent from violating constraints during learning.

93, TITLE:      Learning to Generate Noise for Robustness against Multiple Perturbations
http://arxiv.org/abs/2006.12135
AUTHORS:        Divyam Madaan ; Jinwoo Shin ; Sung Ju Hwang
HIGHLIGHT:      To tackle this challenge of robustness against multiple perturbations, we propose a novel meta-learning framework that explicitly learns to generate noise to improve the model's robustness against multiple types of attacks.

94, TITLE:      High-Precision Digital Traffic Recording with Multi-LiDAR Infrastructure Sensor Setups
http://arxiv.org/abs/2006.12140
AUTHORS:        Laurent Kloeker ; Christian Geller ; Amarin Kloeker ; Lutz Eckstein
COMMENTS:       Accepted to be published as part of the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, September 20-23, 2020
HIGHLIGHT:      Various methods can be used to collect such driving data records.

95, TITLE:      Modelling Agent Policies with Interpretable Imitation Learning
http://arxiv.org/abs/2006.11309
AUTHORS:        Tom Bewley ; Jonathan Lawry ; Arthur Richards
COMMENTS:       6 pages, 3 figures; under review for the 1st TAILOR Workshop, due to take place 29-30 August 2020 in Santiago de Compostela
HIGHLIGHT:      We present initial promising results from an implementation in a multi-agent traffic environment.

96, TITLE:      Envy-freeness up to one item: Shall we add or remove resources?
http://arxiv.org/abs/2006.11312
AUTHORS:        Martin Aleksandrov
COMMENTS:       10 pages, 1 table, 2 figures
HIGHLIGHT:      We propose two new axiomatic properties for allocations in this model: EF1+- and EFX+-.

97, TITLE:      SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
http://arxiv.org/abs/2006.11316
AUTHORS:        Forrest N. Iandola ; Albert E. Shaw ; Ravi Krishna ; Kurt W. Keutzer
COMMENTS:       9 pages + appendix
HIGHLIGHT:      In this work, we observe that methods such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these techniques have not been adopted by NLP neural network designers.

98, TITLE:      Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
http://arxiv.org/abs/2006.11325
AUTHORS:        Carlos Medina ; Arnout Devos ; Matthias Grossglauser
COMMENTS:       Extended version of work presented at the 7th ICML Workshop on Automated Machine Learning (2020). Code available at https://github.com/indy-lab/ProtoTransfer ; 17 pages, 3 figures, 12 tables
HIGHLIGHT:      Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.

99, TITLE:      Normalization Matters in Zero-Shot Learning
http://arxiv.org/abs/2006.11328
AUTHORS:        Ivan Skorokhodov ; Mohamed Elhoseiny
COMMENTS:       22 pages, 7 figures, 7 tables
HIGHLIGHT:      In this paper, we theoretically investigate two very popular tricks used in ZSL: "normalize+scale" trick and attributes normalization and show how they help to preserve a signal's variance in a typical model during a forward pass.

100, TITLE:     Generalized Zero and Few-Shot Transfer for Facial Forgery Detection
http://arxiv.org/abs/2006.11863
AUTHORS:        Shivangi Aneja ; Matthias Nießner
COMMENTS:       Project page: https://shivangi-aneja.github.io/ddt/
HIGHLIGHT:      We propose Deep Distribution Transfer(DDT), a new transfer learning approach to address the problem of zero and few-shot transfer in the context of facial forgery detection.

101, TITLE:     Image Sentiment Transfer
http://arxiv.org/abs/2006.11337
AUTHORS:        Tianlang Chen ; Wei Xiong ; Haitian Zheng ; Jiebo Luo
HIGHLIGHT:      In this work, we introduce an important but still unexplored research task -- image sentiment transfer.

102, TITLE:     Video Panoptic Segmentation
http://arxiv.org/abs/2006.11339
AUTHORS:        Dahun Kim ; Sanghyun Woo ; Joon-Young Lee ; In So Kweon
COMMENTS:       CVPR 2020 Oral. Code: see https://github.com/mcahny/vps
HIGHLIGHT:      In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation.

103, TITLE:     A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets
http://arxiv.org/abs/2006.11880
AUTHORS:        Chengchang Zeng ; Shaobo Li ; Qin Li ; Jie Hu ; Jianjun Hu
COMMENTS:       59 pages
HIGHLIGHT:      This shows the need of improving existing datasets, evaluation metrics and models to move the MRC models toward 'real' understanding.

104, TITLE:     Manifolds for Unsupervised Visual Anomaly Detection
http://arxiv.org/abs/2006.11364
AUTHORS:        Louise Naud ; Alexander Lavin
HIGHLIGHT:      To this end, we propose constant curvature manifolds for embedding data distributions in unsupervised visual anomaly detection.

105, TITLE:     A survey of face recognition techniques under occlusion
http://arxiv.org/abs/2006.11366
AUTHORS:        Dan Zeng ; Raymond Veldhuis ; Luuk Spreeuwers
HIGHLIGHT:      In this paper, we restrict the scope to occluded face recognition.

106, TITLE:     Feel The Music: Automatically Generating A Dance For An Input Song
http://arxiv.org/abs/2006.11905
AUTHORS:        Purva Tendulkar ; Abhishek Das ; Aniruddha Kembhavi ; Devi Parikh
COMMENTS:       4 pages
HIGHLIGHT:      We present a general computational approach that enables a machine to generate a dance for any input music.

107, TITLE:     Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation
http://arxiv.org/abs/2006.11487
AUTHORS:        Duong H. Le ; Vo Trung Nhan ; Nam Thoai
HIGHLIGHT:      In this work, we show that strong ensembles can be constructed from snapshots of iterative pruning, which achieve competitive performance and vary in network structure.

108, TITLE:     Online Handbook of Argumentation for AI: Volume 1
http://arxiv.org/abs/2006.12020
AUTHORS:         OHAAI Collaboration ; Federico Castagna ; Timotheus Kampik ; Atefeh Keshavarzi Zafarghandi ; Mickaël Lafages ; Jack Mumford ; Christos T. Rodosthenous ; Samy Sá ; Stefan Sarkadi ; Joseph Singleton ; Kenneth Skiba ; Andreas Xydis
COMMENTS:       editor: Federico Castagna and Francesca Mosca and Jack Mumford and Stefan Sarkadi and Andreas Xydis
HIGHLIGHT:      Previously, formal theories of argument and argument interaction have been proposed and studied, and this has led to the more recent study of computational models of argument.

109, TITLE:     Constructing Driver Hamiltonians for Several Linear Constraints
http://arxiv.org/abs/2006.12028
AUTHORS:        Hannes Leipold ; Federico M. Spedalieri
COMMENTS:       20 pages, 2 figures
HIGHLIGHT:      In this work, we develop a simple and intuitive algebraic framework for reasoning about the commutation of Hamiltonians with linear constraints - one that allows us to classify the complexity of finding a driver Hamiltonian for an arbitrary set of constraints as NP-hard.

110, TITLE:     Unsupervised Vehicle Re-identification with Progressive Adaptation
http://arxiv.org/abs/2006.11486
AUTHORS:        Jinjia Peng ; Yang Wang ; Huibing Wang ; Zhao Zhang ; Xianping Fu ; Meng Wang
COMMENTS:       Appearing at IJCAI 2020
HIGHLIGHT:      To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations.

111, TITLE:     DO-Conv: Depthwise Over-parameterized Convolutional Layer
http://arxiv.org/abs/2006.12030
AUTHORS:        Jinming Cao ; Yangyan Li ; Mingchao Sun ; Ying Chen ; Dani Lischinski ; Daniel Cohen-Or ; Baoquan Chen ; Changhe Tu
HIGHLIGHT:      In this paper, we propose to augment a convolutional layer with an additional depthwise convolution, where each input channel is convolved with a different 2D kernel.

112, TITLE:     Metaheuristics for the Online Printing Shop Scheduling Problem
http://arxiv.org/abs/2006.12344
AUTHORS:        Willian T. Lunardi ; Ernesto G. Birgin ; Débora P. Ronconi ; Holger Voos
HIGHLIGHT:      In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, to appear) is considered.

113, TITLE:     Clinical Predictive Keyboard using Statistical and Neural Language Modeling
http://arxiv.org/abs/2006.12040
AUTHORS:        John Pavlopoulos ; Panagiotis Papapetrou
COMMENTS:       To appear in CBMS'20
HIGHLIGHT:      A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails).

114, TITLE:     Characterizing Hirability via Personality and Behavior
http://arxiv.org/abs/2006.12041
AUTHORS:        Harshit Malik ; Hersh Dhillon ; Roland Goecke ; Ramanathan Subramanian
COMMENTS:       9 pages
HIGHLIGHT:      Modeling hirability as a discrete/continuous variable with the \emph{big-five} personality traits as predictors, we utilize (a) apparent personality annotations, and (b) personality estimates obtained via audio, visual and textual cues for hirability prediction (HP).

115, TITLE:     Generative Sparse Detection Networks for 3D Single-shot Object Detection
http://arxiv.org/abs/2006.12356
AUTHORS:        JunYoung Gwak ; Christopher Choy ; Silvio Savarese
HIGHLIGHT:      To this end, we propose Generative Sparse Detection Network (GSDN), a fully-convolutional single-shot sparse detection network that efficiently generates the support for object proposals.

116, TITLE:     Few-shot 3D Point Cloud Semantic Segmentation
http://arxiv.org/abs/2006.12052
AUTHORS:        Na Zhao ; Tat-Seng Chua ; Gim Hee Lee
HIGHLIGHT:      To mitigate these limitations, we propose a novel attention-aware multi-prototype transductive few-shot point cloud semantic segmentation method to segment new classes given a few labeled examples.

117, TITLE:     Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"
http://arxiv.org/abs/2006.11524
AUTHORS:        Saeed Amizadeh ; Hamid Palangi ; Oleksandr Polozov ; Yichen Huang ; Kazuhito Koishida
COMMENTS:       To be published in Proceedings of the 37th International Conference on Machine Learning (ICML), Vienna, Austria, PMLR 108, 2020
HIGHLIGHT:      To address this, we propose (1) a framework to isolate and evaluate the reasoning aspect of VQA separately from its perception, and (2) a novel top-down calibration technique that allows the model to answer reasoning questions even with imperfect perception.

118, TITLE:     Remote Sensing Image Scene Classification with Deep Neural Networks in JPEG 2000 Compressed Domain
http://arxiv.org/abs/2006.11529
AUTHORS:        Akshara Preethy Byju ; Gencer Sumbul ; Begüm Demir ; Lorenzo Bruzzone
COMMENTS:       Accepted to IEEE Transactions on Geoscience and Remote Sensing
HIGHLIGHT:      To address this issue, in this paper we propose a novel approach to achieve scene classification in JPEG 2000 compressed RS images.

119, TITLE:     Memory Transformer
http://arxiv.org/abs/2006.11527
AUTHORS:        Mikhail S. Burtsev ; Grigory V. Sapunov
HIGHLIGHT:      In this work, we propose and study two extensions of the Transformer baseline (1) by adding memory tokens to store non-local representations, and (2) creating memory bottleneck for the global information.

120, TITLE:     Artificial intelligence in space
http://arxiv.org/abs/2006.12362
AUTHORS:        George Anthony Gal ; Cristiana Santos ; Lucien Rapp ; Réeka Markovich ; Leendert van der Torre
COMMENTS:       32 pages
HIGHLIGHT:      In the next coming years, space activities are expected to undergo a radical transformation with the emergence of new satellite systems or new services which will incorporate the contributions of artificial intelligence and machine learning defined as covering a wide range of innovations from autonomous objects with their own decision-making power to increasingly sophisticated services exploiting very large volumes of information from space.

121, TITLE:     ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion
http://arxiv.org/abs/2006.12146
AUTHORS:         BingningWang ; Ting Yao ; Qi Zhang ; Jingfang Xu ; Xiaochuan Wang
COMMENTS:       AAAI-2020 camera ready
HIGHLIGHT:      This paper presents the ReCO, a human-curated ChineseReading Comprehension dataset on Opinion.

122, TITLE:     IQA: Interactive Query Construction in Semantic Question Answering Systems
http://arxiv.org/abs/2006.11534
AUTHORS:        Hamid Zafara ; Mohnish Dubey ; Jens Lehmann ; Elena Demidova
HIGHLIGHT:      In this article, we aim to empower users in guiding QA systems towards the intended semantic queries by means of interaction.

123, TITLE:     On Addressing the Impact of ISO Speed upon PRNU and Forgery Detection
http://arxiv.org/abs/2006.11539
AUTHORS:        Yijun Quan ; Chang-Tsun Li
COMMENTS:       The paper is accepted to IEEE Transactions on Information Forensics and Security with the supplementary material
HIGHLIGHT:      In this work, we will show the PRNU correlation's dependency on ISO speed.

124, TITLE:     generating annotated high-fidelity images containing multiple coherent objects
http://arxiv.org/abs/2006.12150
AUTHORS:        B. G. Cardenas ; A. Devanshu ; D. K. Gupta
COMMENTS:       21 pages, 5 tables, 21 figures
HIGHLIGHT:      In this work, we propose a multi-object generation framework that can synthesize images with multiple objects without explicitly requiring their contextual information during the generation process.

125, TITLE:     Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
http://arxiv.org/abs/2006.11538
AUTHORS:        Ionut Cosmin Duta ; Li Liu ; Fan Zhu ; Ling Shao
HIGHLIGHT:      This work introduces pyramidal convolution (PyConv), which is capable of processing the input at multiple filter scales.

126, TITLE:     Neural Cellular Automata Manifold
http://arxiv.org/abs/2006.12155
AUTHORS:        Alejandro Hernandez Ruiz ; Armand Vilalta ; Francesc Moreno-Noguer
HIGHLIGHT:      In this paper, we move a step further and propose a new model that extends the expressive power of NCA from a single image to an manifold of images.

127, TITLE:     First Steps Towards a Runtime Analysis When Starting With a Good Solution
http://arxiv.org/abs/2006.12161
AUTHORS:        Denis Antipov ; Maxim Buzdalov ; Benjamin Doerr
COMMENTS:       The extended version of the PPSN 2020 conference paper
HIGHLIGHT:      We start a mathematical runtime analysis for such situations.

128, TITLE:     Bidirectional Self-Normalizing Neural Networks
http://arxiv.org/abs/2006.12169
AUTHORS:        Yao Lu ; Stephen Gould ; Thalaiyasingam Ajanthan
HIGHLIGHT:      In this paper, we address the problem from the perspective of high-dimensional probability theory.

129, TITLE:     MDR Cluster-Debias: A Nonlinear WordEmbedding Debiasing Pipeline
http://arxiv.org/abs/2006.11642
AUTHORS:        Yuhao Du ; Kenneth Joseph
HIGHLIGHT:      We identify two potential reasons for which residual bias exists and develop a new pipeline, MDR Cluster-Debias, to mitigate this bias.

130, TITLE:     Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies
http://arxiv.org/abs/2006.11645
AUTHORS:        Tsung-Yen Yang ; Justinian Rosca ; Karthik Narasimhan ; Peter J. Ramadge
HIGHLIGHT:      We propose an iterative algorithm for solving this problem.

131, TITLE:     The Importance of Category Labels in Grammar Induction with Child-directed Utterances
http://arxiv.org/abs/2006.11646
AUTHORS:        Lifeng Jin ; William Schuler
COMMENTS:       The 16th International Conference on Parsing Technologies (IWPT 2020)
HIGHLIGHT:      Experiments in this work using a labeled evaluation metric, RH, show that linguistically motivated predictions about grammar sparsity and use of categories can only be revealed through labeled evaluation.

132, TITLE:     BRULÉ: Barycenter-Regularized Unsupervised Landmark Extraction
http://arxiv.org/abs/2006.11643
AUTHORS:        Iaroslav Bespalov ; Nazar Buzun ; Dmitry V. Dylov
COMMENTS:       11 main pages with 5 figures and 1 Table, 13 pages total with 3 supplementary figures
HIGHLIGHT:      In this work, we propose a new unsupervised approach to detect the landmarks in images, and we validate it on the popular task of human face key-points extraction.

133, TITLE:     A numerical framework for elastic surface matching, comparison, and interpolation
http://arxiv.org/abs/2006.11652
AUTHORS:        Martin Bauer ; Nicolas Charon ; Philipp Harms ; Hsi-Wei Hsieh
COMMENTS:       33 pages, 10 figures, 1 table, 2 algorithms
HIGHLIGHT:      In this paper, we take an alternative approach which bypasses the direct estimation of reparametrizations: we relax the geodesic boundary constraint using an auxiliary parametrization-blind varifold fidelity metric.

134, TITLE:     Towards Understanding Label Smoothing
http://arxiv.org/abs/2006.11653
AUTHORS:        Yi Xu ; Yuanhong Xu ; Qi Qian ; Hao Li ; Rong Jin
HIGHLIGHT:      In this paper, we analyze the convergence behaviors of stochastic gradient descent with LSR for solving non-convex problems and show that an appropriate LSR can help to speed up the convergence by reducing the variance of labels.

135, TITLE:     Adversarial Transfer of Pose Estimation Regression
http://arxiv.org/abs/2006.11658
AUTHORS:        Boris Chidlovskii Assem Sadek
HIGHLIGHT:      We identify the dataset shift an important barrier to generalization and consider transfer learning as an alternative way towards a better reuse of pose estimation models.

136, TITLE:     Collective Learning by Ensembles of Altruistic Diversifying Neural Networks
http://arxiv.org/abs/2006.11671
AUTHORS:        Benjamin Brazowski ; Elad Schneidman
HIGHLIGHT:      We therefore present a model for co-learning by ensembles of interacting neural networks that aim to maximize their own performance but also their functional relations to other networks.

137, TITLE:     Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample
http://arxiv.org/abs/2006.12226
AUTHORS:        Shir Gur ; Sagie Benaim ; Lior Wolf
HIGHLIGHT:      We introduce a novel patch-based variational autoencoder (VAE) which allows for a much greater diversity in generation.

138, TITLE:     Quantile-Quantile Embedding for Distribution Transformation, Manifold Embedding, and Image Embedding with Choice of Embedding Distribution
http://arxiv.org/abs/2006.11385
AUTHORS:        Benyamin Ghojogh ; Fakhri Karray ; Mark Crowley
COMMENTS:       The proposed QQE method can be used for manifold embedding and image embedding with choice of embedding distribution. Giving freedom to user to choose the embedding distribution of (embedded) data is the contribution of this paper. QQE is based on the quantile-quantile plot (qq-plot) used in the visual statistical tests
HIGHLIGHT:      We propose a new embedding method, named Quantile-Quantile Embedding (QQE), for distribution transformation, manifold embedding, and image embedding with the ability to choose the embedding distribution.

139, TITLE:     ResFPN: Residual Skip Connections in Multi-Resolution Feature Pyramid Networks for Accurate Dense Pixel Matching
http://arxiv.org/abs/2006.12235
AUTHORS:         Rishav ; René Schuster ; Ramy Battrawy ; Oliver Wasenmüller ; Didier Stricker
COMMENTS:       Accepted at ICPR 2020
HIGHLIGHT:      Thus, we present ResFPN -- a multi-resolution feature pyramid network with multiple residual skip connections, where at any scale, we leverage the information from higher resolution maps for stronger and better localized features.

140, TITLE:     HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
http://arxiv.org/abs/2006.12230
AUTHORS:        Mart van Rijthoven ; Maschenka Balkenhol ; Karina Siliņa ; Jeroen van der Laak ; Francesco Ciompi
HIGHLIGHT:      We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks.

141, TITLE:     Shared Task on Evaluating Accuracy in Natural Language Generation
http://arxiv.org/abs/2006.12234
AUTHORS:        Ehud Reiter ; Craig Thomson
HIGHLIGHT:      We propose a shared task on methodologies and algorithms for evaluating the accuracy of generated texts.

142, TITLE:     Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-based Image Retrieval
http://arxiv.org/abs/2006.11397
AUTHORS:        Anjan Dutta ; Zeynep Akata
COMMENTS:       In International Journal of Computer Vision (IJCV) 2020 (17 pages, 12 figures, 5 tables). arXiv admin note: substantial text overlap with arXiv:1903.03372
HIGHLIGHT:      In this paper, we address any-shot, i.e. zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks, where we introduce the few-shot setting for SBIR.

143, TITLE:     Advantages of biologically-inspired adaptive neural activation in RNNs during learning
http://arxiv.org/abs/2006.12253
AUTHORS:        Victor Geadah ; Giancarlo Kerg ; Stefan Horoi ; Guy Wolf ; Guillaume Lajoie
HIGHLIGHT:      In this paper, we investigate nonlinear activation function adaptation over the large time scale of learning, and outline its impact on sequential processing in recurrent neural networks.

144, TITLE:     Pix2Vox++: Multi-scale Context-aware 3D Object Reconstruction from Single and Multiple Images
http://arxiv.org/abs/2006.12250
AUTHORS:        Haozhe Xie ; Hongxun Yao ; Shengping Zhang ; Shangchen Zhou ; Wenxiu Sun
COMMENTS:       International Journal of Computer Vision (IJCV).arXiv admin note: text overlap with arXiv:1901.11153
HIGHLIGHT:      To address these issues, we propose a novel framework for single-view and multi-view 3D object reconstruction, named Pix2Vox++.

145, TITLE:     FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network
http://arxiv.org/abs/2006.12263
AUTHORS:        Lingtong Kong ; Jie Yang
COMMENTS:       Accepted by ICIP 2020
HIGHLIGHT:      In this work, we present a lightweight yet effective model for real-time optical flow estimation, termed FDFlowNet (fast deep flownet).

146, TITLE:     MedLatin1 and MedLatin2: Two Datasets for the Computational Authorship Analysis of Medieval Latin Texts
http://arxiv.org/abs/2006.12289
AUTHORS:        Silvia Corbara ; Alejandro Moreo ; Fabrizio Sebastiani ; Mirko Tavoni
HIGHLIGHT:      We present and make available MedLatin1 and MedLatin2, two datasets of medieval Latin texts to be used in research on computational authorship analysis.

147, TITLE:     Learning Physical Graph Representations from Visual Scenes
http://arxiv.org/abs/2006.12373
AUTHORS:        Daniel M. Bear ; Chaofei Fan ; Damian Mrowca ; Yunzhu Li ; Seth Alter ; Aran Nayebi ; Jeremy Schwartz ; Li Fei-Fei ; Jiajun Wu ; Joshua B. Tenenbaum ; Daniel L. K. Yamins
COMMENTS:       23 pages
HIGHLIGHT:      To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts.

148, TITLE:     One PLOT to Show Them All: Visualization of Efficient Sets in Multi-Objective Landscapes
http://arxiv.org/abs/2006.11547
AUTHORS:        Lennart Schäpermeier ; Christian Grimme ; Pascal Kerschke
COMMENTS:       This version has been accepted for publication at the 16th International Conference on Parallel Problem Solving from Nature (PPSN XVI)
HIGHLIGHT:      In this paper, we propose a new and hybrid visualization technique, which combines the advantages of both approaches in order to represent local and global optimality together within a single visualization.

149, TITLE:     Named Entity Extraction with Finite State Transducers
http://arxiv.org/abs/2006.11548
AUTHORS:        Diego Alexander Huérfano Villalba ; Elizabeth León Guzmán
HIGHLIGHT:      We describe a named entity tagging system that requires minimal linguistic knowledge and can be applied to more target languages without substantial changes.

150, TITLE:     A Modular Hybridization of Particle Swarm Optimization and Differential Evolution
http://arxiv.org/abs/2006.11886
AUTHORS:        Rick Boks ; Hao Wang ; Thomas Bäck
COMMENTS:       8 pages, 1 figure, to be published in GECCO 2020 Companion
HIGHLIGHT:      In detail, we consider 16 different variation operators originating from existing PSO- and DE algorithms, which, combined with 4 different selection operators, allow the hybridization framework to generate 800 novel algorithms.

151, TITLE:     Driver Intention Anticipation Based on In-Cabin and Driving SceneMonitoring
http://arxiv.org/abs/2006.11557
AUTHORS:        Yao Rong ; Zeynep Akata ; Enkelejda Kasneci
COMMENTS:       8 pages, 9 figures
HIGHLIGHT:      Since the outside view from the traffic scene may also contain informative features for driving maneuver prediction, we present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos.

152, TITLE:     Seq2Seq and Joint Learning Based Unix Command Line Prediction System
http://arxiv.org/abs/2006.11558
AUTHORS:        Thoudam Doren Singh ; Abdullah Faiz Ur Rahman Khilji ;  Divyansha ; Apoorva Vikram Singh ; Surmila Thokchom ; Sivaji Bandyopadhyay
COMMENTS:       9 pages, 1 Figure
HIGHLIGHT:      This work describes an assistive, adaptive and dynamic way of enhancing UNIX command line prediction systems.

153, TITLE:     Learning Objective Boundaries for Constraint Optimization Problems
http://arxiv.org/abs/2006.11560
AUTHORS:        Helge Spieker ; Arnaud Gotlieb
COMMENTS:       The 6th International Conference on machine Learning, Optimization and Data science - LOD 2020
HIGHLIGHT:      This paper introduces Bion, a novel approach for boundary estimation by learning from previously solved instances of the COP.

154, TITLE:     A Symbolic Temporal Pooling method for Video-based Person Re-Identification
http://arxiv.org/abs/2006.11416
AUTHORS:        S V Aruna Kumar ; Ehsan Yaghoubi ; Hugo Proença
COMMENTS:       11 pages
HIGHLIGHT:      To alleviate this problem, this paper introduces a symbolic temporal pooling method, where frame-level features are represented in the distribution valued symbolic form, yielding from fitting an Empirical Cumulative Distribution Function (ECDF) to each feature.

155, TITLE:     Set-Invariant Constrained Reinforcement Learning with a Meta-Optimizer
http://arxiv.org/abs/2006.11419
AUTHORS:        Chuangchuang Sun ; Dong-Ki Kim ; Jonathan P. How
HIGHLIGHT:      To address this, we propose to learn a neural network-based meta-optimizer to optimize the objective while satisfying such linear constraints.

156, TITLE:     A Multiparametric Class of Low-complexity Transforms for Image and Video Coding
http://arxiv.org/abs/2006.11418
AUTHORS:        D. R. Canterle ; T. L. T. da Silveira ; F. M. Bayer ; R. J. Cintra
COMMENTS:       Fixed Figure 1 and typos in the reference list
HIGHLIGHT:      In this paper, we introduce a new class of low-complexity 8-point DCT approximations based on a series of works published by Bouguezel, Ahmed and Swamy.

157, TITLE:     Dirichlet-Smoothed Word Embeddings for Low-Resource Settings
http://arxiv.org/abs/2006.12414
AUTHORS:        Jakob Jungmaier ; Nora Kassner ; Benjamin Roth
HIGHLIGHT:      But these methods are usually applied using very large amounts of text data.

158, TITLE:     Capturing Video Frame Rate Variations through Entropic Differencing
http://arxiv.org/abs/2006.11424
AUTHORS:        Pavan C. Madhusudana ; Neil Birkbeck ; Yilin Wang ; Balu Adsumilli ; Alan C. Bovik
HIGHLIGHT:      We show through extensive experiments that our model correlates very well with subjective scores in the HFR database and achieves state of the art performance when compared with existing methodologies.

159, TITLE:     A Step Towards Interpretable Authorship Verification
http://arxiv.org/abs/2006.12418
AUTHORS:        Oren Halvani ; Lukas Graner ; Roey Regev
COMMENTS:       19 pages, 5 figures
HIGHLIGHT:      To address this problem, we propose an alternative AV approach that considers only topic-agnostic features in its classification decision.

160, TITLE:     SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
http://arxiv.org/abs/2006.11572
AUTHORS:        Ekaterina Vylomova ; Jennifer White ; Elizabeth Salesky ; Sabrina J. Mielke ; Shijie Wu ; Edoardo Ponti ; Rowan Hall Maudslay ; Ran Zmigrod ; Josef Valvoda ; Svetlana Toldova ; Francis Tyers ; Elena Klyachko ; Ilya Yegorov ; Natalia Krizhanovsky ; Paula Czarnowska ; Irene Nikkarinen ; Andrew Krizhanovsky ; Tiago Pimentel ; Lucas Torroba Hennigen ; Christo Kirov ; Garrett Nicolai ; Adina Williams ; Antonios Anastasopoulos ; Hilaria Cruz ; Eleanor Chodroff ; Ryan Cotterell ; Miikka Silfverberg ; Mans Hulden
COMMENTS:       39 pages, SIGMORPHON
HIGHLIGHT:      SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection

161, TITLE:     BEV-Seg: Bird's Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud
http://arxiv.org/abs/2006.11436
AUTHORS:        Mong H. Ng ; Kaahan Radia ; Jianfei Chen ; Dequan Wang ; Ionel Gog ; Joseph E. Gonzalez
HIGHLIGHT:      In this work, we focus on bird's eye semantic segmentation, a task that predicts pixel-wise semantic segmentation in BEV from side RGB images.

162, TITLE:     Band-limited Soft Actor Critic Model
http://arxiv.org/abs/2006.11431
AUTHORS:        Miguel Campo ; Zhengxing Chen ; Luke Kung ; Kittipat Virochsiri ; Jianyu Wang
COMMENTS:       8 pages plus additional material
HIGHLIGHT:      We derive the closed form solution in the linear case and show that bandlimiting reduces the interdependency between the low and high frequency components of the state-action value approximation, allowing the critic to learn faster.

163, TITLE:     Learning aligned embeddings for semi-supervised word translation using Maximum Mean Discrepancy
http://arxiv.org/abs/2006.11578
AUTHORS:        Antonio H. O. Fonseca ; David van Dijk
HIGHLIGHT:      Here we propose an end-to-end approach for word embedding alignment that does not require known word pairs.

164, TITLE:     Deep Negative Volume Segmentation
http://arxiv.org/abs/2006.12430
AUTHORS:        Kristina Belikova ; Oleg Rogov ; Aleksandr Rybakov ; Maxim V. Maslov ; Dmitry V. Dylov
COMMENTS:       20 pages, 5 main figures, 3 tables, 11 supplemental figures, supplementary material
HIGHLIGHT:      To address the challenge, we invent a new angle to the 3D segmentation task: namely, we propose to segment empty spaces between all the tissues surrounding the object - the so-called negative volume segmentation.

165, TITLE:     AraDIC: Arabic Document Classification using Image-Based Character Embeddings and Class-Balanced Loss
http://arxiv.org/abs/2006.11586
AUTHORS:        Mahmoud Daif ; Shunsuke Kitada ; Hitoshi Iyatomi
HIGHLIGHT:      We propose a novel end-to-end Arabic document classification framework, Arabic document image-based classifier (AraDIC), inspired by the work on image-based character embeddings.

166, TITLE:     Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
http://arxiv.org/abs/2006.12434
AUTHORS:        Xiahai Zhuang ; Jiahang Xu ; Xinzhe Luo ; Chen Chen ; Cheng Ouyang ; Daniel Rueckert ; Victor M. Campello ; Karim Lekadir ; Sulaiman Vesal ; Nishant RaviKumar ; Yashu Liu ; Gongning Luo ; Jingkun Chen ; Hongwei Li ; Buntheng Ly ; Maxime Sermesant ; Holger Roth ; Wentao Zhu ; Jiexiang Wang ; Xinghao Ding ; Xinyue Wang ; Sen Yang ; Lei Li
COMMENTS:       14 pages
HIGHLIGHT:      This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019.

167, TITLE:     To Explain or Not to Explain: A Study on the Necessity of Explanations for Autonomous Vehicles
http://arxiv.org/abs/2006.11684
AUTHORS:        Yuan Shen ; Shanduojiao Jiang ; Yanlin Chen ; Eileen Yang ; Xilun Jin ; Yuliang Fan ; Katie Driggs Campbell
COMMENTS:       9.5 pages, 7 figures, submitted to UIST2020
HIGHLIGHT:      In this work, we investigate which scenarios people need explanations and how the critical degree of explanation shifts with situations and driver types.

168, TITLE:     Semi-Supervised Object Detection with Sparsely Annotated Dataset
http://arxiv.org/abs/2006.11692
AUTHORS:        Jihun Yoon ; Seungbum Hong ; Sanha Jeong ; Min-Kook Choi
COMMENTS:       Challenge Winner in Epic-Kitchens 2020 Object Detection Challenge (EPIC@CVPR 2020)
HIGHLIGHT:      We used two approaches to solve this problem: 1) the use of an anchorless object detector and 2) a semi-supervised learning-based object detection using a single object tracker.

169, TITLE:     Dense-Captioning Events in Videos: SYSU Submission to ActivityNet Challenge 2020
http://arxiv.org/abs/2006.11693
AUTHORS:        Teng Wang ; Huicheng Zheng ; Mingjing Yu
COMMENTS:       technical report, 4 pages, 2 figures
HIGHLIGHT:      This technical report presents a brief description of our submission to the dense video captioning task of ActivityNet Challenge 2020.

170, TITLE:     Fast and Accurate: Structure Coherence Component for Face Alignment
http://arxiv.org/abs/2006.11697
AUTHORS:        Beier Zhu ; Chunze Lin ; Quan Wang ; Renjie Liao ; Chen Qian
HIGHLIGHT:      In this paper, we propose a fast and accurate coordinate regression method for face alignment.

171, TITLE:     Studying Attention Models in Sentiment Attitude Extraction Task
http://arxiv.org/abs/2006.11605
AUTHORS:        Nicolay Rusnachenko ; Natalia Loukachevitch
COMMENTS:       This is a preprint of an article published in the Proceedings of the 25th International Conference on Natural Language and Information Systems. The final authenticated publication is available online at https://doi.org/10.1007/978-3-030-51310-8_15
HIGHLIGHT:      In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task.

172, TITLE:     How do SGD hyperparameters in natural training affect adversarial robustness?
http://arxiv.org/abs/2006.11604
AUTHORS:        Sandesh Kamath ; Amit Deshpande ; K V Subrahmanyam
COMMENTS:       Preliminary version presented in ICML 2019 Workshop on "Understanding and Improving Generalization in Deep Learning" as "On Adversarial Robustness of Small vs Large Batch Training"
HIGHLIGHT:      In the same paper, the authors train models with different batch sizes and compute the eigenvalues of the Hessian of loss function.

173, TITLE:     Real-time Pupil Tracking from Monocular Video for Digital Puppetry
http://arxiv.org/abs/2006.11341
AUTHORS:        Artsiom Ablavatski ; Andrey Vakunov ; Ivan Grishchenko ; Karthik Raveendran ; Matsvei Zhdanovich
HIGHLIGHT:      We present a simple, real-time approach for pupil tracking from live video on mobile devices.

174, TITLE:     Text Recognition in Real Scenarios with a Few Labeled Samples
http://arxiv.org/abs/2006.12209
AUTHORS:        Jinghuang Lin ; Zhanzhan Cheng ; Fan Bai ; Yi Niu ; Shiliang Pu ; Shuigeng Zhou
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      To tackle this challenging problem, this paper proposes a few-shot adversarial sequence domain adaptation (FASDA) approach to build sequence adaptation between the synthetic source domain (with many synthetic labeled samples) and a specific target domain (with only some or a few real labeled samples).

175, TITLE:     Optimal Statistical Hypothesis Testing for Social Choice
http://arxiv.org/abs/2006.11362
AUTHORS:        Lirong Xia
HIGHLIGHT:      We address the following question in this paper: "What are the most robust statistical methods for social choice?''

176, TITLE:     Facial Expression Editing with Continuous Emotion Labels
http://arxiv.org/abs/2006.12210
AUTHORS:        Alexandra Lindt ; Pablo Barros ; Henrique Siqueira ; Stefan Wermter
COMMENTS:       8 pages, 5 figures. 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), May 2019
HIGHLIGHT:      We propose a deep generative model that can be used to manipulate facial expressions in facial images according to continuous two-dimensional emotion labels.

177, TITLE:     Moore's Paradox and the logic of belief
http://arxiv.org/abs/2006.11363
AUTHORS:        Andrés Páez
HIGHLIGHT:      In this paper I argue that Hintikkas interpretation of one of the doxastic operators is philosophically problematic and leads to an unnecessarily strong logical system.

178, TITLE:     Hierarchical Reinforcement Learning for Deep Goal Reasoning: An Expressiveness Analysis
http://arxiv.org/abs/2006.11704
AUTHORS:        Weihang Yuan ; Héctor Muñoz-Avila
HIGHLIGHT:      We describe the recurrent hierarchical framework (RHF), generalizing architectures that use a recurrent neural network at the meta level.

179, TITLE:     A Universal Representation Transformer Layer for Few-Shot Image Classification
http://arxiv.org/abs/2006.11702
AUTHORS:        Lu Liu ; William Hamilton ; Guodong Long ; Jing Jiang ; Hugo Larochelle
HIGHLIGHT:      We analyze variants of URT and present a visualization of the attention score heatmaps that sheds light on how the model performs cross-domain generalization.

180, TITLE:     Mapping Low-Resolution Images To Multiple High-Resolution Images Using Non-Adversarial Mapping
http://arxiv.org/abs/2006.11708
AUTHORS:        Vasileios Lioutas
COMMENTS:       Paper completed in April 2019
HIGHLIGHT:      Several methods have recently been proposed for the Single Image Super-Resolution (SISR) problem.

181, TITLE:     Exploiting Contextual Information with Deep Neural Networks
http://arxiv.org/abs/2006.11706
AUTHORS:        Ismail Elezi
COMMENTS:       Ph.D. thesis
HIGHLIGHT:      In this thesis, we show that contextual information can be exploited in 2 fundamentally different ways: implicitly and explicitly.

182, TITLE:     Learning compact generalizable neural representations supporting perceptual grouping
http://arxiv.org/abs/2006.11716
AUTHORS:        Vijay Veerabadran ; Virginia R. de Sa
HIGHLIGHT:      We introduce V1Net, a bio-inspired recurrent unit that incorporates lateral connections ubiquitous in cortical circuitry.

183, TITLE:     Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation
http://arxiv.org/abs/2006.11714
AUTHORS:        Shiyang Yan ; Yang Hua ; Neil M. Robertson
HIGHLIGHT:      Recently, several approaches have been proposed to solve language generation problems.

184, TITLE:     Match$^2$: A Matching over Matching Model for Similar Question Identification
http://arxiv.org/abs/2006.11719
AUTHORS:        Zizhen Wang ; Yixing Fan ; Jiafeng Guo ; Liu Yang ; Ruqing Zhang ; Yanyan Lan ; Xueqi Cheng ; Hui Jiang ; Xiaozhao Wang
COMMENTS:       Accepted by SIGIR 2020. 10 pages
HIGHLIGHT:      In this work, we propose a two-side usage, which leverages the answer as a bridge of the two questions.

185, TITLE:     Pose Trainer: Correcting Exercise Posture using Pose Estimation
http://arxiv.org/abs/2006.11718
AUTHORS:        Steven Chen ; Richard R. Yang
HIGHLIGHT:      In our work, we introduce Pose Trainer, an application that detects the user's exercise pose and provides personalized, detailed recommendations on how the user can improve their form.

186, TITLE:     Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
http://arxiv.org/abs/2006.11438
AUTHORS:        Sheng Li ; Jayesh K. Gupta ; Peter Morales ; Ross Allen ; Mykel J. Kochenderfer
HIGHLIGHT:      This paper introduces the deep implicit coordination graph (DICG) architecture for such scenarios.

187, TITLE:     Optimising Monotone Chance-Constrained Submodular Functions Using Evolutionary Multi-Objective Algorithms
http://arxiv.org/abs/2006.11444
AUTHORS:        Aneta Neumann ; Frank Neumann
COMMENTS:       Paper accepted for publication in the proceedings of PPSN 2020
HIGHLIGHT:      We present a first runtime analysis of evolutionary multi-objective algorithms for chance-constrained submodular functions.

188, TITLE:     Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
http://arxiv.org/abs/2006.11441
AUTHORS:        Mengdi Xu ; Wenhao Ding ; Jiacheng Zhu ; Zuxin Liu ; Baiming Chen ; Ding Zhao
COMMENTS:       16 pages, 6 figures
HIGHLIGHT:      This paper proposes a continual online model-based reinforcement learning approach that does not require pre-training to solve task-agnostic problems with unknown task boundaries.

189, TITLE:     MALOnt: An Ontology for Malware Threat Intelligence
http://arxiv.org/abs/2006.11446
AUTHORS:        Nidhi Rastogi ; Sharmishtha Dutta ; Mohammed J. Zaki ; Alex Gittens ; Charu Aggarwal
HIGHLIGHT:      In this paper, we introduce an open-source malware ontology - MALOnt that allows the structured extraction of information and knowledge graph generation, especially for threat intelligence.

190, TITLE:     Towards Adaptive Benthic Habitat Mapping
http://arxiv.org/abs/2006.11453
AUTHORS:        Jackson Shields ; Oscar Pizarro ; Stefan B. Williams
COMMENTS:       To be published in ICRA2020 conference proceedings. 6 pages, 7 figures
HIGHLIGHT:      Using these two data sources, the relationship between remotely-sensed acoustic data and the sampled imagery can be learned, creating a habitat model.

191, TITLE:     Optimal Mutation Rates for the $(1+λ)$ EA on OneMax
http://arxiv.org/abs/2006.11457
AUTHORS:        Maxim Buzdalov ; Carola Doerr
COMMENTS:       This is an extended version of the paper accepted to the PPSN 2020 conference
HIGHLIGHT:      In this work, we extend the analysis of optimal mutation rates to two variants of the $(1+\lambda)$ EA and to the $(1+\lambda)$ RLS.

192, TITLE:     Visualising Evolution History in Multi- and Many-Objective Optimisation
http://arxiv.org/abs/2006.12309
AUTHORS:        Mathew Walter ; David Walker ; Matthew Craven
HIGHLIGHT:      We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape.

193, TITLE:     Towards a self-organizing pre-symbolic neural model representing sensorimotor primitives
http://arxiv.org/abs/2006.11465
AUTHORS:        Junpei Zhong ; Angelo Cangelosi ; Stefan Wermter
HIGHLIGHT:      We propose a model that relates the conceptualization of the higher-level information from visual stimuli to the development of ventral/dorsal visual streams.



==========Updates to Previous Papers==========
1, TITLE:       Investigating naturalistic hand movements by behavior mining in long-term video and neural recordings
http://arxiv.org/abs/2001.08349
AUTHORS:        Satpreet H. Singh ; Steven M. Peterson ; Rajesh P. N. Rao ; Bingni W. Brunton
HIGHLIGHT:      Here we describe an automated approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and naturalistic behavior video data. We publicly release our curated dataset, providing a resource to study naturalistic neural and behavioral variability at a scale not previously available.

2, TITLE:       No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium
http://arxiv.org/abs/2004.00603
AUTHORS:        Andrea Celli ; Alberto Marchesi ; Gabriele Farina ; Nicola Gatti
HIGHLIGHT:      In this paper, we give the first uncoupled no-regret dynamics that converge to the set of EFCEs in $n$-player general-sum extensive-form games with perfect recall.

3, TITLE:       Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks
http://arxiv.org/abs/2002.10085
AUTHORS:        Wenrui Zhang ; Peng Li
HIGHLIGHT:      We present a novel Temporal Spike Sequence Learning Backpropagation (TSSL-BP) method for training deep SNNs, which breaks down error backpropagation across two types of inter-neuron and intra-neuron dependencies and leads to improved temporal learning precision.

4, TITLE:       Continuous Control for Searching and Planning with a Learned Model
http://arxiv.org/abs/2006.07430
AUTHORS:        Xuxi Yang ; Werner Duvaud ; Peng Wei
HIGHLIGHT:      In this paper, we provide a way and the necessary theoretical results to extend the MuZero algorithm to more generalized environments with continuous action space.

5, TITLE:       Revisiting Convolutional Neural Networks for Citywide Crowd Flow Analytics
http://arxiv.org/abs/2003.00895
AUTHORS:        Yuxuan Liang ; Kun Ouyang ; Yiwei Wang ; Ye Liu ; Junbo Zhang ; Yu Zheng ; David S. Rosenblum
COMMENTS:       to appear at ECML-PKDD 2020
HIGHLIGHT:      To tackle these challenges, in this paper we present a novel framework entitled DeepLGR that can be easily generalized to address various citywide crowd flow analytics problems.

6, TITLE:       Learning to combine primitive skills: A step towards versatile robotic manipulation
http://arxiv.org/abs/1908.00722
AUTHORS:        Robin Strudel ; Alexander Pashevich ; Igor Kalevatykh ; Ivan Laptev ; Josef Sivic ; Cordelia Schmid
COMMENTS:       ICRA 2020. See the project webpage at https://www.di.ens.fr/willow/research/rlbc/
HIGHLIGHT:      In this work we aim to overcome previous limitations and propose a reinforcement learning (RL) approach to task planning that learns to combine primitive skills.

7, TITLE:       Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
http://arxiv.org/abs/1804.08313
AUTHORS:        Diego Marcheggiani ; Jasmijn Bastings ; Ivan Titov
HIGHLIGHT:      In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation.

8, TITLE:       Explorable Super Resolution
http://arxiv.org/abs/1912.01839
AUTHORS:        Yuval Bahat ; Tomer Michaeli
HIGHLIGHT:      In this paper, we introduce the task of explorable super resolution.

9, TITLE:       RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
http://arxiv.org/abs/1911.04942
AUTHORS:        Bailin Wang ; Richard Shin ; Xiaodong Liu ; Oleksandr Polozov ; Matthew Richardson
COMMENTS:       ACL 2020 camera-ready; 12 pages, 5 figures, 7 tables. arXiv admin note: text overlap with arXiv:1906.11790
HIGHLIGHT:      We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.

10, TITLE:      AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors
http://arxiv.org/abs/2006.01765
AUTHORS:        Matthew Z. Wong ; Benoit Guillard ; Riku Murai ; Sajad Saeedi ; Paul H. J. Kelly
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      In this work, we show how to convert standard CNNs to FPSP code, and demonstrate a method of training networks to increase their robustness to analog computation errors.

11, TITLE:      Deep Context Maps: Agent Trajectory Prediction using Location-specific Latent Maps
http://arxiv.org/abs/1912.06785
AUTHORS:        Igor Gilitschenski ; Guy Rosman ; Arjun Gupta ; Sertac Karaman ; Daniela Rus
HIGHLIGHT:      In this paper, we propose a novel approach for agent motion prediction in cluttered environments.

12, TITLE:      PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks
http://arxiv.org/abs/2003.11110
AUTHORS:        Junfeng Guo ; Ting Wang ; Cong Liu
HIGHLIGHT:      In this paper, we present PoisHygiene, the first effective and robust detection and mitigation framework against AP attacks.

13, TITLE:      Object Detection in the DCT Domain: is Luminance the Solution?
http://arxiv.org/abs/2006.05732
AUTHORS:        Benjamin Deguerre ; Clement Chatelain ; Gilles Gasso
COMMENTS:       ICPR 2020
HIGHLIGHT:      To gain in efficiency, this paper proposes to take advantage of the compressed representation of images to carry out object detection usable in constrained resources conditions.

14, TITLE:      Mining Public Opinion on Twitter about Natural Disaster Response Using Machine Learning Techniques
http://arxiv.org/abs/2005.07019
AUTHORS:        Zhijie Sasha Dong ; Lingyu Meng ; Lauren Christenson ; Lawrence Fulton
COMMENTS:       27 pages, 12 figures
HIGHLIGHT:      Results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster response.

15, TITLE:      Ultrafast Photorealistic Style Transfer via Neural Architecture Search
http://arxiv.org/abs/1912.02398
AUTHORS:        Jie An ; Haoyi Xiong ; Jun Huan ; Jiebo Luo
HIGHLIGHT:      In this work, we propose an effective solution to these issues.

16, TITLE:      Coreference Resolution as Query-based Span Prediction
http://arxiv.org/abs/1911.01746
AUTHORS:        Wei Wu ; Fei Wang ; Arianna Yuan ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we present an accurate and extensible approach for the coreference resolution task.

17, TITLE:      Multi-domain Dialogue State Tracking as Dynamic Knowledge Graph Enhanced Question Answering
http://arxiv.org/abs/1911.06192
AUTHORS:        Li Zhou ; Kevin Small
HIGHLIGHT:      In this paper, we propose to model multi-domain DST as a question answering problem, referred to as Dialogue State Tracking via Question Answering (DSTQA).

18, TITLE:      SensitiveNets: Learning Agnostic Representations with Application to Face Images
http://arxiv.org/abs/1902.00334
AUTHORS:        Aythami Morales ; Julian Fierrez ; Ruben Vera-Rodriguez
HIGHLIGHT:      This work proposes a novel neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data. In addition, we present a new annotation dataset with balanced distribution between genders and ethnic origins.

19, TITLE:      A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics
http://arxiv.org/abs/1912.08577
AUTHORS:        Aiqing Fang ; Xinbo Zhao ; Jiaqi Yang ; Yanning Zhang
HIGHLIGHT:      Inspired by the characteristics of human visual perception, we propose a robust multi-task auxiliary learning optimization image fusion theory.

20, TITLE:      Code and Named Entity Recognition in StackOverflow
http://arxiv.org/abs/2005.01634
AUTHORS:        Jeniya Tabassum ; Mounica Maddela ; Wei Xu ; Alan Ritter
COMMENTS:       updated with better results. (To appear in ACL 2020)
HIGHLIGHT:      In this paper, we introduce a new named entity recognition (NER) corpus for the computer programming domain, consisting of 15,372 sentences annotated with 20 fine-grained entity types.

21, TITLE:      Enhanced nonconvex low-rank approximation of tensor multi-modes for tensor completion
http://arxiv.org/abs/2005.14521
AUTHORS:        Haijin Zeng ; Xiaozhen Xie ; Jifeng Ning
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:2004.08747
HIGHLIGHT:      To alleviate the above problems, in this paper, we propose a novel low-rank approximation of tensor multi-modes (LRATM), in which a double nonconvex $L_{\gamma}$ norm is designed to represent the underlying joint-manifold drawn from the modal factorization factors of the underlying tensor.

22, TITLE:      On Learning and Learned Data Representation by Capsule Networks
http://arxiv.org/abs/1810.04041
AUTHORS:        Ancheng Lin ; Jun Li ; Zhenyuan Ma
HIGHLIGHT:      In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks.

23, TITLE:      Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
http://arxiv.org/abs/2001.11003
AUTHORS:        Leonardo F. R. Ribeiro ; Yue Zhang ; Claire Gardent ; Iryna Gurevych
COMMENTS:       Accepted for publication in Transactions of the Association for Computational Linguistics (TACL), 2020; Author's final version; pre-MIT Press publication version
HIGHLIGHT:      In this work, we gather both encoding strategies, proposing novel neural models which encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings.

24, TITLE:      Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation
http://arxiv.org/abs/2002.09103
AUTHORS:        Dmitry Molchanov ; Alexander Lyzhov ; Yuliya Molchanova ; Arsenii Ashukha ; Dmitry Vetrov
HIGHLIGHT:      The primary goal of this paper is to demonstrate that test-time augmentation policies can be successfully learned too.

25, TITLE:      A Cluster Ranking Model for Full Anaphora Resolution
http://arxiv.org/abs/1911.09532
AUTHORS:        Juntao Yu ; Alexandra Uma ; Massimo Poesio
COMMENTS:       LREC 2020
HIGHLIGHT:      In this paper, we introduce an architecture to simultaneously identify non-referring expressions (including expletives, predicative s, and other types) and build coreference chains, including singletons.

26, TITLE:      Preliminary Forensics Analysis of DeepFake Images
http://arxiv.org/abs/2004.12626
AUTHORS:        Luca Guarnera ; Oliver Giudice ; Cristina Nastasi ; Sebastiano Battiato
HIGHLIGHT:      This paper will present a brief overview of technologies able to produce DeepFake images of faces.

27, TITLE:      Reinforcement Learning as Iterative and Amortised Inference
http://arxiv.org/abs/2006.10524
AUTHORS:        Beren Millidge ; Alexander Tschantz ; Anil K Seth ; Christopher L Buckley
COMMENTS:       initial upload
HIGHLIGHT:      In this paper, we utilise the control as inference framework to outline a novel classification scheme based on amortised and iterative inference.

28, TITLE:      Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles
http://arxiv.org/abs/2005.00610
AUTHORS:        Joris M. Mooij ; Tom Claassen
COMMENTS:       Major revision. To appear in Proceedings of the 36 th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 2020
HIGHLIGHT:      In this work, we show that---surprisingly---the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback.

29, 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).

30, TITLE:      Where is the Information in a Deep Neural Network?
http://arxiv.org/abs/1905.12213
AUTHORS:        Alessandro Achille ; Giovanni Paolini ; Stefano Soatto
HIGHLIGHT:      We establish a novel relation between the information in the weights and the effective information in the activations, and use this result to show that models with low (information) complexity not only generalize better, but are bound to learn invariant representations of future inputs.

31, TITLE:      Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
http://arxiv.org/abs/1807.05648
AUTHORS:        Euijoon Ahn ; Jinman Kim ; Ashnil Kumar ; Michael Fulham ; Dagan Feng
COMMENTS:       Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005)
HIGHLIGHT:      We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data.

32, TITLE:      AMPSO: Artificial Multi-Swarm Particle Swarm Optimization
http://arxiv.org/abs/2004.07561
AUTHORS:        Haohao Zhou ; Zhi-Hui Zhan ; Zhi-Xin Yang ; 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.

33, TITLE:      An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
http://arxiv.org/abs/2005.10284
AUTHORS:        Arash Rahnama ; Andrew Tseng
HIGHLIGHT:      In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning.

34, TITLE:      Bandits with Temporal Stochastic Constraints
http://arxiv.org/abs/1811.09026
AUTHORS:        Priyank Agrawal ; Theja Tulabandhula
COMMENTS:       An extended abstract appeared in the 4th Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM 2019)
HIGHLIGHT:      We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it.

35, TITLE:      Local Facial Makeup Transfer via Disentangled Representation
http://arxiv.org/abs/2003.12065
AUTHORS:        Zhaoyang Sun ; Wenxuan Liu ; Feng Liu ; Ryan Wen Liu ; Shengwu Xiong
COMMENTS:       There's something wrong with the experiment. It's not complete
HIGHLIGHT:      In this paper, we propose a novel unified adversarial disentangling network to further decompose face images into four independent components, i.e., personal identity, lips makeup style, eyes makeup style and face makeup style.

36, TITLE:      EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets
http://arxiv.org/abs/2006.08369
AUTHORS:        Junhua Liu ; Trisha Singhal ; Lucienne T. M. Blessing ; Kristin L. Wood ; Kwan Hui Lim
HIGHLIGHT:      In this paper, we present EPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020.

37, TITLE:      LiStereo: Generate Dense Depth Maps from LIDAR and Stereo Imagery
http://arxiv.org/abs/1905.02744
AUTHORS:        Junming Zhang ; Manikandasriram Srinivasan Ramanagopalg ; Ram Vasudevan ; Matthew Johnson-Roberson
COMMENTS:       14 pages, 3 figures, 5 tables
HIGHLIGHT:      This paper combines these approaches together to generate high-quality dense depth maps.

38, TITLE:      Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled Representation
http://arxiv.org/abs/2004.02186
AUTHORS:        Edoardo Remelli ; Shangchen Han ; Sina Honari ; Pascal Fua ; Robert Wang
HIGHLIGHT:      We present a lightweight solution to recover 3D pose from multi-view images captured with spatially calibrated cameras.

39, TITLE:      Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications
http://arxiv.org/abs/2003.01455
AUTHORS:        Biagio Brattoli ; Joseph Tighe ; Fedor Zhdanov ; Pietro Perona ; Krzysztof Chalupka
COMMENTS:       Accepted for publication at CVPR 2020
HIGHLIGHT:      We propose the first end-to-end algorithm for ZSL in video classification.

40, TITLE:      Neural Mention Detection
http://arxiv.org/abs/1907.12524
AUTHORS:        Juntao Yu ; Bernd Bohnet ; Massimo Poesio
COMMENTS:       LREC 2020
HIGHLIGHT:      In this work, we propose and compare three neural network-based approaches to mention detection.

41, TITLE:      Folding-based compression of point cloud attributes
http://arxiv.org/abs/2002.04439
AUTHORS:        Maurice Quach ; Giuseppe Valenzise ; Frederic Dufaux
COMMENTS:       Published in ICIP 2020. The source code can be found at https://github.com/mauriceqch/pcc_attr_folding
HIGHLIGHT:      In this work, we consider point cloud attribute compression; thus, we compress this image with a conventional 2D image codec.

42, TITLE:      Inducing Hierarchical Compositional Model by Sparsifying Generator Network
http://arxiv.org/abs/1909.04324
AUTHORS:        Xianglei Xing ; Tianfu Wu ; Song-Chun Zhu ; Ying Nian Wu
COMMENTS:       This is the CVPR version
HIGHLIGHT:      This paper proposes to learn hierarchical compositional AND-OR model for interpretable image synthesis by sparsifying the generator network.

43, TITLE:      Learning Texture Transformer Network for Image Super-Resolution
http://arxiv.org/abs/2006.04139
AUTHORS:        Fuzhi Yang ; Huan Yang ; Jianlong Fu ; Hongtao Lu ; Baining Guo
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      In this paper, we propose a novel Texture Transformer Network for Image Super-Resolution (TTSR), in which the LR and Ref images are formulated as queries and keys in a transformer, respectively.

44, TITLE:      RAIN: A Simple Approach for Robust and Accurate Image Classification Networks
http://arxiv.org/abs/2004.14798
AUTHORS:        Jiawei Du ; Hanshu Yan ; Vincent Y. F. Tan ; Joey Tianyi Zhou ; Rick Siow Mong Goh ; Jiashi Feng
HIGHLIGHT:      We propose a novel defense framework, \emph{\underline{R}obust and \underline{A}ccurate \underline{I}mage classificatio\underline{N}} (RAIN), to improve the robustness of given CNN classifiers and, at the same time, preserve their high prediction accuracies.

45, TITLE:      An operator view of policy gradient methods
http://arxiv.org/abs/2006.11266
AUTHORS:        Dibya Ghosh ; Marlos C. Machado ; Nicolas Le Roux
HIGHLIGHT:      We cast policy gradient methods as the repeated application of two operators: a policy improvement operator $\mathcal{I}$, which maps any policy $\pi$ to a better one $\mathcal{I}\pi$, and a projection operator $\mathcal{P}$, which finds the best approximation of $\mathcal{I}\pi$ in the set of realizable policies.

46, TITLE:      On the Entanglement Cost of One-Shot Compression
http://arxiv.org/abs/1905.02110
AUTHORS:        Shima Bab Hadiashar ; Ashwin Nayak
COMMENTS:       21 pages, 1 figure
HIGHLIGHT:      We revisit the task of visible compression of an ensemble of quantum states with entanglement assistance in the one-shot setting.

47, TITLE:      Toward a Controllable Disentanglement Network
http://arxiv.org/abs/2001.08572
AUTHORS:        Zengjie Song ; Oluwasanmi Koyejo ; Jiangshe Zhang
COMMENTS:       Improved version of arXiv:1912.11675
HIGHLIGHT:      This paper addresses two crucial problems of learning disentangled image representations, namely controlling the degree of disentanglement during image editing, and balancing the disentanglement strength and the reconstruction quality.

48, TITLE:      Groups with ALOGTIME-hard word problems and PSPACE-complete compressed word problems
http://arxiv.org/abs/1909.13781
AUTHORS:        Laurent Bartholdi ; Michael Figelius ; Markus Lohrey ; Armin Weiß
COMMENTS:       A short version of the paper will appear in the Proceedings of the Computational Complexity Conference 2020
HIGHLIGHT:      We give lower bounds on the complexity of the word problem of certain non-solvable groups: for a large class of non-solvable infinite groups, including in particular free groups, Grigorchuk's group and Thompson's groups, we prove that their word problem is $\mathsf{NC}^1$-hard.

49, TITLE:      Artificial Intelligence-Based Image Classification for Diagnosis of Skin Cancer: Challenges and Opportunities
http://arxiv.org/abs/1911.11872
AUTHORS:        Manu Goyal ; Thomas Knackstedt ; Shaofeng Yan ; Saeed Hassanpour
COMMENTS:       AI Skin Cancer
HIGHLIGHT:      In this review, we discuss advancements in the digital image-based AI solutions for the diagnosis of skin cancer, along with some challenges and future opportunities to improve these AI systems to support dermatologists and enhance their ability to diagnose skin cancer.

50, TITLE:      A Complete Characterization of Projectivity for Statistical Relational Models
http://arxiv.org/abs/2004.10984
AUTHORS:        Manfred Jaeger ; Oliver Schulte
COMMENTS:       Extended version (with proof appendix) of paper that is too appear in Proceedings of IJCAI 2020
HIGHLIGHT:      In this paper we fill this gap: exploiting representation theorems for infinite exchangeable arrays we introduce a class of directed graphical latent variable models that precisely correspond to the class of projective relational models.

51, TITLE:      Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction
http://arxiv.org/abs/2002.10523
AUTHORS:        Bhavya Vasudeva ; Puneesh Deora ; Saumik Bhattacharya ; Pyari Mohan Pradhan
COMMENTS:       Under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible
HIGHLIGHT:      In this paper, we propose a novel deep network that can process complex-valued input to perform high-quality reconstruction.

52, TITLE:      NP-completeness of slope-constrained drawing of complete graphs
http://arxiv.org/abs/2001.04671
AUTHORS:        Cédric Pilatte
COMMENTS:       18 pages, 8 figures
HIGHLIGHT:      We prove the NP-completeness of the following problem.

53, TITLE:      ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators
http://arxiv.org/abs/1811.03906
AUTHORS:        Arnaud Gotlieb ; Dusica Marijan ; Helge Spieker
COMMENTS:       Extended journal version
HIGHLIGHT:      This paper presents If Then Else (ITE), a lightweight implementation of stratified constructive reasoning for logical connectives.

54, TITLE:      Population-based Gradient Descent Weight Learning for Graph Coloring Problems
http://arxiv.org/abs/1909.02261
AUTHORS:        Olivier Goudet ; Béatrice Duval ; Jin-Kao Hao
HIGHLIGHT:      In this work, we present a general population-based weight learning framework for solving graph coloring problems.

55, TITLE:      Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
http://arxiv.org/abs/2002.02909
AUTHORS:        Xian Zhang ; Xin Wang ; Bin Kong ; Canghong Shi ; Youbing Yin ; Qi Song ; Siwei Lyu ; Jiancheng Lv ; Canghong Shi ; Xiaojie Li
HIGHLIGHT:      To solve this problem, we present a domain embedded multi-model generative adversarial model for inpainting of face images with large cropped regions.

56, TITLE:      Exploration and Coordination of Complementary Multi-Robot Teams in a Hunter and Gatherer Scenario
http://arxiv.org/abs/1912.07521
AUTHORS:        Mehdi Dadvar ; Saeed Moazami ; Harley R. Myler ; Hassan Zargarzadeh
COMMENTS:       19 pages, 10 figures. arXiv admin note: text overlap with arXiv:1912.05748
HIGHLIGHT:      This paper proposes a multi-robot exploration algorithm for hunters based on innovative notions of "expected information gain" to minimize the collective cost of task accomplishments in a distributed manner.

57, TITLE:      Learning the Sampling Pattern for MRI
http://arxiv.org/abs/1906.08754
AUTHORS:        Ferdia Sherry ; Martin Benning ; Juan Carlos De los Reyes ; Martin J. Graves ; Georg Maierhofer ; Guy Williams ; Carola-Bibiane Schönlieb ; Matthias J. Ehrhardt
COMMENTS:       The main document is 12 pages, the supporting document is 2 pages and attached at the end of the main document
HIGHLIGHT:      In this work, we consider the problem of learning a sparse sampling pattern that can be used to optimally balance acquisition time versus quality of the reconstructed image.

58, TITLE:      A Diffractive Neural Network with Weight-Noise-Injection Training
http://arxiv.org/abs/2006.04462
AUTHORS:        Jiashuo Shi
HIGHLIGHT:      We propose a diffractive neural network with strong robustness based on Weight Noise Injection training, which achieves accurate and fast optical-based classification while diffraction layers have a certain amount of surface shape error.

59, TITLE:      Representing Attitudes Towards Ambiguity in Hilbert Space: Foundations and Applications
http://arxiv.org/abs/1907.06314
AUTHORS:        Sandro Sozzo
COMMENTS:       23 pages, standard LaTeX, 1 figure
HIGHLIGHT:      We provide here a general mathematical framework to model attitudes towards ambiguity which uses the formalism of quantum theory as a ``purely mathematical formalism, detached from any physical interpretation''.

60, TITLE:      Behind the Scenes: Towards Reading Beyond Faces for Sparsity-Aware 4D Affect Recognition
http://arxiv.org/abs/2002.03157
AUTHORS:        Muzammil Behzad ; Nhat Vo ; Xiaobai Li ; Guoying Zhao
HIGHLIGHT:      In this paper, we present a sparsity-aware deep network for automatic 4D facial expression recognition (FER).

61, TITLE:      AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
http://arxiv.org/abs/2005.02258
AUTHORS:        Huazhu Fu ; Fei Li ; Xu Sun ; Xingxing Cao ; Jingan Liao ; Jose Ignacio Orlando ; Xing Tao ; Yuexiang Li ; Shihao Zhang ; Mingkui Tan ; Chenglang Yuan ; Cheng Bian ; Ruitao Xie ; Jiongcheng Li ; Xiaomeng Li ; Jing Wang ; Le Geng ; Panming Li ; Huaying Hao ; Jiang Liu ; Yan Kong ; Yongyong Ren ; Hrvoje Bogunovic ; Xiulan Zhang ; Yanwu Xu
COMMENTS:       AGE Challenge website at: https://age.grand-challenge.org
HIGHLIGHT:      In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results in the two tasks. For this challenge, we released a large data of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models.

62, TITLE:      Adding A Filter Based on The Discriminator to Improve Unconditional Text Generation
http://arxiv.org/abs/2004.02135
AUTHORS:        Xingyuan Chen ; Ping Cai ; Peng Jin ; Hongjun Wang ; Xinyu Dai ; Jiajun Chen
HIGHLIGHT:      We propose a novel mechanism by adding a filter which has the same input as the discriminator.

63, TITLE:      Optical Music Recognition: State of the Art and Major Challenges
http://arxiv.org/abs/2006.07885
AUTHORS:        Elona Shatri ; György Fazekas
COMMENTS:       Author manuscript for TENOR 2020 conference. 11 pages with references, 3 figures
HIGHLIGHT:      In this paper, we review relevant works in OMR, including fundamental methods and significant outcomes, and highlight different stages of the OMR pipeline.

64, TITLE:      Transformer Reasoning Network for Image-Text Matching and Retrieval
http://arxiv.org/abs/2004.09144
AUTHORS:        Nicola Messina ; Fabrizio Falchi ; Andrea Esuli ; Giuseppe Amato
COMMENTS:       Accepted to ICPR 2020
HIGHLIGHT:      In this work, we consider the problem of accurate image-text matching for the task of multi-modal large-scale information retrieval.

65, TITLE:      CFO: A Framework for Building Production NLP Systems
http://arxiv.org/abs/1908.06121
AUTHORS:        Rishav Chakravarti ; Cezar Pendus ; Andrzej Sakrajda ; Anthony Ferritto ; Lin Pan ; Michael Glass ; Vittorio Castelli ; J. William Murdock ; Radu Florian ; Salim Roukos ; Avirup Sil
COMMENTS:       http://ibm.biz/cfo_framework
HIGHLIGHT:      This paper introduces a novel orchestration framework, called CFO (COMPUTATION FLOW ORCHESTRATOR), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments.

66, TITLE:      FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
http://arxiv.org/abs/2006.04558
AUTHORS:        Yi Ren ; Chenxu Hu ; Xu Tan ; Tao Qin ; Sheng Zhao ; Zhou Zhao ; Tie-Yan Liu
HIGHLIGHT:      In this paper, we propose FastSpeech 2, which addresses the issues in FastSpeech and better solves the one-to-many mapping problem in TTS by 1) directly training the model with ground-truth target instead of the simplified output from teacher, and 2) introducing more variation information of speech (e.g., pitch, energy and more accurate duration) as conditional inputs.

67, TITLE:      Word Sense Disambiguation using Knowledge-based Word Similarity
http://arxiv.org/abs/1911.04015
AUTHORS:        Sunjae Kwon ; Dongsuk Oh ; Youngjoong Ko
COMMENTS:       Since we changed some hyper-parameters, experimental results must be changed. We will resubmit with the retest results
HIGHLIGHT:      To address this problem, we introduce a novel knowledge-based WSD system.

68, TITLE:      Neural Relation Prediction for Simple Question Answering over Knowledge Graph
http://arxiv.org/abs/2002.07715
AUTHORS:        Amin Abolghasemi ; Saeedeh Momtazi
HIGHLIGHT:      In this paper, we propose an instance-based method to find similar questions of a new question, in the sense of their relations, to predict its mentioned relation.

69, TITLE:      A Novel Cascade Binary Tagging Framework for Relational Triple Extraction
http://arxiv.org/abs/1909.03227
AUTHORS:        Zhepei Wei ; Jianlin Su ; Yue Wang ; Yuan Tian ; Yi Chang
COMMENTS:       Accepted by ACL 2020. Code and data are available at: https://github.com/weizhepei/CasRel
HIGHLIGHT:      In this work, we introduce a fresh perspective to revisit the relational triple extraction task and propose a novel cascade binary tagging framework (CasRel) derived from a principled problem formulation.

70, TITLE:      Angle Covers: Algorithms and Complexity
http://arxiv.org/abs/1911.02040
AUTHORS:        William Evans ; Ellen Gethner ; Jack Spalding-Jamieson ; Alexander Wolff
HIGHLIGHT:      We show that any graph of maximum degree 4 admits an angle cover, give a poly-time algorithm for deciding if a graph with no degree-3 vertices has an angle-cover, and prove that, given a graph of maximum degree 5, it is NP-hard to decide whether it admits an angle cover.

71, TITLE:      GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
http://arxiv.org/abs/1911.02042
AUTHORS:        Thai Le ; Suhang Wang ; Dongwon Lee
HIGHLIGHT:      To mitigate this limitation, therefore, we borrow two notable ideas (i.e., "explanation by intervention" from causality and "explanation are contrastive" from philosophy) and propose a novel solution, named as GRACE, that better explains neural network models' predictions for tabular datasets.

72, TITLE:      AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
http://arxiv.org/abs/1912.04443
AUTHORS:        Laura Smith ; Nikita Dhawan ; Marvin Zhang ; Pieter Abbeel ; Sergey Levine
COMMENTS:       Robotics: Science and Systems (RSS) 2020 camera ready submission. Project website: https://sites.google.com/view/rss20avid
HIGHLIGHT:      In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.

73, TITLE:      ImmuNeCS: Neural Committee Search by an Artificial Immune System
http://arxiv.org/abs/1911.07729
AUTHORS:        Luc Frachon ; Wei Pang ; George M. Coghill
COMMENTS:       16 pages including references, 6 figures, 3 tables, 2 algorithms
HIGHLIGHT:      In this paper, we present ImmuNeCS, an attempt at addressing these issues with a method that offers a simple, flexible, and efficient way of building deep learning models automatically, and we demonstrate its effectiveness in the context of convolutional neural networks.

74, TITLE:      A Communication Efficient Collaborative Learning Framework for Distributed Features
http://arxiv.org/abs/1912.11187
AUTHORS:        Yang Liu ; Yan Kang ; Xinwei Zhang ; Liping Li ; Yong Cheng ; Tianjian Chen ; Mingyi Hong ; Qiang Yang
HIGHLIGHT:      We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user to jointly build models without exposing their raw data or model parameters.

75, 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.

76, TITLE:      Chinese Named Entity Recognition Augmented with Lexicon Memory
http://arxiv.org/abs/1912.08282
AUTHORS:        Yi Zhou ; Xiaoqing Zheng ; Xuanjing Huang
HIGHLIGHT:      Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates.

77, TITLE:      Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses
http://arxiv.org/abs/1909.05926
AUTHORS:        Rodney LaLonde ; Drew Torigian ; Ulas Bagci
COMMENTS:       Accepted for publication at MICCAI 2020 (23rd International Conference on Medical Image Computing and Computer Assisted Intervention). Code is publicly available at https://github.com/lalonderodney/X-Caps
HIGHLIGHT:      We demonstrate a simple 2D capsule network can outperform a state-of-the-art deep dense dual-path 3D CNN at capturing visually-interpretable high-level attributes and malignancy prediction, while providing malignancy prediction scores approaching that of non-explainable 3D CNNs.

78, TITLE:      Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
http://arxiv.org/abs/2003.08757
AUTHORS:        Ranjie Duan ; Xingjun Ma ; Yisen Wang ; James Bailey ; A. K. Qin ; Yun Yang
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      In this paper, we propose a novel approach, called Adversarial Camouflage (\emph{AdvCam}), to craft and camouflage physical-world adversarial examples into natural styles that appear legitimate to human observers.

79, TITLE:      Pattern-based design applied to cultural heritage knowledge graphs
http://arxiv.org/abs/1911.07585
AUTHORS:        Valentina Anita Carriero ; Aldo Gangemi ; Maria Letizia Mancinelli ; Andrea Giovanni Nuzzolese ; Valentina Presutti ; Chiara Veninata
HIGHLIGHT:      This paper presents ArCo and describes how to apply XD to the development and validation of a CH knowledge graph, also detailing the (intellectual) process implemented for matching the encountered modelling problems to ODPs.

80, TITLE:      Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers
http://arxiv.org/abs/2004.00849
AUTHORS:        Zhicheng Huang ; Zhaoyang Zeng ; Bei Liu ; Dongmei Fu ; Jianlong Fu
HIGHLIGHT:      We propose Pixel-BERT to align image pixels with text by deep multi-modal transformers that jointly learn visual and language embedding in a unified end-to-end framework.

81, TITLE:      Deep Quaternion Features for Privacy Protection
http://arxiv.org/abs/2003.08365
AUTHORS:        Hao Zhang ; Yiting Chen ; Liyao Xiang ; Haotian Ma ; Jie Shi ; Quanshi Zhang
HIGHLIGHT:      We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information.

82, TITLE:      Provable Meta-Learning of Linear Representations
http://arxiv.org/abs/2002.11684
AUTHORS:        Nilesh Tripuraneni ; Chi Jin ; Michael I. Jordan
HIGHLIGHT:      In this paper, we focus on the problem of multi-task linear regression---in which multiple linear regression models share a common, low-dimensional linear representation.

83, TITLE:      A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds
http://arxiv.org/abs/2005.10745
AUTHORS:        Mohammed Yousefhussien ; David J. Kelbe ; Carl Salvaggio
COMMENTS:       Preprint submitted to Science of Remote Sensing
HIGHLIGHT:      In contrast, here we present an algorithm that directly operates on 3D-point clouds and estimate the underlying DTM for the scene using an end-to-end approach without the need to classify points into ground and non-ground cover types.

84, TITLE:      Visual-Inertial Telepresence for Aerial Manipulation
http://arxiv.org/abs/2003.11509
AUTHORS:        Jongseok Lee ; Ribin Balachandran ; Yuri S. Sarkisov ; Marco De Stefano ; Andre Coelho ; Kashmira Shinde ; Min Jun Kim ; Rudolph Triebel ; Konstantin Kondak
COMMENTS:       Accepted to International Conference on Robotics and Automation (ICRA) 2020, IEEE copyright, 8 pages, 10 figures
HIGHLIGHT:      This paper presents a novel telepresence system for enhancing aerial manipulation capabilities.

85, TITLE:      Extending and Automating Basic Probability Theory with Propositional Computability Logic
http://arxiv.org/abs/1909.07375
AUTHORS:        Keehang Kwon
COMMENTS:       4 pages. Some errors were fixed
HIGHLIGHT:      In this paper, we extend classical probability theory with propositional computability logic.

86, TITLE:      Momentum-Net: Fast and convergent iterative neural network for inverse problems
http://arxiv.org/abs/1907.11818
AUTHORS:        Il Yong Chun ; Zhengyu Huang ; Hongki Lim ; Jeffrey A. Fessler
COMMENTS:       28 pages, 13 figures, 3 algorithms, 4 tables, submitted revision to IEEE T-PAMI
HIGHLIGHT:      This paper proposes the first fast and convergent INN architecture, Momentum-Net, by generalizing a block-wise MBIR algorithm that uses momentum and majorizers with regression NNs.

87, TITLE:      Non-linear and Selective Fusion of Cross-Modal Images
http://arxiv.org/abs/1912.10738
AUTHORS:        Aiqing Fang ; Xinbo Zhao ; Jiaqi Yang ; Yanning Zhang
HIGHLIGHT:      In order to simulate the human visual perception mechanism in image fusion tasks, we propose a multi-source image fusion framework that combines illuminance factors and attention mechanisms.

88, TITLE:      Surreal: Complex-Valued Deep Learning as Principled Transformations on a Rotational Lie Group
http://arxiv.org/abs/1910.11334
AUTHORS:        Rudrasis Chakraborty ; Yifei Xing ; Stella Yu
COMMENTS:       12 pages, submitted to TNNLS journal
HIGHLIGHT:      We propose a principled geometric approach to complex-valued deep learning.

89, TITLE:      Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays
http://arxiv.org/abs/2004.08379
AUTHORS:        Sivaramakrishnan Rajaraman ; Jen Siegelman ; Philip O. Alderson ; Lucas S. Folio ; Les R. Folio ; Sameer K. Antani
COMMENTS:       10 pages, 6 figures, journal
HIGHLIGHT:      We demonstrate use of iteratively pruned deep learning model ensembles for detecting pulmonary manifestation of COVID-19 with chest X-rays.

90, TITLE:      Relation Adversarial Network for Low Resource Knowledge Graph Completion
http://arxiv.org/abs/1911.03091
AUTHORS:        Ningyu Zhang ; Shumin Deng ; Zhanlin Sun ; Jiaoayan Chen ; Wei Zhang ; Huajun Chen
COMMENTS:       WWW2020
HIGHLIGHT:      In this work, we aim at predicting new facts under a challenging setting where only limited training instances are available.

91, TITLE:      Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
http://arxiv.org/abs/1912.02424
AUTHORS:        Shifeng Zhang ; Cheng Chi ; Yongqiang Yao ; Zhen Lei ; Stan Z. Li
COMMENTS:       Accepted by CVPR 2020 as Oral; Best Paper Nomination
HIGHLIGHT:      In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them.

92, TITLE:      Dissecting Neural ODEs
http://arxiv.org/abs/2002.08071
AUTHORS:        Stefano Massaroli ; Michael Poli ; Jinkyoo Park ; Atsushi Yamashita ; Hajime Asama
HIGHLIGHT:      In this work, we "open the box" and offer a system-theoretic perspective, including state augmentation strategies and robustness, with the aim of clarifying the influence of several design choices on the underlying dynamics.

93, TITLE:      Conversational Neuro-Symbolic Commonsense Reasoning
http://arxiv.org/abs/2006.10022
AUTHORS:        Forough Arabshahi ; Jennifer Lee ; Mikayla Gawarecki ; Kathryn Mazaitis ; Amos Azaria ; Tom Mitchell
HIGHLIGHT:      We propose a new commonsense reasoning benchmark where the task is to uncover commonsense presumptions implied by imprecisely stated natural language commands in the form of if-then-because statements. We release a benchmark data set for this task, collected from humans and annotated with commonsense presumptions.

94, TITLE:      Dissimilarity Mixture Autoencoder for Deep Clustering
http://arxiv.org/abs/2006.08177
AUTHORS:        Juan S. Lara ; Fabio A. González
COMMENTS:       8 pages (5 additional pages for broader impact, references and supplementary material)
HIGHLIGHT:      In this paper, we introduce the Dissimilarity Mixture Autoencoder (DMAE), a novel neural network model that uses a dissimilarity function to generalize a family of density estimation and clustering methods.

95, TITLE:      SOLOIST: Few-shot Task-Oriented Dialog with A Single Pre-trained Auto-regressive Model
http://arxiv.org/abs/2005.05298
AUTHORS:        Baolin Peng ; Chunyuan Li ; Jinchao Li ; Shahin Shayandeh ; Lars Liden ; Jianfeng Gao
COMMENTS:       11 pages; Project Website: https://aka.ms/soloist
HIGHLIGHT:      This paper presents a new method SOLOIST, which uses transfer learning to efficiently build task-oriented dialog systems at scale.

96, TITLE:      MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop
http://arxiv.org/abs/2004.14858
AUTHORS:        Lukas Stappen ; Alice Baird ; Georgios Rizos ; Panagiotis Tzirakis ; Xinchen Du ; Felix Hafner ; Lea Schumann ; Adria Mallol-Ragolta ; Björn W. Schuller ; Iulia Lefter ; Erik Cambria ; Ioannis Kompatsiaris
COMMENTS:       Baseline Paper MuSe 2020, MuSe Workshop Challenge, ACM Multimedia
HIGHLIGHT:      In this paper, we provide detailed information on MuSe-CaR, the first of its kind in-the-wild database, which is utilised for the challenge, as well as the state-of-the-art features and modelling approaches applied.

97, TITLE:      Training Efficient Network Architecture and Weights via Direct Sparsity Control
http://arxiv.org/abs/2002.04301
AUTHORS:        Yangzi Guo ; Yiyuan She ; Adrian Barbu
HIGHLIGHT:      In this paper, we propose a novel efficient network pruning method that is suitable for both non-structured and structured channel-level pruning.

98, TITLE:      Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
http://arxiv.org/abs/2006.09989
AUTHORS:        Elvis Dohmatob
HIGHLIGHT:      Our contributions are three-fold.