2020.05.29.txt

==========New Papers==========
1, TITLE:       Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection
http://arxiv.org/abs/2005.14140
AUTHORS:        Oliver Rippel ; Patrick Mertens ; Dorit Merhof
COMMENTS:       First two authors contributed equally to this work
HIGHLIGHT:      Popular AD algorithms commonly try to learn a model of normality from scratch using task specific datasets, but are limited to semi-supervised approaches employing mostly normal data due to the inaccessibility of anomalies on a large scale combined with the ambiguous nature of anomaly appearance.

2, TITLE:       QEBA: Query-Efficient Boundary-Based Blackbox Attack
http://arxiv.org/abs/2005.14137
AUTHORS:        Huichen Li ; Xiaojun Xu ; Xiaolu Zhang ; Shuang Yang ; Bo Li
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      In this paper, we propose a Query-Efficient Boundary-based blackbox Attack (QEBA) based only on model's final prediction labels.

3, TITLE:       Unlucky Explorer: A Complete non-Overlapping Map Exploration
http://arxiv.org/abs/2005.14156
AUTHORS:        Mohammad Sina Kiarostami ; Saleh Khalaj Monfared ; Mohammadreza Daneshvaramoli ; Ali Oliayi ; Negar Yousefian ; Dara Rahmati ; Saeid Gorgin
HIGHLIGHT:      Our comparison indicates that the MCTS-based approach is an up-and-coming method that could cope with the test cases with small and medium sizes with faster run-time compared to SAT.

4, TITLE:       Self-supervised Modal and View Invariant Feature Learning
http://arxiv.org/abs/2005.14169
AUTHORS:        Longlong Jing ; Yucheng Chen ; Ling Zhang ; Mingyi He ; Yingli Tian
HIGHLIGHT:      By exploring the inherent multi-modality attributes of 3D objects, in this paper, we propose to jointly learn modal-invariant and view-invariant features from different modalities including image, point cloud, and mesh with heterogeneous networks for 3D data.

5, TITLE:       Language Models are Few-Shot Learners
http://arxiv.org/abs/2005.14165
AUTHORS:        Tom B. Brown ; Benjamin Mann ; Nick Ryder ; Melanie Subbiah ; Jared Kaplan ; Prafulla Dhariwal ; Arvind Neelakantan ; Pranav Shyam ; Girish Sastry ; Amanda Askell ; Sandhini Agarwal ; Ariel Herbert-Voss ; Gretchen Krueger ; Tom Henighan ; Rewon Child ; Aditya Ramesh ; Daniel M. Ziegler ; Jeffrey Wu ; Clemens Winter ; Christopher Hesse ; Mark Chen ; Eric Sigler ; Mateusz Litwin ; Scott Gray ; Benjamin Chess ; Jack Clark ; Christopher Berner ; Sam McCandlish ; Alec Radford ; Ilya Sutskever ; Dario Amodei
COMMENTS:       40+32 pages
HIGHLIGHT:      Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

6, TITLE:       HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
http://arxiv.org/abs/2005.14187
AUTHORS:        Hanrui Wang ; Zhanghao Wu ; Zhijian Liu ; Han Cai ; Ligeng Zhu ; Chuang Gan ; Song Han
COMMENTS:       Accepted to ACL 2020. 14 pages, 12 figures. Code available at http://github.com/mit-han-lab/hardware-aware-transformers.git
HIGHLIGHT:      To enable low-latency inference on resource-constrained hardware platforms, we propose to design Hardware-Aware Transformers (HAT) with neural architecture search.

7, TITLE:       Liar's Domination in Unit Disk Graphs
http://arxiv.org/abs/2005.13913
AUTHORS:        Ramesh K. Jallu ; Sangram K. Jena ; Gautam K. Das
HIGHLIGHT:      In this article, we study a variant of the minimum dominating set problem known as the minimum liar's dominating set (MLDS) problem.

8, TITLE:       Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images
http://arxiv.org/abs/2005.13928
AUTHORS:        D. Gil ; K. Díaz-Chito ; C. Sánchez ; A. Hernández-Sabaté
HIGHLIGHT:      In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection.

9, TITLE:       CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images
http://arxiv.org/abs/2005.13924
AUTHORS:        Ferdaous Idlahcen ; Mohammed Majid Himmi ; Abdelhak Mahmoudi
COMMENTS:       Presented at the ICLR 2020 Workshop on AI for Overcoming Global Disparities in Cancer Care (AI4CC)
HIGHLIGHT:      In this study, few cervical tissue digital slides from TCGA data portal were pre-processed to overcome whole-slide images obstacles and included in our proposed VGG16-CNN classification approach.

10, TITLE:      Quantifying the Complexity of Standard Benchmarking Datasets for Long-Term Human Trajectory Prediction
http://arxiv.org/abs/2005.13934
AUTHORS:        Ronny Hug ; Stefan Becker ; Wolfgang Hübner ; Michael Arens
COMMENTS:       Submitted to RA-L Special Issue on Long-Term Human Motion Prediction
HIGHLIGHT:      In order to gain a better understanding of the complexity of trajectory datasets, an approach for deriving complexity scores from a prototype-based dataset representation is proposed.

11, TITLE:      Variational Autoencoder with Embedded Student-$t$ Mixture Model for Authorship Attribution
http://arxiv.org/abs/2005.13930
AUTHORS:        Benedikt Boenninghoff ; Steffen Zeiler ; Robert M. Nickel ; Dorothea Kolossa
COMMENTS:       Preprint
HIGHLIGHT:      In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task.

12, TITLE:      Disentanglement Then Reconstruction: Learning Compact Features for Unsupervised Domain Adaptation
http://arxiv.org/abs/2005.13947
AUTHORS:        Lihua Zhou ; Mao Ye ; Xinpeng Li ; Ce Zhu ; Yiguang Liu ; Xue Li
HIGHLIGHT:      We propose a new domain adaptation method based on prototype construction which likes capturing data cluster centers.

13, TITLE:      Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification
http://arxiv.org/abs/2005.13705
AUTHORS:        Zhuotun Zhu ; Ke Yan ; Dakai Jin ; Jinzheng Cai ; Tsung-Ying Ho ; Adam P Harrison ; Dazhou Guo ; Chun-Hung Chao ; Xianghua Ye ; Jing Xiao ; Alan Yuille ; Le Lu
COMMENTS:       14 pages, 4 Figures
HIGHLIGHT:      In this work, we propose a divide-and-conquer decision stratification approach that divides OSLNs into tumor-proximal and tumor-distal categories.

14, TITLE:      AFAT: Adaptive Failure-Aware Tracker for Robust Visual Object Tracking
http://arxiv.org/abs/2005.13708
AUTHORS:        Tianyang Xu ; Zhen-Hua Feng ; Xiao-Jun Wu ; Josef Kittler
HIGHLIGHT:      In this paper, we advocate online adaptation in the tracking stage.

15, TITLE:      Towards the Infeasibility of Membership Inference on Deep Models
http://arxiv.org/abs/2005.13702
AUTHORS:        Shahbaz Rezaei ; Xin Liu
HIGHLIGHT:      Recent studies propose membership inference (MI) attacks on deep models.

16, TITLE:      Graph-based Proprioceptive Localization Using a Discrete Heading-Length Feature Sequence Matching Approach
http://arxiv.org/abs/2005.13704
AUTHORS:        Hsin-Min Cheng ; Dezhen Song
COMMENTS:       13 pages, 32 figures
HIGHLIGHT:      Named as graph-based proprioceptive localization (GBPL), we provide a low cost fallback solution for localization under challenging environmental conditions.

17, TITLE:      Few-Shot Open-Set Recognition using Meta-Learning
http://arxiv.org/abs/2005.13713
AUTHORS:        Bo Liu ; Hao Kang ; Haoxiang Li ; Gang Hua ; Nuno Vasconcelos
HIGHLIGHT:      Few-Shot Open-Set Recognition using Meta-Learning

18, TITLE:      Improving Generalized Zero-Shot Learning by Semantic Discriminator
http://arxiv.org/abs/2005.13956
AUTHORS:        Xinpeng Li ; Mao Ye ; Lihua Zhou ; Dan Zhang ; Ce Zhu ; Yiguang Liu
HIGHLIGHT:      We propose a new approach to distinguish whether the instances come from the seen or unseen classes.

19, TITLE:      From Functional Nondeterministic Transducers to Deterministic Two-Tape Automata
http://arxiv.org/abs/2005.13710
AUTHORS:        Elisabet Burjons ; Fabian Frei ; Martin Raszyk
HIGHLIGHT:      In this paper, we examine the analogous problem for finite transducers and automata.

20, TITLE:      D2D: Keypoint Extraction with Describe to Detect Approach
http://arxiv.org/abs/2005.13605
AUTHORS:        Yurun Tian ; Vassileios Balntas ; Tony Ng ; Axel Barroso-Laguna ; Yiannis Demiris ; Krystian Mikolajczyk
HIGHLIGHT:      In this paper, we present a novel approach that exploits the information within the descriptor space to propose keypoint locations.

21, TITLE:      The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems
http://arxiv.org/abs/2005.13601
AUTHORS:        Eric MSP Veith ; Nils Wenninghoff ; Emilie Frost
COMMENTS:       Submitted to NIPS 2020
HIGHLIGHT:      In this paper, we describe the concept of Adversarial Resilience Learning (ARL) that formulates a new approach to complex environment checking and resilient operation: It defines two agent classes, attacker and defender agents.

22, TITLE:      Language Representation Models for Fine-Grained Sentiment Classification
http://arxiv.org/abs/2005.13619
AUTHORS:        Brian Cheang ; Bailey Wei ; David Kogan ; Howey Qiu ; Masud Ahmed
HIGHLIGHT:      In this paper, we examine whether the improvements hold true when applied to a novel task, by replicating the BERT model from Munikar et al., and swapping the embedding layer to the alternative models.

23, TITLE:      Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments
http://arxiv.org/abs/2005.13857
AUTHORS:        Hartmut Surmann ; Christian Jestel ; Robin Marchel ; Franziska Musberg ; Houssem Elhadj ; Mahbube Ardani
COMMENTS:       7 pages, report
HIGHLIGHT:      In this paper we present our proof of concept for autonomous self-learning robot navigation in an unknown environment for a real robot without a map or planner.

24, TITLE:      Dynamic Bi-Objective Routing of Multiple Vehicles
http://arxiv.org/abs/2005.13872
AUTHORS:        Jakob Bossek ; Christian Grimme ; Heike Trautmann
HIGHLIGHT:      In this paper we study this type of bi-objective dynamic VRP including sequential decision making and concurrent realization of decisions.

25, TITLE:      Parameter Sharing is Surprisingly Useful for Multi-Agent Deep Reinforcement Learning
http://arxiv.org/abs/2005.13625
AUTHORS:        Justin K Terry ; Nathaniel Grammel ; Ananth Hari ; Luis Santos ; Benjamin Black ; Dinesh Manocha
HIGHLIGHT:      We use the MAILP model to show that increasing training centralization arbitrarily mitigates the slowing of convergence due to nonstationarity.

26, TITLE:      Learning Various Length Dependence by Dual Recurrent Neural Networks
http://arxiv.org/abs/2005.13867
AUTHORS:        Chenpeng Zhang ; Shuai Li ; Mao Ye ; Ce Zhu ; Xue Li
HIGHLIGHT:      To this problem, we propose a new model named Dual Recurrent Neural Networks (DuRNN).

27, TITLE:      Traditional Method Inspired Deep Neural Network for Edge Detection
http://arxiv.org/abs/2005.13862
AUTHORS:        Jan Kristanto Wibisono ; Hsueh-Ming Hang
HIGHLIGHT:      Therefore, we propose a traditional method inspired framework to produce good edges with minimal complexity.

28, TITLE:      Towards Decision Support in Dynamic Bi-Objective Vehicle Routing
http://arxiv.org/abs/2005.13865
AUTHORS:        Jakob Bossek ; Christian Grimme ; Günter Rudolph ; Heike Trautmann
HIGHLIGHT:      We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time.

29, TITLE:      AI Forensics: Did the Artificial Intelligence System Do It? Why?
http://arxiv.org/abs/2005.13635
AUTHORS:        Johannes Schneider ; Frank Breitinger
HIGHLIGHT:      This paper discusses how to identify AI systems responsible for incidents as well as their motives that might be "malicious by design".

30, TITLE:      Looking back to lower-level information in few-shot learning
http://arxiv.org/abs/2005.13638
AUTHORS:        Zhongjie Yu ; Sebastian Raschka
COMMENTS:       13 pages, 2 figures
HIGHLIGHT:      In this work, we propose the utilization of lower-level, supporting information, namely the feature embeddings of the hidden neural network layers, to improve classifier accuracy.

31, TITLE:      GraFS: Graph Analytics Fusion and Synthesis
http://arxiv.org/abs/2005.13632
AUTHORS:        Farzin Houshmand ; Mohsen Lesani ; Keval Vora
HIGHLIGHT:      This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks.

32, TITLE:      On motifs in colored graphs
http://arxiv.org/abs/2005.13634
AUTHORS:        Diego P Rubert ; Eloi Araujo ; Marco A Stefanes ; Jens Stoye ; Fábio V Martinez
COMMENTS:       28 pages, 9 figures, to be published in Journal of Combinatorial Optimization
HIGHLIGHT:      In this work we are interested in searching and inferring network motifs in a class of biological networks that can be represented by vertex-colored graphs.

33, TITLE:      P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds
http://arxiv.org/abs/2005.13888
AUTHORS:        Haozhe Qi ; Chen Feng ; Zhiguo Cao ; Feng Zhao ; Yang Xiao
COMMENTS:       Accepted by CVPR 2020 (oral)
HIGHLIGHT:      Our main idea is to first localize potential target centers in 3D search area embedded with target information.

34, TITLE:      Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model
http://arxiv.org/abs/2005.13643
AUTHORS:        Abdul Qayyum ; Alain Lalande ; Thomas Decourselle ; Thibaut Pommier ; Alexandre Cochet ; Fabrice Meriaudeau
HIGHLIGHT:      The proposed model could be used for the automatic segmentation of myocardial border that is a very important step for accurate quantification of no-reflow, myocardial infarction, myocarditis, and hypertrophic cardiomyopathy, among others.

35, TITLE:      CGGAN: A Context Guided Generative Adversarial Network For Single Image Dehazing
http://arxiv.org/abs/2005.13884
AUTHORS:        Zhaorun Zhou ; Zhenghao Shi ; Mingtao Guo ; Yaning Feng ; Minghua Zhao
COMMENTS:       12 pages, 7 figures, 3 tables
HIGHLIGHT:      This paper proposes a novel Context Guided Generative Adversarial Network (CGGAN) for single image dehazing.

36, TITLE:      Deep Learning for Automatic Pneumonia Detection
http://arxiv.org/abs/2005.13899
AUTHORS:        Tatiana Gabruseva ; Dmytro Poplavskiy ; Alexandr A. Kalinin
COMMENTS:       to appear in CVPR 2020 Workshops proceedings
HIGHLIGHT:      In this work, we develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.

37, TITLE:      Local Algebraic Effect Theories
http://arxiv.org/abs/2005.13654
AUTHORS:        Žiga Lukšič ; Matija Pretnar
COMMENTS:       24 pages, 8 figures
HIGHLIGHT:      We present an alternative approach where the type system tracks equations that are observed in subparts of the program, yielding a sound and flexible logic, and paving a way for practical optimizations and reasoning tools.

38, TITLE:      When Can Self-Attention Be Replaced by Feed Forward Layers?
http://arxiv.org/abs/2005.13895
AUTHORS:        Shucong Zhang ; Erfan Loweimi ; Peter Bell ; Steve Renals
HIGHLIGHT:      Recently, self-attention models such as Transformers have given competitive results compared to recurrent neural network systems in speech recognition.

39, TITLE:      ProTuner: Tuning Programs with Monte Carlo Tree Search
http://arxiv.org/abs/2005.13685
AUTHORS:        Ameer Haj-Ali ; Hasan Genc ; Qijing Huang ; William Moses ; John Wawrzynek ; Krste Asanović ; Ion Stoica
HIGHLIGHT:      We explore applying the Monte Carlo Tree Search (MCTS) algorithm in a notoriously difficult task: tuning programs for high-performance deep learning and image processing.

40, TITLE:      Phone Features Improve Speech Translation
http://arxiv.org/abs/2005.13681
AUTHORS:        Elizabeth Salesky ; Alan W Black
COMMENTS:       Accepted to ACL2020
HIGHLIGHT:      We compare cascaded and end-to-end models across high, medium, and low-resource conditions, and show that cascades remain stronger baselines.

41, TITLE:      An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images
http://arxiv.org/abs/2005.13695
AUTHORS:        Mohammed Ahmed ; Hongbo Du ; Alaa AlZoubi
COMMENTS:       6 pages, 3 figures, Conference: Medical Imaging with Deep Learning 2020
HIGHLIGHT:      In this paper, we applied the Efficient Neural Architecture Search (ENAS) method to find optimal CNN architectures for classifying breast lesions from ultrasound (US) images.

42, TITLE:      Multiple resolution residual network for automatic thoracic organs-at-risk segmentation from CT
http://arxiv.org/abs/2005.13690
AUTHORS:        Hyemin Um ; Jue Jiang ; Maria Thor ; Andreas Rimner ; Leo Luo ; Joseph O. Deasy ; Harini Veeraraghavan
COMMENTS:       MIDL 2020 short paper
HIGHLIGHT:      We trained our approach using 206 thoracic CT scans of lung cancer patients with 35 scans held out for validation to segment the left and right lungs, heart, esophagus, and spinal cord.

43, TITLE:      Adversarial Attacks and Defense on Textual Data: A Review
http://arxiv.org/abs/2005.14108
AUTHORS:        Aminul Huq ; Mst. Tasnim Pervin
HIGHLIGHT:      In this manuscript we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea.

44, TITLE:      Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size
http://arxiv.org/abs/2005.14105
AUTHORS:        Christoph Hertrich ; Martin Skutella
HIGHLIGHT:      Our main contribution is a class of recurrent neural networks (RNNs) with rectified linear units that are iteratively applied to each item of a Knapsack instance and thereby compute optimal or provably good solution values.

45, TITLE:      Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies
http://arxiv.org/abs/2005.14107
AUTHORS:        Mattias P Heinrich ; Lasse Hansen
COMMENTS:       Medical Imaging with Deep Learning (accepted short paper) https://openreview.net/forum?id=wbZM-DcJB9
HIGHLIGHT:      To improve upon the sliced Wasserstein metric for 2D histograms, we present a novel approximation that projects predictions into 1D and computes the L1 distance of their cumulative sums.

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

47, TITLE:      Heatmap-Based Method for Estimating Drivers' Cognitive Distraction
http://arxiv.org/abs/2005.14136
AUTHORS:        Antonyo Musabini ; Mounsif Chetitah
HIGHLIGHT:      In this study, the influence of cognitive processes on the drivers gaze behavior is explored.

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

49, TITLE:      Joint Modelling of Emotion and Abusive Language Detection
http://arxiv.org/abs/2005.14028
AUTHORS:        Santhosh Rajamanickam ; Pushkar Mishra ; Helen Yannakoudakis ; Ekaterina Shutova
COMMENTS:       Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020
HIGHLIGHT:      In this paper, we present the first joint model of emotion and abusive language detection, experimenting in a multi-task learning framework that allows one task to inform the other.

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

51, TITLE:      Sound Regular Corecursion in coFJ
http://arxiv.org/abs/2005.14085
AUTHORS:        Davide Ancona ; Pietro Barbieri ; Francesco Dagnino ; Elena Zucca
HIGHLIGHT:      The aim of the paper is to provide solid foundations for a programming paradigm natively supporting the creation and manipulation of cyclic data structures.

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

53, TITLE:      User Intent Inference for Web Search and Conversational Agents
http://arxiv.org/abs/2005.13808
AUTHORS:        Ali Ahmadvand
COMMENTS:       WSDM2020
HIGHLIGHT:      To address the first topic, I proposed novel models to incorporate entity information and conversation-context clues to predict both topic and intent of the user's utterances.

54, TITLE:      Contextual Dialogue Act Classification for Open-Domain Conversational Agents
http://arxiv.org/abs/2005.13804
AUTHORS:        Ali Ahmadvand ; Jason Ingyu Choi ; Eugene Agichtein
COMMENTS:       SIGIR 2019
HIGHLIGHT:      To address these problems, we propose a novel method, CDAC (Contextual Dialogue Act Classifier), a simple yet effective deep learning approach for contextual dialogue act classification.

55, TITLE:      Would you Like to Talk about Sports Now? Towards Contextual Topic Suggestion for Open-Domain Conversational Agents
http://arxiv.org/abs/2005.13803
AUTHORS:        Ali Ahmadvand ; Harshita Sahijwani ; Eugene Agichtein
COMMENTS:       CHIIR 2020
HIGHLIGHT:      Instead, we explore different methods for a personalized, contextual topic suggestion for open-domain conversations.

56, TITLE:      Explicit Effect Subtyping
http://arxiv.org/abs/2005.13814
AUTHORS:        Georgios Karachalias ; Matija Pretnar ; Amr Hany Saleh ; Stien Vanderhallen ; Tom Schrijvers
COMMENTS:       57 pages, 29 figures
HIGHLIGHT:      To remedy this, we present an explicitly-typed polymorphic core calculus for algebraic effect handlers with a subtyping-based type-and-effect system.

57, TITLE:      Subword RNNLM Approximations for Out-Of-Vocabulary Keyword Search
http://arxiv.org/abs/2005.13827
AUTHORS:        Mittul Singh ; Sami Virpioja ; Peter Smit ; Mikko Kurimo
COMMENTS:       INTERSPEECH 2019
HIGHLIGHT:      In this paper, we propose to interpolate the conventional n-gram models and the RNNLM approximation for better OOV recognition.

58, TITLE:      Boosting Few-Shot Learning With Adaptive Margin Loss
http://arxiv.org/abs/2005.13826
AUTHORS:        Aoxue Li ; Weiran Huang ; Xu Lan ; Jiashi Feng ; Zhenguo Li ; Liwei Wang
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      This paper proposes an adaptive margin principle to improve the generalization ability of metric-based meta-learning approaches for few-shot learning problems.

59, TITLE:      Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs
http://arxiv.org/abs/2005.13823
AUTHORS:        Behnam Pourghassemi ; Chenghao Zhang ; Joo Hwan Lee ; Aparna Chandramowlishwaran
COMMENTS:       3 pages, 1 figure, to be published in Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA '20)
HIGHLIGHT:      We identify the challenges and limitations in enabling concurrent layer execution on GPU backends (such as cuDNN) of DL frameworks and propose potential solutions.

60, TITLE:      More Effective Randomized Search Heuristics for Graph Coloring Through Dynamic Optimization
http://arxiv.org/abs/2005.13825
AUTHORS:        Jakob Bossek ; Frank Neumann ; Pan Peng ; Dirk Sudholt
COMMENTS:       To be presented at GECCO2020
HIGHLIGHT:      We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior.

61, TITLE:      TOAN: Target-Oriented Alignment Network for Fine-Grained Image Categorization with Few Labeled Samples
http://arxiv.org/abs/2005.13820
AUTHORS:        Huaxi Huang ; Junjie Zhang ; Jian Zhang ; Qiang Wu ; Chang Xu
HIGHLIGHT:      In this paper, we propose a Target-Oriented Alignment Network (TOAN) to investigate the fine-grained relation between the target query image and support classes.

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

63, TITLE:      A Corpus for Large-Scale Phonetic Typology
http://arxiv.org/abs/2005.13962
AUTHORS:        Elizabeth Salesky ; Eleanor Chodroff ; Tiago Pimentel ; Matthew Wiesner ; Ryan Cotterell ; Alan W Black ; Jason Eisner
COMMENTS:       Accepted to ACL2020
HIGHLIGHT:      We present VoxClamantis v1.0, the first large-scale corpus for phonetic typology, with aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants.

64, TITLE:      Robust Modeling of Epistemic Mental States
http://arxiv.org/abs/2005.13982
AUTHORS:        AKMMahbubur Rahman ; ASM Iftekhar Anam ; Mohammed Yeasin
COMMENTS:       Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologies
HIGHLIGHT:      In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states.

65, TITLE:      Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents
http://arxiv.org/abs/2005.13734
AUTHORS:        Satoshi Hashimoto ; Yonghoon Ji ; Kenichi Kudo ; Takayuki Takahashi ; Kazunori Umeda
HIGHLIGHT:      This paper proposes an anomaly detection method for the prevention of industrial accidents using machine learning technology.

66, TITLE:      Variational Neural Machine Translation with Normalizing Flows
http://arxiv.org/abs/2005.13978
AUTHORS:        Hendra Setiawan ; Matthias Sperber ; Udhay Nallasamy ; Matthias Paulik
COMMENTS:       To appear in 2020 Association for Computational Linguistics (ACL) as a short paper
HIGHLIGHT:      In this paper, we propose to apply the VNMT framework to the state-of-the-art Transformer and introduce a more flexible approximate posterior based on normalizing flows.

67, TITLE:      L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion
http://arxiv.org/abs/2005.13736
AUTHORS:        Tunai Porto Marques ; Alexandra Branzan Albu
COMMENTS:       Accepted in the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshop NTIRE: New Trends in Image Restoration and Enhancement. To be published in the "2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops" proceedings book
HIGHLIGHT:      We present a novel single-image low-light underwater image enhancer, L^2UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from local contrast information.

68, TITLE:      Complex networks for event detection in heterogeneous high volume news streams
http://arxiv.org/abs/2005.13751
AUTHORS:        Iraklis Moutidis ; Hywel T. P. Williams
HIGHLIGHT:      In this paper we develop a network-based approach that makes the workingassumption that important news events always involve named entities (such as persons, locationsand organizations) that are linked in news articles.

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

70, TITLE:      Perception-aware time optimal path parameterization for quadrotors
http://arxiv.org/abs/2005.13986
AUTHORS:        Igor Spasojevic ; Varun Murali ; Sertac Karaman
COMMENTS:       Accepted to appear at ICRA 2020
HIGHLIGHT:      The main contribution of this paper is an efficient time optimal path parametrization algorithm for quadrotors with limited field of view constraints.

71, TITLE:      The SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
http://arxiv.org/abs/2005.13756
AUTHORS:        Katharina Kann ; Arya McCarthy ; Garrett Nicolai ; Mans Hulden
COMMENTS:       SIGMORPHON 2020
HIGHLIGHT:      In this paper, we describe the findings of the SIGMORPHON 2020 shared task on unsupervised morphological paradigm completion (SIGMORPHON 2020 Task 2), a novel task in the field of inflectional morphology. In order to simulate a realistic use case, we first released data for 5 development languages.

72, TITLE:      Stereo Vision Based Single-Shot 6D Object Pose Estimation for Bin-Picking by a Robot Manipulator
http://arxiv.org/abs/2005.13759
AUTHORS:        Yoshihiro Nakano
COMMENTS:       7 pages, 8 figures
HIGHLIGHT:      We propose a fast and accurate method of 6D object pose estimation for bin-picking of mechanical parts by a robot manipulator. First, we create original synthetic datasets for training and evaluating of the proposed model.

73, TITLE:      Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled Datasets
http://arxiv.org/abs/2005.13753
AUTHORS:        Ke Yan ; Jinzheng Cai ; Adam P. Harrison ; Dakai Jin ; Jing Xiao ; Le Lu
HIGHLIGHT:      In this work, we propose a novel framework to leverage all these datasets together to improve the performance of ULD.

74, TITLE:      From Prediction to Prescription: AI-Based Optimization of Non-Pharmaceutical Interventions for the COVID-19 Pandemic
http://arxiv.org/abs/2005.13766
AUTHORS:        Risto Miikkulainen ; Olivier Francon ; Elliot Meyerson ; Xin Qiu ; Elisa Canzani ; Babak Hodjat
HIGHLIGHT:      Several models have been developed to predict how the COVID-19 pandemic spreads, and how it could be contained with non-pharmaceutical interventions (NPIs) such as social distancing restrictions and school and business closures.

75, TITLE:      JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search
http://arxiv.org/abs/2005.13783
AUTHORS:        Ali Ahmadvand ; Surya Kallumadi ; Faizan Javed ; Eugene Agichtein
COMMENTS:       SIGIR 2020
HIGHLIGHT:      In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.

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

77, TITLE:      Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning
http://arxiv.org/abs/2005.13778
AUTHORS:        Parth Chadha
HIGHLIGHT:      In this paper, we propose a gradient matching algorithm to improve sample efficiency by utilizing target slope information from the dynamics predictor to aid the model-free learner.

78, TITLE:      3D human pose estimation with adaptive receptive fields and dilated temporal convolutions
http://arxiv.org/abs/2005.13797
AUTHORS:        Michael Shin ; Eduardo Castillo ; Irene Font Peradejordi ; Shobhna Jayaraman
HIGHLIGHT:      In this work, we demonstrate that receptive fields in 3D pose estimation can be effectively specified using optical flow.

79, TITLE:      A Feature-map Discriminant Perspective for Pruning Deep Neural Networks
http://arxiv.org/abs/2005.13796
AUTHORS:        Zejiang Hou ; Sun-Yuan Kung
HIGHLIGHT:      In this paper, we present a new mathematical formulation to accurately and efficiently quantify the feature-map discriminativeness, which gives rise to a novel criterion,Discriminant Information(DI).

80, TITLE:      Explainable deep learning models in medical image analysis
http://arxiv.org/abs/2005.13799
AUTHORS:        Amitojdeep Singh ; Sourya Sengupta ; Vasudevan Lakshminarayanan
COMMENTS:       Preprint submitted to J.Imaging, MDPI
HIGHLIGHT:      Recent explainability studies aim to show the features that influence the decision of a model the most.

81, TITLE:      ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents
http://arxiv.org/abs/2005.13798
AUTHORS:        Ali Ahmadvand ; Harshita Sahijwani ; Jason Ingyu Choi ; Eugene Agichtein
COMMENTS:       CIKM 2019
HIGHLIGHT:      To complement our model, we propose a simple and effective method for generating synthetic training data, to augment the typically limited amounts of labeled training data, using commonly available knowledge bases to generate additional labeled utterances.

82, TITLE:      In search of isoglosses: continuous and discrete language embeddings in Slavic historical phonology
http://arxiv.org/abs/2005.13575
AUTHORS:        Chundra A. Cathcart ; Florian Wandl
HIGHLIGHT:      We employ models using three different types of language embedding (dense, sigmoid, and straight-through).

83, TITLE:      Network Fusion for Content Creation with Conditional INNs
http://arxiv.org/abs/2005.13580
AUTHORS:        Robin Rombach ; Patrick Esser ; Björn Ommer
COMMENTS:       AI for Content Creation at CVPR2020
HIGHLIGHT:      Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them.

84, TITLE:      Antenna Optimization Using a New Evolutionary Algorithm Based on Tukey-Lambda Probability Distribution
http://arxiv.org/abs/2005.13594
AUTHORS:        Vahraz Jamnejad ; Ahmad Hoorfar
COMMENTS:       5 pages, to be submitted to IEEE ACCESS
HIGHLIGHT:      In this paper, we introduce a new evolutionary optimization algorithm based on Tukey's symmetric lambda distribution.

85, TITLE:      Breiman's "Two Cultures" Revisited and Reconciled
http://arxiv.org/abs/2005.13596
AUTHORS:         Subhadeep ;  Mukhopadhyay ; Kaijun Wang
COMMENTS:       This paper celebrates the 70th anniversary of Statistical Machine Learning--- how far we've come, and how far we have to go. Keywords: Integrated statistical learning theory, Exploratory machine learning, Uncertainty prediction machine, ML-powered modern applied statistics, Information theory
HIGHLIGHT:      This article presents a solution by establishing a link between the two cultures.

86, TITLE:      MACER: A Modular Framework for Accelerated Compilation Error Repair
http://arxiv.org/abs/2005.14015
AUTHORS:        Darshak Chhatbar ; Umair Z. Ahmed ; Purushottam Kar
COMMENTS:       19 pages, 9 figures. A short version of this paper will appear at the 21st International Conference on Artificial Intelligence in Education (AIED). Code for the MACER tool-chain is available at https://github.com/purushottamkar/macer/
HIGHLIGHT:      We present MACER, a novel technique for accelerated error repair based on a modular segregation of the repair process into repair identification and repair application.



==========Updates to Previous Papers==========
1, TITLE:       Reconciling Event Structures with Modern Multiprocessors
http://arxiv.org/abs/1911.06567
AUTHORS:        Evgenii Moiseenko ; Anton Podkopaev ; Ori Lahav ; Orestis Melkonian ; Viktor Vafeiadis
HIGHLIGHT:      In this paper, we prove the correctness in Coq of the intended compilation schemes for Weakestmo to a range of hardware memory models (x86, POWER, ARMv7, ARMv8, RISC-V).

2, TITLE:       Prediction of Thrombectomy Functional Outcomes using Multimodal Data
http://arxiv.org/abs/2005.13061
AUTHORS:        Zeynel A. Samak ; Philip Clatworthy ; Majid Mirmehdi
COMMENTS:       Accepted at Medical Image Understanding and Analysis (MIUA) 2020
HIGHLIGHT:      We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment.

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

4, TITLE:       Multimodal Interaction-aware Motion Prediction for Autonomous Street Crossing
http://arxiv.org/abs/1808.06887
AUTHORS:        Noha Radwan ; Wolfram Burgard ; Abhinav Valada
HIGHLIGHT:      In this paper, we address this challenge and propose a multimodal convolutional neural network framework to predict the safety of a street intersection for crossing.

5, TITLE:       RelDenClu:A Relative Density based Biclustering Method for identifying non-linear feature relations with an Application to identify factors effecting spread of COVID-19
http://arxiv.org/abs/1811.04661
AUTHORS:        Namita Jain ; Susmita Ghosh ; C. A. Murthy
HIGHLIGHT:      The proposed method, RelDenClu uses the local variations in marginal and joint densities for each pair of features to find the subset of observations, which forms the bases of the relation between them.

6, TITLE:       One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
http://arxiv.org/abs/2004.08067
AUTHORS:        Jaeyeon Jang ; Chang Ouk Kim
COMMENTS:       16 pages, 11 figures
HIGHLIGHT:      In this paper, we propose a DNN structure in which multiple one-vs-rest sigmoid networks follow a convolutional neural network feature extractor.

7, TITLE:       Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
http://arxiv.org/abs/2005.07654
AUTHORS:        Asan Agibetov ; Matthias Samwald
HIGHLIGHT:      In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes.

8, TITLE:       Emergence of Compositional Language with Deep Generational Transmission
http://arxiv.org/abs/1904.09067
AUTHORS:        Michael Cogswell ; Jiasen Lu ; Stefan Lee ; Devi Parikh ; Dhruv Batra
HIGHLIGHT:      In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language.

9, TITLE:       Classification of Spam Emails through Hierarchical Clustering and Supervised Learning
http://arxiv.org/abs/2005.08773
AUTHORS:        Francisco Jáñez-Martino ; Eduardo Fidalgo ; Santiago González-Martínez ; Javier Velasco-Mata
COMMENTS:       4 pages, 2 figures, to be published in conference JNIC 2020
HIGHLIGHT:      For the first time in literature, we propose to classify spam email in categories to improve the handle of already detected spam emails, instead of just using a binary model.

10, TITLE:      Region adaptive graph fourier transform for 3d point clouds
http://arxiv.org/abs/2003.01866
AUTHORS:        Eduardo Pavez ; Benjamin Girault ; Antonio Ortega ; Philip A. Chou
COMMENTS:       5 pages, 3 figures, accepted ICIP 2020
HIGHLIGHT:      We introduce the Region Adaptive Graph Fourier Transform (RA-GFT) for compression of 3D point cloud attributes.

11, TITLE:      A method for detecting text of arbitrary shapes in natural scenes that improves text spotting
http://arxiv.org/abs/1911.07046
AUTHORS:        Qitong Wang ; Yi Zheng ; Margrit Betke
COMMENTS:       Accepted by IEEE CVPR-W 2020
HIGHLIGHT:      The main contribution of our work is the text detection component, which we call UHT, short for UNet, Heatmap, and Textfill.

12, TITLE:      Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model
http://arxiv.org/abs/2003.13850
AUTHORS:        Ifeatu Ezenwe ; Alok Joshi ; KongFatt Wong-Lin
COMMENTS:       6 pages, 6 figures
HIGHLIGHT:      In this work, we investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations.

13, TITLE:      Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks
http://arxiv.org/abs/2004.11676
AUTHORS:        Narinder Singh Punn ; Sonali Agarwal
HIGHLIGHT:      Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images.

14, TITLE:      Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
http://arxiv.org/abs/2005.12855
AUTHORS:        Alexander Wong ; Zhong Qiu Lin ; Linda Wang ; Audrey G. Chung ; Beiyi Shen ; Almas Abbasi ; Mahsa Hoshmand-Kochi ; Timothy Q. Duong
COMMENTS:       7 pages
HIGHLIGHT:      In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system.

15, TITLE:      NASNet: A Neuron Attention Stage-by-Stage Net for Single Image Deraining
http://arxiv.org/abs/1912.03151
AUTHORS:        Xu Qin ; Zhilin Wang
COMMENTS:       underreviewed by conference
HIGHLIGHT:      In this paper, we propose a novel end-to-end Neuron Attention Stage-by-Stage Net (NASNet), which can solve all types of rain model tasks efficiently.

16, TITLE:      Derivation of Symmetric PCA Learning Rules from a Novel Objective Function
http://arxiv.org/abs/2005.11689
AUTHORS:        Ralf Möller
COMMENTS:       Added a paragraph to section 'Conclusion' describing a disadvantage of the novel learning rules
HIGHLIGHT:      Here we introduce an alternative objective function where it is not necessary to introduce fixed weight factors; instead, the alternative objective function uses squared summands.

17, TITLE:      CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation
http://arxiv.org/abs/2005.00329
AUTHORS:        Lei Shen ; Yang Feng
COMMENTS:       To appear at ACL 2020 (long paper)
HIGHLIGHT:      To alleviate these problems, we propose a novel framework named Curriculum Dual Learning (CDL) which extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively.

18, TITLE:      Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks
http://arxiv.org/abs/2004.06569
AUTHORS:        Davood Karimi ; Ali Gholipour
HIGHLIGHT:      In this study, we address some of the main unresolved issues regarding these models.

19, TITLE:      Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samples
http://arxiv.org/abs/2005.11699
AUTHORS:        Andrei Ivanov ; Uwe Iben ; Anna Golovkina
HIGHLIGHT:      The paper describes practical results on real experiments with both a simple pendulum and one of the largest worldwide X-ray source.

20, TITLE:      Attention in Natural Language Processing
http://arxiv.org/abs/1902.02181
AUTHORS:        Andrea Galassi ; Marco Lippi ; Paolo Torroni
HIGHLIGHT:      In this paper, we define a unified model for attention architectures in natural language processing, with a focus on those designed to work with vector representations of the textual data.

21, TITLE:      SASL: Saliency-Adaptive Sparsity Learning for Neural Network Acceleration
http://arxiv.org/abs/2003.05891
AUTHORS:        Jun Shi ; Jianfeng Xu ; Kazuyuki Tasaka ; Zhibo Chen
HIGHLIGHT:      In this paper, we propose a Saliency-Adaptive Sparsity Learning (SASL) approach for further optimization.

22, TITLE:      Knowledge forest: a novel model to organize knowledge fragments
http://arxiv.org/abs/1912.06825
AUTHORS:        Qinghua Zheng ; Jun Liu ; Hongwei Zeng ; Zhaotong Guo ; Bei Wu ; Bifan Wei
COMMENTS:       Accepted for publication in Science China Information Science
HIGHLIGHT:      To solve the knowledge fragmentization problem, we propose a novel knowledge organization model, knowledge forest, which consists of facet trees and learning dependencies.

23, TITLE:      Summarizing the performances of a background subtraction algorithm measured on several videos
http://arxiv.org/abs/2002.05654
AUTHORS:        Sébastien Piérard ; Marc Van Droogenbroeck
COMMENTS:       Copyright 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      In this paper, we present a theoretical approach to summarize the performances for multiple videos that preserves the relationships between performance indicators.

24, TITLE:      $R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge
http://arxiv.org/abs/2004.13248
AUTHORS:        Tuhin Chakrabarty ; Debanjan Ghosh ; Smaranda Muresan ; Nanyun Peng
COMMENTS:       Accepted at the 2020 Annual Conference of the Association for Computational Linguistics (ACL)
HIGHLIGHT:      We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence.

25, TITLE:      Lessons from reinforcement learning for biological representations of space
http://arxiv.org/abs/1912.06615
AUTHORS:        Alex Muryy ; Siddharth Narayanaswamy ; Nantas Nardelli ; Andrew Glennerster ; Philip H. S. Torr
COMMENTS:       40 pages including Appendix, 6 figures plus 3 figures in Appendix. Accepted for publication in Vision Research
HIGHLIGHT:      In this paper, we focus on reinforcement learning methods that reward an agent for arriving at a target image without any attempt to build up a 3D 'map'.

26, TITLE:      Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
http://arxiv.org/abs/1911.07731
AUTHORS:        Bernhard Stimpel ; Christopher Syben ; Franziska Schirrmacher ; Philipp Hoelter ; Arnd Dörfler ; Andreas Maier
HIGHLIGHT:      Consequently, we propose the use of the locally linear guided filter in combination with a learned guidance map for general purpose medical image processing.

27, TITLE:      Appraisal Theories for Emotion Classification in Text
http://arxiv.org/abs/2003.14155
AUTHORS:        Jan Hofmann ; Enrica Troiano ; Kai Sassenberg ; Roman Klinger
HIGHLIGHT:      With this paper, we propose to make such interpretations of events explicit, following theories of cognitive appraisal of events and show their potential for emotion classification when being encoded in classification models.

28, TITLE:      Amora: Black-box Adversarial Morphing Attack
http://arxiv.org/abs/1912.03829
AUTHORS:        Run Wang ; Felix Juefei-Xu ; Qing Guo ; Yihao Huang ; Xiaofei Xie ; Lei Ma ; Yang Liu
HIGHLIGHT:      In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora).

29, TITLE:      Testing Membership for Timed Automata
http://arxiv.org/abs/1912.08277
AUTHORS:        Richard Lassaigne ; Michel de Rougemont
COMMENTS:       26 pages
HIGHLIGHT:      Given a timed automata which admits thick components and a timed word $x$, we present a tester which decides if $x$ is in the language of the automaton or if $x$ is $\epsilon$-far from the language, using finitely many samples taken from the weighted time distribution $\mu$ associated with an input $x$.

30, TITLE:      Generative Tweening: Long-term Inbetweening of 3D Human Motions
http://arxiv.org/abs/2005.08891
AUTHORS:        Yi Zhou ; Jingwan Lu ; Connelly Barnes ; Jimei Yang ; Sitao Xiang ; Hao li
HIGHLIGHT:      To this end, we introduce the problem of long-term inbetweening, which involves automatically synthesizing complex motions over a long time interval given very sparse keyframes by users.

31, TITLE:      Optimizing Through Learned Errors for Accurate Sports Field Registration
http://arxiv.org/abs/1909.08034
AUTHORS:        Wei Jiang ; Juan Camilo Gamboa Higuera ; Baptiste Angles ; Weiwei Sun ; Mehrsan Javan ; Kwang Moo Yi
HIGHLIGHT:      We propose an optimization-based framework to register sports field templates onto broadcast videos.

32, TITLE:      HAMLET -- A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection
http://arxiv.org/abs/2001.11261
AUTHORS:        Mischa Schmidt ; Julia Gastinger ; Sébastien Nicolas ; Anett Schülke
COMMENTS:       8 pages, 8 figures; IJCNN 2020: International Joint Conference on Neural Networks
HIGHLIGHT:      This work addresses that insight by introducing HAMLET, which extends the bandit approach with learning curve extrapolation and computation time-awareness for selecting among a set of machine learning algorithms.

33, TITLE:      CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents
http://arxiv.org/abs/2004.12629
AUTHORS:        Devashish Prasad ; Ayan Gadpal ; Kshitij Kapadni ; Manish Visave ; Kavita Sultanpure
COMMENTS:       Paper has been accepted at CVPR Workshop 2020 (CVPR2020 Workshop on Text and Documents in the Deep Learning Era)
HIGHLIGHT:      In this paper, we present an improved deep learning-based end to end approach for solving both problems of table detection and structure recognition using a single Convolution Neural Network (CNN) model.

34, TITLE:      End-to-End Object Detection with Transformers
http://arxiv.org/abs/2005.12872
AUTHORS:        Nicolas Carion ; Francisco Massa ; Gabriel Synnaeve ; Nicolas Usunier ; Alexander Kirillov ; Sergey Zagoruyko
HIGHLIGHT:      We present a new method that views object detection as a direct set prediction problem.

35, TITLE:      Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis
http://arxiv.org/abs/2005.11797
AUTHORS:        Pranav Poduval ; Hrushikesh Loya ; Amit Sethi
COMMENTS:       Meaningful priors on the functional space rather than the weight space, result in well calibrated uncertainty estimates
HIGHLIGHT:      In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.

36, TITLE:      M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
http://arxiv.org/abs/2004.09722
AUTHORS:        Baichuan Huang ; Hongwei Yi ; Can Huang ; Yijia He ; Jingbin Liu ; Xiao Liu
COMMENTS:       Welcome to communicate with the author by the repo https://github.com/whubaichuan/M3VSNet
HIGHLIGHT:      In this paper, we propose a novel unsupervised multi-metric MVS network, named M^3VSNet, for dense point cloud reconstruction without any supervision.

37, TITLE:      Robust Encoder-Decoder Learning Framework towards Offline Handwritten Mathematical Expression Recognition Based on Multi-Scale Deep Neural Network
http://arxiv.org/abs/1902.05376
AUTHORS:        Guangcun Shan ; Hongyu Wang ; Wei Liang
COMMENTS:       11 pages, 16 figures
HIGHLIGHT:      As a result, the model proposed in the present work has achieved a WER error of 25.715% and ExpRate of 28.216%.

38, TITLE:      How to do Physics-based Learning
http://arxiv.org/abs/2005.13531
AUTHORS:        Michael Kellman ; Michael Lustig ; Laura Waller
COMMENTS:       3 pages, 2 figures, linked repository https://github.com/kellman/physics_based_learning
HIGHLIGHT:      The goal of this tutorial is to explain step-by-step how to implement physics-based learning for the rapid prototyping of a computational imaging system.

39, TITLE:      Low-Rank Pairwise Alignment Bilinear Network For Few-Shot Fine-Grained Image Classification
http://arxiv.org/abs/1908.01313
AUTHORS:        Huaxi Huang ; Junjie Zhang ; Jian Zhang ; Jingsong Xu ; Qiang Wu
HIGHLIGHT:      To filling the classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting.

40, TITLE:      JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
http://arxiv.org/abs/2005.12209
AUTHORS:        Fengze Liu ; Jingzheng Cai ; Yuankai Huo ; Chi-Tung Cheng ; Ashwin Raju ; Dakai Jin ; Jing Xiao ; Alan Yuille ; Le Lu ; ChienHung Liao ; Adam P Harrison
HIGHLIGHT:      In this work, we propose a novel multi-task learning system, JSSR, based on an end-to-end 3D convolutional neural network that is composed of a generator, a register and a segmentor, for the tasks of synthesis, registration and segmentation, respectively.

41, 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.

42, TITLE:      Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
http://arxiv.org/abs/2005.03501
AUTHORS:        Lena Maier-Hein ; Martin Wagner ; Tobias Ross ; Annika Reinke ; Sebastian Bodenstedt ; Peter M. Full ; Hellena Hempe ; Diana Mindroc-Filimon ; Patrick Scholz ; Thuy Nuong Tran ; Pierangela Bruno ; Anna Kisilenko ; Benjamin Müller ; Tornike Davitashvili ; Manuela Capek ; Minu Tizabi ; Matthias Eisenmann ; Tim J. Adler ; Janek Gröhl ; Melanie Schellenberg ; Silvia Seidlitz ; T. Y. Emmy Lai ; Veith Roethlingshoefer ; Fabian Both ; Sebastian Bittel ; Marc Mengler ; Martin Apitz ; Stefanie Speidel ; Hannes G. Kenngott ; Beat P. Müller-Stich
COMMENTS:       Submitted to Nature Scientific Data
HIGHLIGHT:      This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods.

43, TITLE:      Intersectional Bias in Hate Speech and Abusive Language Datasets
http://arxiv.org/abs/2005.05921
AUTHORS:        Jae Yeon Kim ; Carlos Ortiz ; Sarah Nam ; Sarah Santiago ; Vivek Datta
HIGHLIGHT:      Algorithms are widely applied to detect hate speech and abusive language in social media.

44, TITLE:      RHLE: Relational Reasoning for Existential Program Verification
http://arxiv.org/abs/2002.02904
AUTHORS:        Robert Dickerson ; Qianchuan Ye ; Benjamin Delaware
HIGHLIGHT:      This paper proposes a flexible way to underapproximate the behaviors of nondeterministic choices so that clients can soundly reason about the existence of desirable behaviors, while at the same time permitting some freedom in how those choices are implemented.

45, TITLE:      AutoSweep: Recovering 3D Editable Objectsfrom a Single Photograph
http://arxiv.org/abs/2005.13312
AUTHORS:        Xin Chen ; Yuwei Li ; Xi Luo ; Tianjia Shao ; Jingyi Yu ; Kun Zhou ; Youyi Zheng
COMMENTS:       10 pages, 12 figures
HIGHLIGHT:      This paper presents a fully automatic framework for extracting editable 3D objects directly from a single photograph.

46, TITLE:      Detection and Classification of Industrial Signal Lights for Factory Floors
http://arxiv.org/abs/2004.11187
AUTHORS:        Felix Nilsson ; Jens Jakobsen ; Fernando Alonso-Fernandez
COMMENTS:       Published at Proc International Conference on Intelligent Systems and Computer Vision, ISCV, Fez, Morocco, 9-11 June 2020
HIGHLIGHT:      Accordingly, the goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor.

47, TITLE:      Transition-based Semantic Dependency Parsing with Pointer Networks
http://arxiv.org/abs/2005.13344
AUTHORS:        Daniel Fernández-González ; Carlos Gómez-Rodríguez
COMMENTS:       Proceedings of ACL 2020. 12 pages
HIGHLIGHT:      In order to further test the capabilities of these powerful neural networks on a harder NLP problem, we propose a transition system that, thanks to Pointer Networks, can straightforwardly produce labelled directed acyclic graphs and perform semantic dependency parsing.

48, TITLE:      A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews
http://arxiv.org/abs/2005.13362
AUTHORS:        Edison Marrese-Taylor ; Cristian Rodriguez-Opazo ; Jorge A. Balazs ; Stephen Gould ; Yutaka Matsuo
COMMENTS:       Second Grand Challenge and Workshop on Multimodal Language ACL 2020
HIGHLIGHT:      In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them.

49, TITLE:      Understanding Cross-Lingual Syntactic Transfer in Multilingual Recurrent Neural Networks
http://arxiv.org/abs/2003.14056
AUTHORS:        Prajit Dhar ; Arianna Bisazza
COMMENTS:       v2: Added acknowledgements, 9 pages single column with 6 figures
HIGHLIGHT:      In this paper we dissect different forms of cross-lingual transfer and look for its most determining factors, using a variety of models and probing tasks.

50, TITLE:      False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
http://arxiv.org/abs/2005.13153
AUTHORS:        Sungmin Woo ; Sangwon Hwang ; Woojin Kim ; Junhyeop Lee ; Dogyoon Lee ; Sangyoun Lee
COMMENTS:       Accepted by ICIP 2020
HIGHLIGHT:      To address the issue, we introduce Penetrated Point Classifier (PPC) based on the underlying property of LiDAR that points cannot be generated behind vehicles.

51, TITLE:      Driver Gaze Estimation in the Real World: Overcoming the Eyeglass Challenge
http://arxiv.org/abs/2002.02077
AUTHORS:        Akshay Rangesh ; Bowen Zhang ; Mohan M. Trivedi
HIGHLIGHT:      In this study, we offer solutions to address these problems encountered in the real world.

52, TITLE:      Noise-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer
http://arxiv.org/abs/2005.08806
AUTHORS:        Yang Chen ; Zongqing Lu ; Xuechen Zhang ; Lei Chen ; Qingmin Liao
COMMENTS:       Accepted by IEEE ICIP 2020
HIGHLIGHT:      In this paper, inspired by previous canonical definition of cost volume, we propose the noise-sampling cross entropy loss function to regularize the cost volume produced by deep neural networks to be unimodal and coherent.