2020.07.13.txt

==========New Papers==========
1, TITLE:       Neural Knowledge Extraction From Cloud Service Incidents
http://arxiv.org/abs/2007.05505
AUTHORS:        Manish Shetty ; Chetan Bansal ; Sumit Kumar ; Nikitha Rao ; Nachiappan Nagappan ; Thomas Zimmermann
HIGHLIGHT:      In this work, we address the fundamental problem of structured knowledge extraction from service incidents.

2, TITLE:       Scientific Discovery by Generating Counterfactuals using Image Translation
http://arxiv.org/abs/2007.05500
AUTHORS:        Arunachalam Narayanaswamy ; Subhashini Venugopalan ; Dale R. Webster ; Lily Peng ; Greg Corrado ; Paisan Ruamviboonsuk ; Pinal Bavishi ; Michael Brenner ; Philip Nelson ; Avinash V. Varadarajan
COMMENTS:       Accepted at MICCAI 2020. This version combines camera-ready and supplement
HIGHLIGHT:      We demonstrate that the proposed framework is able to explain the underlying scientific mechanism, thus bridging the gap between the model's performance and human understanding.

3, TITLE:       Representations for Stable Off-Policy Reinforcement Learning
http://arxiv.org/abs/2007.05520
AUTHORS:        Dibya Ghosh ; Marc G. Bellemare
COMMENTS:       ICML 2020
HIGHLIGHT:      In this paper, we formally show that there are indeed nontrivial state representations under which the canonical TD algorithm is stable, even when learning off-policy.

4, TITLE:       AViD Dataset: Anonymized Videos from Diverse Countries
http://arxiv.org/abs/2007.05515
AUTHORS:        AJ Piergiovanni ; Michael S. Ryoo
COMMENTS:       https://github.com/piergiaj/AViD
HIGHLIGHT:      We introduce a new public video dataset for action recognition: Anonymized Videos from Diverse countries (AViD).

5, TITLE:       A Causal Linear Model to Quantify Edge Unfairness for Unfair Edge Prioritization and Discrimination Removal
http://arxiv.org/abs/2007.05516
AUTHORS:        Pavan Ravishankar ; Pranshu Malviya ; Balaraman Ravindran
COMMENTS:       Accepted in the 1st Workshop on Law and Machine Learning, 37th International Conference on Machine Learning, 2020; First two authors contributed equally
HIGHLIGHT:      Prior work of (Zhang, et al., 2017) identifies and removes discrimination after data is generated but does not suggest a methodology to mitigate unfairness in the data generation phase.

6, TITLE:       Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation
http://arxiv.org/abs/2007.05393
AUTHORS:        Shen Wang ; Kongming Liang ; Yiming Li ; Yizhou Yu ; Yizhou Wang
HIGHLIGHT:      To address these challenges, we propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet.

7, TITLE:       Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques
http://arxiv.org/abs/2007.05149
AUTHORS:        Yijun Zhao ; Jacek Ossowski ; Xuming Wang ; Shangjin Li ; Orrin Devinsky ; Samantha P. Martin ; Heath R. Pardoe
COMMENTS:       10 pages, 8 figures
HIGHLIGHT:      We introduce a deep learning-based MRI artifact reduction model (DMAR) to localize and correct head motion artifacts in brain MRI scans. We further introduce a set of novel data augmentation techniques to address the high dimensionality of MRI images and the scarcity of available data.

8, TITLE:       Self-Reflective Variational Autoencoder
http://arxiv.org/abs/2007.05166
AUTHORS:        Ifigeneia Apostolopoulou ; Elan Rosenfeld ; Artur Dubrawski
HIGHLIGHT:      In this work, we introduce an orthogonal solution, which we call self-reflective inference.

9, TITLE:       Rain Streak Removal in a Video to Improve Visibility by TAWL Algorithm
http://arxiv.org/abs/2007.05167
AUTHORS:        Muhammad Rafiqul Islam ; Manoranjan Paul
HIGHLIGHT:      In this paper, we propose a novel and simple method by combining three novel extracted features focusing on temporal appearance, wide shape and relative location of the rain streak and we called it TAWL (Temporal Appearance, Width, and Location) method.

10, TITLE:      SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation
http://arxiv.org/abs/2007.05168
AUTHORS:        John Yang ; Hyung Jin Chang ; Seungeui Lee ; Nojun Kwak
HIGHLIGHT:      In this paper, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images.

11, TITLE:      Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling
http://arxiv.org/abs/2007.05163
AUTHORS:        Leonard K. M. Poon ; Nevin L. Zhang ; Haoran Xie ; Gary Cheng
HIGHLIGHT:      Therefore, we propose a method for extracting and selecting collocations as a preprocessing step for HLTA.

12, TITLE:      SacreROUGE: An Open-Source Library for Using and Developing Summarization Evaluation Metrics
http://arxiv.org/abs/2007.05374
AUTHORS:        Daniel Deutsch ; Dan Roth
HIGHLIGHT:      We present SacreROUGE, an open-source library for using and developing summarization evaluation metrics.

13, TITLE:      AGI Agent Safety by Iteratively Improving the Utility Function
http://arxiv.org/abs/2007.05411
AUTHORS:        Koen Holtman
COMMENTS:       Part 1 of this work is a preprint of a conference paper to appear in: Proceedings of the 13th International Conference on Artificial General Intelligence (AGI-20), Springer LNAI 12177 (2020). Part 2 has additional, new research results that go beyond those in the conference paper
HIGHLIGHT:      We present an AGI safety layer that creates a special dedicated input terminal to support the iterative improvement of an AGI agent's utility function.

14, TITLE:      Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
http://arxiv.org/abs/2007.05146
AUTHORS:        Xinghao Chen ; Yiman Zhang ; Yunhe Wang ; Han Shu ; Chunjing Xu ; Chang Xu
HIGHLIGHT:      This paper proposes to learn a lightweight video style transfer network via knowledge distillation paradigm.

15, TITLE:      Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
http://arxiv.org/abs/2007.05405
AUTHORS:        Chinedu Innocent Nwoye ; Cristians Gonzalez ; Tong Yu ; Pietro Mascagni ; Didier Mutter ; Jacques Marescaux ; Nicolas Padoy
COMMENTS:       13 pages, 4 figures, 6 tables. Accepted and to be published in MICCAI 2020
HIGHLIGHT:      In this work, we tackle the recognition of fine-grained activities, modeled as action triplets <instrument, verb, target> representing the tool activity. To this end, we introduce a new laparoscopic dataset, CholecT40, consisting of 40 videos from the public dataset Cholec80 in which all frames have been annotated using 128 triplet classes.

16, TITLE:      Machine Learning Explainability for External Stakeholders
http://arxiv.org/abs/2007.05408
AUTHORS:        Umang Bhatt ; McKane Andrus ; Adrian Weller ; Alice Xiang
HIGHLIGHT:      In this paper, we provide a short summary of various case studies of explainable machine learning, lessons from those studies, and discuss open challenges.

17, TITLE:      MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System
http://arxiv.org/abs/2007.05402
AUTHORS:        Jinho Lee ; Raehyun Kim ; Seok-Won Yi ; Jaewoo Kang
COMMENTS:       7 pages, 5 figures, IJCAI-PRICAI 2020
HIGHLIGHT:      In this paper, we propose the Multi-Agent reinforcement learning-based Portfolio management System (MAPS).

18, TITLE:      Biological credit assignment through dynamic inversion of feedforward networks
http://arxiv.org/abs/2007.05112
AUTHORS:        William F. Podlaski ; Christian K. Machens
HIGHLIGHT:      Overall, our work introduces an alternative perspective on credit assignment in the brain, and proposes a special role for temporal dynamics and feedback control during learning.

19, TITLE:      Spine Landmark Localization with combining of Heatmap Regression and Direct Coordinate Regression
http://arxiv.org/abs/2007.05355
AUTHORS:        Wanhong Huang ; Chunxi Yang ; TianHong Hou
HIGHLIGHT:      Spine Landmark Localization with combining of Heatmap Regression and Direct Coordinate Regression

20, TITLE:      Solving the Clustered Traveling Salesman Problem via TSP methods
http://arxiv.org/abs/2007.05254
AUTHORS:        Yongliang Lu ; Jin-Kao Hao ; Qinghua Wu
COMMENTS:       21 pages, 6 figures
HIGHLIGHT:      In this work, we explore an uncharted solution approach that solves the CTSP by transforming it to the well-studied TSP.

21, TITLE:      FC2RN: A Fully Convolutional Corner Refinement Network for Accurate Multi-Oriented Scene Text Detection
http://arxiv.org/abs/2007.05113
AUTHORS:        Xugong Qin ; Yu Zhou ; Dayan Wu ; Yinliang Yue ; Weiping Wang
HIGHLIGHT:      In this work, we aim to improve this while keeping the pipeline simple.

22, TITLE:      Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels
http://arxiv.org/abs/2007.05351
AUTHORS:        Christof A. Bertram ; Mitko Veta ; Christian Marzahl ; Nikolas Stathonikos ; Andreas Maier ; Robert Klopfleisch ; Marc Aubreville
COMMENTS:       10 pages, submitted to LABELS@MICCAI 2020
HIGHLIGHT:      To tackle this, we present an alternative set of labels for the images of the auxiliary mitosis dataset of the TUPAC16 challenge.

23, TITLE:      Multi-Emitter MAP-Elites: Improving quality, diversity and convergence speed with heterogeneous sets of emitters
http://arxiv.org/abs/2007.05352
AUTHORS:        Antoine Cully
HIGHLIGHT:      In this paper, we introduce a novel algorithm named Multi-Emitter MAP-Elites (ME-MAP-Elites) that improves the quality, diversity and convergence speed of MAP-Elites.

24, TITLE:      Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification
http://arxiv.org/abs/2007.05009
AUTHORS:        Pengyu Yuan ; Aryan Mobiny ; Jahandar Jahanipour ; Xiaoyang Li ; Pietro Antonio Cicalese ; Badrinath Roysam ; Vishal Patel ; Maric Dragan ; Hien Van Nguyen
HIGHLIGHT:      In this paper, we propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a small number of training examples.

25, TITLE:      StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification
http://arxiv.org/abs/2007.05008
AUTHORS:        Pietro Antonio Cicalese ; Aryan Mobiny ; Pengyu Yuan ; Jan Becker ; Chandra Mohan ; Hien Van Nguyen
HIGHLIGHT:      We propose a novel pipeline to build robust deep neural networks for AMR classification based on StyPath, a histological data augmentation technique that leverages a light weight style-transfer algorithm as a means to reduce sample-specific bias.

26, TITLE:      Dota Underlords game is NP-complete
http://arxiv.org/abs/2007.05020
AUTHORS:        Alexander A. Ponomarenko ; Dmitry V. Sirotkin
HIGHLIGHT:      In this paper, we demonstrate how the problem of the optimal team choice in the popular computer game Dota Underlords can be reduced to the problem of linear integer programming.

27, TITLE:      Continual Adaptation for Deep Stereo
http://arxiv.org/abs/2007.05233
AUTHORS:        Matteo Poggi ; Alessio Tonioni ; Fabio Tosi ; Stefano Mattoccia ; Luigi Di Stefano
COMMENTS:       Extended version of CVPR 2019 paper "Real-time self-adaptive deep stereo"
HIGHLIGHT:      Purposely, we propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments.

28, TITLE:      Pragmatic information in translation: a corpus-based study of tense and mood in English and German
http://arxiv.org/abs/2007.05234
AUTHORS:        Anita Ramm ; Ekaterina Lapshinova-Koltunski ; Alexander Fraser
COMMENTS:       Technical Report of CIS, LMU Munich. September 19th, 2019
HIGHLIGHT:      Human translators do not find this correspondence easy, and as we will show through careful analysis, there are no simplistic ways to map tense and mood from one language to another.

29, TITLE:      Geometric Style Transfer
http://arxiv.org/abs/2007.05471
AUTHORS:        Xiao-Chang Liu ; Xuan-Yi Li ; Ming-Ming Cheng ; Peter Hall
COMMENTS:       10 pages, 12 figures
HIGHLIGHT:      Our contribution is to introduce a neural architecture that supports transfer of geometric style.

30, TITLE:      Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
http://arxiv.org/abs/2007.05230
AUTHORS:        Jing Yao ; Danfeng Hong ; Jocelyn Chanussot ; Deyu Meng ; Xiaoxiang Zhu ; Zongben Xu
HIGHLIGHT:      To this end, we propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet for short, to enhance the spatial resolution of HSI by means of higher-spatial-resolution multispectral image (MSI).

31, TITLE:      Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
http://arxiv.org/abs/2007.05225
AUTHORS:        Qingsong Yao ; Zecheng He ; Hu Han ; S. Kevin Zhou
COMMENTS:       accepted by MICCAI2020
HIGHLIGHT:      Specifically, we propose a novel Adaptive Targeted Iterative FGSM (ATI-FGSM) attack against the state-of-the-art models in multiple landmark detection.

32, TITLE:      Efficient ancilla-free reversible and quantum circuits for the Hidden Weighted Bit function
http://arxiv.org/abs/2007.05469
AUTHORS:        Sergey Bravyi ; Theodore J. Yoder ; Dmitri Maslov
COMMENTS:       20 pages, 4 figures
HIGHLIGHT:      In this paper, we refute the exponential hardness conjecture by developing a polynomial-size reversible ancilla-free circuit computing the Hidden Weighted Bit function.

33, TITLE:      Target set selection with maximum activation time
http://arxiv.org/abs/2007.05246
AUTHORS:        Lucas Keiler ; Carlos Vinicius G. C. Lima ; Ana Karolinna Maia ; Rudini Sampaio ; Ignasi Sau
COMMENTS:       27 pages, 12 figures
HIGHLIGHT:      In this article, we investigate its variant, which we call TSS-time, in which the goal is to find a target set $S_0$ that maximizes $t_{\tau}(S_0)$.

34, TITLE:      STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation
http://arxiv.org/abs/2007.05481
AUTHORS:        Pierre Godet ; Alexandre Boulch ; Aurélien Plyer ; Guy Le Besnerais
COMMENTS:       9 pages, 7 figures, 4 tables
HIGHLIGHT:      We present a new lightweight CNN-based algorithm for multi-frame optical flow estimation.

35, TITLE:      $n$-Reference Transfer Learning for Saliency Prediction
http://arxiv.org/abs/2007.05104
AUTHORS:        Yan Luo ; Yongkang Wong ; Mohan S. Kankanhalli ; Qi Zhao
COMMENTS:       ECCV 2020
HIGHLIGHT:      To solve this problem, we propose a few-shot transfer learning paradigm for saliency prediction, which enables efficient transfer of knowledge learned from the existing large-scale saliency datasets to a target domain with limited labeled examples.

36, TITLE:      Improving Adversarial Robustness by Enforcing Local and Global Compactness
http://arxiv.org/abs/2007.05123
AUTHORS:        Anh Bui ; Trung Le ; He Zhao ; Paul Montague ; Olivier deVel ; Tamas Abraham ; Dinh Phung
COMMENTS:       Proceeding of the European Conference on Computer Vision (ECCV) 2020
HIGHLIGHT:      In this work, based on an observation from a previous study that the representations of a clean data example and its adversarial examples become more divergent in higher layers of a deep neural net, we propose the Adversary Divergence Reduction Network which enforces local/global compactness and the clustering assumption over an intermediate layer of a deep neural network.

37, TITLE:      Artificial Neural Network Approach for the Identification of Clove Buds Origin Based on Metabolites Composition
http://arxiv.org/abs/2007.05125
AUTHORS:         Rustam ; Agus Yodi Gunawan ; Made Tri Ari Penia Kresnowati
COMMENTS:       10 pages, 6 figures, submitted to Acta Polytechnica as scientific journal published by the Czech Technical University in Prague
HIGHLIGHT:      This paper examines the use of artificial neural network approach in identifying the origin of clove buds based on metabolites composition.

38, TITLE:      Progressive Point Cloud Deconvolution Generation Network
http://arxiv.org/abs/2007.05361
AUTHORS:        Le Hui ; Rui Xu ; Jin Xie ; Jianjun Qian ; Jian Yang
COMMENTS:       Accepted to ECCV 2020; Project page: https://github.com/fpthink/PDGN
HIGHLIGHT:      In this paper, we propose an effective point cloud generation method, which can generate multi-resolution point clouds of the same shape from a latent vector.

39, TITLE:      A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images
http://arxiv.org/abs/2007.05324
AUTHORS:        Stefan Gerl ; Johannes C. Paetzold ; Hailong He ; Ivan Ezhov ; Suprosanna Shit ; Florian Kofler ; Amirhossein Bayat ; Giles Tetteh ; Vasilis Ntziachristos ; Bjoern Menze
COMMENTS:       Accepted at International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2020
HIGHLIGHT:      We propose a novel, shape-specific loss function that overcomes discontinuous segmentations and achieves smooth segmentation surfaces while preserving the same volumetric Dice and IoU.

40, TITLE:      Learnable Hollow Kernels for Anatomical Segmentation
http://arxiv.org/abs/2007.05103
AUTHORS:        Elizaveta Lazareva ; Oleg Rogov ; Olga Shegai ; Denis Larionov ; Dmitry V. Dylov
COMMENTS:       21 pages total. Main: 11 pages, 3 figures, 1 table. Supplemental: 10 pages, 8 figures, 2 tables
HIGHLIGHT:      To address this issue, we propose a new class of hollow kernels that learn to 'mimic' the contours of the segmented organ, effectively replicating its shape and structural complexity.

41, TITLE:      TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
http://arxiv.org/abs/2007.05307
AUTHORS:        Yushan Liu ; Markus M. Geipel ; Christoph Tietz ; Florian Buettner
COMMENTS:       Accepted at ECAI 2020 (24th European Conference on Artificial Intelligence)
HIGHLIGHT:      We introduce TIMELY, a probabilistic model that combines pseudotime inference methods with inhomogeneous hidden Markov trees, which addresses this challenge of label inconsistency.

42, TITLE:      Topic Modeling on User Stories using Word Mover's Distance
http://arxiv.org/abs/2007.05302
AUTHORS:        Kim Julian Gülle ; Nicholas Ford ; Patrick Ebel ; Florian Brokhausen ; Andreas Vogelsang
HIGHLIGHT:      In this paper, we focus on topic modeling as a means to identify topics within a large set of crowd-generated user stories and compare three approaches: (1) a traditional approach based on Latent Dirichlet Allocation, (2) a combination of word embeddings and principal component analysis, and (3) a combination of word embeddings and Word Mover's Distance.

43, TITLE:      Pre-trained Word Embeddings for Goal-conditional Transfer Learning in Reinforcement Learning
http://arxiv.org/abs/2007.05196
AUTHORS:        Matthias Hutsebaut-Buysse ; Kevin Mets ; Steven Latré
COMMENTS:       Paper accepted to the ICML 2020 Language in Reinforcement Learning (LaReL) Workshop
HIGHLIGHT:      In this paper, we examine how a pre-trained task-independent language model can make a goal-conditional RL agent more sample efficient.

44, TITLE:      Hyperspectral Imaging to detect Age, Defects and Individual Nutrient Deficiency in Grapevine Leaves
http://arxiv.org/abs/2007.05197
AUTHORS:        Manoranjan Paul ; Sourabhi Debnath ; Tanmoy Debnath ; Suzy Rogiers ; Tintu Baby ; DM Motiur Rahaman ; Lihong Zheng ; Leigh Schmidtke
COMMENTS:       24 pages, 23 figures
HIGHLIGHT:      Hyperspectral Imaging to detect Age, Defects and Individual Nutrient Deficiency in Grapevine Leaves

45, TITLE:      What Can We Learn From Almost a Decade of Food Tweets
http://arxiv.org/abs/2007.05194
AUTHORS:        Uga Sproģis ; Matīss Rikters
HIGHLIGHT:      We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking.

46, TITLE:      Impact of Legal Requirements on Explainability in Machine Learning
http://arxiv.org/abs/2007.05479
AUTHORS:        Adrien Bibal ; Michael Lognoul ; Alexandre de Streel ; Benoît Frénay
COMMENTS:       ICML Workshop on Law and Machine Learning
HIGHLIGHT:      In that perspective, our research analyzes explanation obligations imposed for private and public decision-making, and how they can be implemented by machine learning techniques.

47, TITLE:      Current Advancements on Autonomous Mission Planning and Management Systems: an AUV and UAV perspective
http://arxiv.org/abs/2007.05179
AUTHORS:        Adham Atyabi ; Somaiyeh MahmoudZadeh ; Samia Nefti-Meziani
HIGHLIGHT:      A comprehensive survey over autonomy assessment of UVs, and different aspects of autonomy such as situation awareness, cognition, and decision-making has been provided in this study.

48, TITLE:      SIMBA: Specific Identity Markers for Bone Age Assessment
http://arxiv.org/abs/2007.05454
AUTHORS:        Cristina González ; María Escobar ; Laura Daza ; Felipe Torres ; Gustavo Triana ; Pablo Arbeláez
COMMENTS:       Accepted at MICCAI 2020
HIGHLIGHT:      With this lack of available methods as motivation, we present SIMBA: Specific Identity Markers for Bone Age Assessment.

49, TITLE:      Affine Non-negative Collaborative Representation Based Pattern Classification
http://arxiv.org/abs/2007.05175
AUTHORS:        He-Feng Yin ; Xiao-Jun Wu ; Zhen-Hua Feng ; Josef Kittler
COMMENTS:       submitted to the 25th International Conference on Pattern Recognition (ICPR2020)
HIGHLIGHT:      To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification.

50, TITLE:      Evaluation of Big Data based CNN Models in Classification of Skin Lesions with Melanoma
http://arxiv.org/abs/2007.05446
AUTHORS:        Prasitthichai Naronglerdrit ; Iosif Mporas
COMMENTS:       Series Title: Studies in Computational Intelligence, Book Title: Deep Learning for Cancer Diagnosis, Series Volume: 908, DOI: 10.1007/978-981-15-6321-8, eBook ISBN: 978-981-15-6321-8
HIGHLIGHT:      This chapter presents a methodology for diagnosis of pigmented skin lesions using convolutional neural networks.

51, TITLE:      OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
http://arxiv.org/abs/2007.05205
AUTHORS:        Jaeyoung Huh ; Shujaat Khan ; Jong Chul Ye
HIGHLIGHT:      Inspired by the recent success of multi-domain image transfer, here we propose a novel, unsupervised, deep learning approach in which a single neural network can be used to deal with different types of US artifacts simply by changing a mask vector that switches between different target domains.

52, TITLE:      Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
http://arxiv.org/abs/2007.05448
AUTHORS:        Hui Qu ; Pengxiang Wu ; Qiaoying Huang ; Jingru Yi ; Zhennan Yan ; Kang Li ; Gregory M. Riedlinger ; Subhajyoti De ; Shaoting Zhang ; Dimitris N. Metaxas
COMMENTS:       12 pages
HIGHLIGHT:      To alleviate such tedious and manual effort, in this paper we propose a novel weakly supervised segmentation framework based on partial points annotation, i.e., only a small portion of nuclei locations in each image are labeled.

53, TITLE:      A Benchmark for Inpainting of Clothing Images with Irregular Holes
http://arxiv.org/abs/2007.05080
AUTHORS:        Furkan Kınlı ; Barış Özcan ; Furkan Kıraç
COMMENTS:       15 pages, 7 figures
HIGHLIGHT:      For that matter, we present an extensive benchmark of clothing image inpainting on well-known fashion datasets.

54, TITLE:      Distillation Guided Residual Learning for Binary Convolutional Neural Networks
http://arxiv.org/abs/2007.05223
AUTHORS:        Jianming Ye ; Shiliang Zhang ; Jingdong Wang
HIGHLIGHT:      We observe that, this performance gap leads to substantial residuals between intermediate feature maps of BCNN and FCNN.

55, TITLE:      Automatic Segmentation of Non-Tumor Tissues in Glioma MR Brain Images Using Deformable Registration with Partial Convolutional Networks
http://arxiv.org/abs/2007.05224
AUTHORS:        Zhongqiang Liu
HIGHLIGHT:      In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates missed normal tissues within the tumor region using a partial convolutional network.

56, TITLE:      VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
http://arxiv.org/abs/2007.05397
AUTHORS:        Adithya Ranga ; Filippo Giruzzi ; Jagdish Bhanushali ; Emilie Wirbel ; Patrick Pérez ; Tuan-Hung Vu ; Xavier Perrotton
COMMENTS:       This paper is reprinted from, "VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users, IS&T Electronic Imaging: Autonomous Vehicles and Machines 2020 Proceedings, (IS&T, Springfield, VA, 2020) page 109-1-10. DOI: 10.2352/ISSN.2470-1173.2020.16.AVM-109." Reprinted with permission of The Society for Imaging Science and Technology, holders of the 2020 copyright
HIGHLIGHT:      In this paper we propose a multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences.

57, TITLE:      Multimodal price prediction
http://arxiv.org/abs/2007.05056
AUTHORS:        Aidin Zehtab-Salmasi ; Ali-Reza Feizi-Derakhshi ; Narjes Nikzad-Khasmakhi ; Meysam Asgari-Chenaghlu ; Saeideh Nabipour
HIGHLIGHT:      The goal of this research is to achieve an arrangement to predict the price of a product based on specifications of that.

58, TITLE:      Prediction of Traffic Flow via Connected Vehicles
http://arxiv.org/abs/2007.05460
AUTHORS:        Ranwa Al Mallah ; Bilal Farooq ; Alejandro Quintero
HIGHLIGHT:      We propose a Short-term Traffic flow Prediction (STP) framework so that transportation authorities take early actions to control flow and prevent congestion.

59, TITLE:      A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning
http://arxiv.org/abs/2007.05156
AUTHORS:        Xuan Di ; Rongye Shi
HIGHLIGHT:      This paper serves as an introduction and overview of the potentially useful models and methodologies from artificial intelligence (AI) into the field of transportation engineering for autonomous vehicle (AV) control in the era of mixed autonomy.

60, TITLE:      Efficient Unpaired Image Dehazing with Cyclic Perceptual-Depth Supervision
http://arxiv.org/abs/2007.05220
AUTHORS:        Chen Liu ; Jiaqi Fan ; Guosheng Yin
HIGHLIGHT:      Hence, we propose to anneal the depth border degradation in unpaired image dehazing with cyclic perceptual-depth supervision.

61, TITLE:      Data-Efficient Ranking Distillation for Image Retrieval
http://arxiv.org/abs/2007.05299
AUTHORS:        Zakaria Laskar ; Juho Kannala
COMMENTS:       10 pages, 2 figures
HIGHLIGHT:      In this paper we address knowledge distillation for metric learning problems.

62, TITLE:      Learning Representations that Support Extrapolation
http://arxiv.org/abs/2007.05059
AUTHORS:        Taylor W. Webb ; Zachary Dulberg ; Steven M. Frankland ; Alexander A. Petrov ; Randall C. O'Reilly ; Jonathan D. Cohen
COMMENTS:       ICML 2020
HIGHLIGHT:      In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data.

63, TITLE:      Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images
http://arxiv.org/abs/2007.05295
AUTHORS:        Julia M. H. Noothout ; Bob D. de Vos ; Jelmer M. Wolterink ; Elbrich M. Postma ; Paul A. M. Smeets ; Richard A. P. Takx ; Tim Leiner ; Max A. Viergever ; Ivana Išgum
COMMENTS:       12 pages, accepted at IEEE transactions in Medical Imaging
HIGHLIGHT:      In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images.

64, TITLE:      Learn to Use Future Information in Simultaneous Translation
http://arxiv.org/abs/2007.05290
AUTHORS:        Xueqing Wu ; Yingce Xia ; Lijun Wu ; Shufang Xie ; Weiqing Liu ; Jiang Bian ; Tao Qin ; Tie-Yan Liu
HIGHLIGHT:      Based on this observation, we propose a framework that automatically learns how much future information to use in training for simultaneous NMT.

65, TITLE:      Border rank non-additivity for higher order tensors
http://arxiv.org/abs/2007.05458
AUTHORS:        Matthias Christandl ; Fulvio Gesmundo ; Mateusz Michałek ; Jeroen Zuiddam
COMMENTS:       26 pages, 5 figures, Comments are welcome
HIGHLIGHT:      In this work, we settle this problem by providing analogs of Sch\"onhage's construction for tensors of order four and higher.

66, TITLE:      Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
http://arxiv.org/abs/2007.05292
AUTHORS:        Yushan Liu ; Marcel Hildebrandt ; Mitchell Joblin ; Martin Ringsquandl ; Volker Tresp
COMMENTS:       Accepted at the ICML 2020 Workshop Graph Representation Learning and Beyond (GRL+)
HIGHLIGHT:      We propose a novel method that combines these rules with a neural multi-hop reasoning approach that uses reinforcement learning.

67, TITLE:      Program Synthesis with Pragmatic Communication
http://arxiv.org/abs/2007.05060
AUTHORS:        Yewen Pu ; Kevin Ellis ; Marta Kryven ; Josh Tenenbaum ; Armando Solar-Lezama
COMMENTS:       The second author and the third author contributed equally to this work
HIGHLIGHT:      This work introduces a new inductive bias derived by modeling the program synthesis task as rational communication, drawing insights from recursive reasoning models of pragmatics.

68, TITLE:      Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging
http://arxiv.org/abs/2007.05428
AUTHORS:        Duong-Hung Pham ; Adrian Basarab ; Ilyess Zemmoura ; Jean-Pierre Remenieras ; Denis Kouame
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      To overcome this limitation, we propose herein a blind deconvolution method able to estimate both the blood component and the PSF from Doppler data.

69, TITLE:      Half-checking propagators
http://arxiv.org/abs/2007.05423
AUTHORS:        Mikael Zayenz Lagerkvist ; Magnus Rattfeldt
HIGHLIGHT:      A formal model for half-checking propagators is introduced, together with a detailed description of how to support such propagators in a constraint programming system.

70, TITLE:      Impression Space from Deep Template Network
http://arxiv.org/abs/2007.05441
AUTHORS:        Gongfan Fang ; Xinchao Wang ; Haofei Zhang ; Jie Song ; Mingli Song
HIGHLIGHT:      To achieve this, we propose a simple but powerful framework to establish an {\emph{Impression Space}} upon an off-the-shelf pretrained network.

71, TITLE:      Algorithmic Fairness in Education
http://arxiv.org/abs/2007.05443
AUTHORS:        René F. Kizilcec ; Hansol Lee
COMMENTS:       Forthcoming in W. Holmes & K. Porayska-Pomsta (Eds.), Ethics in Artificial Intelligence in Education, Taylor & Francis
HIGHLIGHT:      Data-driven predictive models are increasingly used in education to support students, instructors, and administrators.

72, TITLE:      ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model
http://arxiv.org/abs/2007.05201
AUTHORS:        Yuhui Ma ; Huaying Hao ; Huazhu Fu ; Jiong Zhang ; Jianlong Yang ; Jiang Liu ; Yalin Zheng ; Yitian Zhao
COMMENTS:       10 pages, 9 figures
HIGHLIGHT:      To address these issues, for the first time in the field of retinal image analysis we construct a dedicated Retinal OCT-A SEgmentation dataset (ROSE), which consists of 229 OCT-A images with vessel annotations at either centerline-level or pixel level.

73, TITLE:      Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models
http://arxiv.org/abs/2007.05033
AUTHORS:        Adarsh K. Jeewajee ; Leslie P. Kaelbling
COMMENTS:       11 pages, 4 figures, 2 tables. Submitted to NeurIPS 2020
HIGHLIGHT:      Instead, we propose an inference-agnostic adversarial training framework for producing an ensemble of graphical models (AGMs).

74, TITLE:      Learning to plan with uncertain topological maps
http://arxiv.org/abs/2007.05270
AUTHORS:        Edward Beeching ; Jilles Dibangoye ; Olivier Simonin ; Christian Wolf
COMMENTS:       ECCV 2020
HIGHLIGHT:      Our main contribution is a data driven learning based approach for planning under uncertainty in topological maps, requiring an estimate of shortest paths in valued graphs with a probabilistic structure.

75, TITLE:      Learning to Play Sequential Games versus Unknown Opponents
http://arxiv.org/abs/2007.05271
AUTHORS:        Pier Giuseppe Sessa ; Ilija Bogunovic ; Maryam Kamgarpour ; Andreas Krause
HIGHLIGHT:      We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents.

76, TITLE:      Multi-Agent Routing Value Iteration Network
http://arxiv.org/abs/2007.05096
AUTHORS:        Quinlan Sykora ; Mengye Ren ; Raquel Urtasun
COMMENTS:       Published at ICML 2020
HIGHLIGHT:      In this paper we tackle the problem of routing multiple agents in a coordinated manner.

77, TITLE:      DCANet: Learning Connected Attentions for Convolutional Neural Networks
http://arxiv.org/abs/2007.05099
AUTHORS:        Xu Ma ; Jingda Guo ; Sihai Tang ; Zhinan Qiao ; Qi Chen ; Qing Yang ; Song Fu
HIGHLIGHT:      In this paper, we present Deep Connected Attention Network (DCANet), a novel design that boosts attention modules in a CNN model without any modification of the internal structure.

78, TITLE:      Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions
http://arxiv.org/abs/2007.05028
AUTHORS:        Li Wang ; Ren-Cang Li ;  Wen-Wei
HIGHLIGHT:      Building on the least squares reformulation of OPLS, we propose a unified multi-view learning framework to learn a classifier over a common latent space shared by all views.

79, TITLE:      ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians
http://arxiv.org/abs/2007.05094
AUTHORS:        Deshana Desai ; Etai Shuchatowitz ; Zhongshi Jiang ; Teseo Schneider ; Daniele Panozzo
HIGHLIGHT:      We propose an algorithm to automatically differentiate algorithms written in a subset of C99 code and its efficient implementation as a Python script.

80, TITLE:      Reverse AD at Higher Types: Pure, Principled and Denotationally Correct
http://arxiv.org/abs/2007.05283
AUTHORS:        Matthijs Vákár
HIGHLIGHT:      We show how to define source-code transformations for forward- and reverse-mode Automatic Differentiation on a standard higher-order functional language.

81, TITLE:      Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation
http://arxiv.org/abs/2007.05284
AUTHORS:        Guilherme Paulino-Passos ; Francesca Toni
HIGHLIGHT:      In this paper, we focus on analysing the non-monotonicity properties of a regular version of $AA{\text -}CBR$ (that we call $AA{\text -}CBR_{\succeq}$).

82, TITLE:      Advances of Transformer-Based Models for News Headline Generation
http://arxiv.org/abs/2007.05044
AUTHORS:        Alexey Bukhtiyarov ; Ilya Gusev
COMMENTS:       Submitted to AINL 2020
HIGHLIGHT:      In this paper, we fine-tune two pretrained Transformer-based models (mBART and BertSumAbs) for that task and achieve new state-of-the-art results on the RIA and Lenta datasets of Russian news.

83, TITLE:      Automatic Detection of Major Freeway Congestion Events Using Wireless Traffic Sensor Data: A Machine Learning Approach
http://arxiv.org/abs/2007.05079
AUTHORS:        Sanaz Aliari ; Kaveh F. Sadabadi
COMMENTS:       12 pages, 3 figures
HIGHLIGHT:      Monitoring the dynamics of traffic in major corridors can provide invaluable insight for traffic planning purposes.

84, TITLE:      Using Machine Learning to Detect Ghost Images in Automotive Radar
http://arxiv.org/abs/2007.05280
AUTHORS:        Florian Kraus ; Nicolas Scheiner ; Werner Ritter ; Klaus Dietmayer
HIGHLIGHT:      In this article, we present a novel approach to detect these ghost objects by applying data-driven machine learning algorithms.

85, TITLE:      Denotational Correctness of Foward-Mode Automatic Differentiation for Iteration and Recursion
http://arxiv.org/abs/2007.05282
AUTHORS:        Matthijs Vákár
HIGHLIGHT:      We present semantic correctness proofs of forward-mode Automatic Differentiation (AD) for languages with sources of partiality such as partial operations, lazy conditionals on real parameters, iteration, and term and type recursion.

86, TITLE:      Intelligent Warehouse Allocator for Optimal Regional Utilization
http://arxiv.org/abs/2007.05081
AUTHORS:        Girish Sathyanarayana ; Arun Patro
COMMENTS:       7 pages, 1 figures
HIGHLIGHT:      In this paper, we describe a novel solution to compute optimal warehouse allocations for fashion inventory.



==========Updates to Previous Papers==========
1, TITLE:       Interpretation of Deep Temporal Representations by Selective Visualization of Internally Activated Nodes
http://arxiv.org/abs/2004.12538
AUTHORS:        Sohee Cho ; Ginkyeng Lee ; Wonjoon Chang ; Jaesik Choi
HIGHLIGHT:      In this paper, we propose two new frameworks to visualize temporal representations learned from deep neural networks.

2, TITLE:       JGR-P2O: Joint Graph Reasoning based Pixel-to-Offset Prediction Network for 3D Hand Pose Estimation from a Single Depth Image
http://arxiv.org/abs/2007.04646
AUTHORS:        Linpu Fang ; Xingyan Liu ; Li Liu ; Hang Xu ; Wenxiong Kang
COMMENTS:       Accepted by ECCV2020 as a Spotlight paper
HIGHLIGHT:      In this paper, a novel pixel-wise prediction-based method is proposed to address the above issues.

3, TITLE:       Computing Maximum Matchings in Temporal Graphs
http://arxiv.org/abs/1905.05304
AUTHORS:        George B. Mertzios ; Hendrik Molter ; Rolf Niedermeier ; Viktor Zamaraev ; Philipp Zschoche
HIGHLIGHT:      We introduce and study the complexity of a natural temporal extension of the classical graph problem Maximum Matching, taking into account the dynamic nature of temporal graphs.

4, TITLE:       SaADB: A Self-attention Guided ADB Network for Person Re-identification
http://arxiv.org/abs/2007.03584
AUTHORS:        Bo Jiang ; Sheng Wang ; Xiao Wang ; Aihua Zheng
COMMENTS:       Under Review
HIGHLIGHT:      In this paper, we propose a novel Self-attention guided Adaptive DropBlock network (SaADB) for person re-ID which can adaptively erase the most discriminative regions.

5, TITLE:       Multi-Granularity Modularized Network for Abstract Visual Reasoning
http://arxiv.org/abs/2007.04670
AUTHORS:        Xiangru Tang ; Haoyuan Wang ; Xiang Pan ; Jiyang Qi
HIGHLIGHT:      Inspired by cognitive studies, we propose a Multi-Granularity Modularized Network (MMoN) to bridge the gap between the processing of raw sensory information and symbolic reasoning.

6, TITLE:       FKAConv: Feature-Kernel Alignment for Point Cloud Convolution
http://arxiv.org/abs/2004.04462
AUTHORS:        Alexandre Boulch ; Gilles Puy ; Renaud Marlet
HIGHLIGHT:      In this paper, inspired by discrete convolution in image processing, we provide a formulation to relate and analyze a number of point convolution methods.

7, TITLE:       Translationese as a Language in "Multilingual" NMT
http://arxiv.org/abs/1911.03823
AUTHORS:        Parker Riley ; Isaac Caswell ; Markus Freitag ; David Grangier
HIGHLIGHT:      Motivated by this, we model translationese and original (i.e. natural) text as separate languages in a multilingual model, and pose the question: can we perform zero-shot translation between original source text and original target text?

8, TITLE:       SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
http://arxiv.org/abs/2004.03696
AUTHORS:        Changlu Guo ; Márton Szemenyei ; Yugen Yi ; Wenle Wang ; Buer Chen ; Changqi Fan
COMMENTS:       Submitted to IEEE ICPR 2020
HIGHLIGHT:      In this work, we propose a lightweight network named Spatial Attention U-Net (SA-UNet) that does not require thousands of annotated training samples and can be utilized in a data augmentation manner to use the available annotated samples more efficiently.

9, TITLE:       A general approach to progressive learning
http://arxiv.org/abs/2004.12908
AUTHORS:        Joshua T. Vogelstein ; Hayden S. Helm ; Ronak D. Mehta ; Jayanta Dey ; Will LeVine ; Weiwei Yang ; Bryan Tower ; Jonathan Larson ; Chris White ; Carey E. Priebe
HIGHLIGHT:      We propose representation ensembling, as opposed to learner ensembling (e.g., bagging), to address progressive learning.

10, TITLE:      AvE: Assistance via Empowerment
http://arxiv.org/abs/2006.14796
AUTHORS:        Yuqing Du ; Stas Tiomkin ; Emre Kiciman ; Daniel Polani ; Pieter Abbeel ; Anca Dragan
COMMENTS:       Fix missing citation on page 4; edit acknowledgements
HIGHLIGHT:      We propose a new paradigm for assistance by instead increasing the human's ability to control their environment, and formalize this approach by augmenting reinforcement learning with human empowerment.

11, TITLE:      The growing amplification of social media: Measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009--2020
http://arxiv.org/abs/2003.03667
AUTHORS:        Thayer Alshaabi ; David R. Dewhurst ; Joshua R. Minot ; Michael V. Arnold ; Jane L. Adams ; Christopher M. Danforth ; Peter Sheridan Dodds
COMMENTS:       25 pages (15 main, 10 appendix), 15 figures (7 main, 8 appendix), 4 online appendices available http://compstorylab.org/share/papers/alshaabi2020a , our source code along with our documentation is publicly available online on a Gitlab repository https://gitlab.com/compstorylab/storywrangler
HIGHLIGHT:      Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter.

12, TITLE:      Localized convolutional neural networks for geospatial wind forecasting
http://arxiv.org/abs/2005.05930
AUTHORS:        Arnas Uselis ; Mantas Lukoševičius ; Lukas Stasytis
HIGHLIGHT:      In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets.

13, TITLE:      Density Matrices with Metric for Derivational Ambiguity
http://arxiv.org/abs/1908.07347
AUTHORS:        Adriana D. Correia ; Michael Moortgat ; Henk T. C. Stoof
COMMENTS:       24 pages, 10 figures. SemSpace 2019, to appear in J. of Applied Logics
HIGHLIGHT:      Our aims in this paper are threefold.

14, TITLE:      Uncertainty Estimation in One-Stage Object Detection
http://arxiv.org/abs/1905.10296
AUTHORS:        Florian Kraus ; Klaus Dietmayer
HIGHLIGHT:      We present a viable approaches to estimate uncertainty in an one-stage object detector, while improving the detection performance of the baseline approach.

15, TITLE:      Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes
http://arxiv.org/abs/2002.00860
AUTHORS:        Christoph Stöckl ; Wolfgang Maass
COMMENTS:       15 pages, 6 figures, 2 tables
HIGHLIGHT:      We show that a substantially more efficient conversion from artificial neural networks to spike-based networks is possible if one optimizes the spiking neuron model for that purpose, and enables it to use the timing of spikes to encode information.

16, TITLE:      Channel Attention Residual U-Net for Retinal Vessel Segmentation
http://arxiv.org/abs/2004.03702
AUTHORS:        Changlu Guo ; Márton Szemenyei ; Yugen Yi ; Wei Zhou
COMMENTS:       Submitted to IEEE ICTAI 2020
HIGHLIGHT:      In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-U-Net), to accurately segment retinal vascular and non-vascular pixels.

17, TITLE:      Approximation spaces of deep neural networks
http://arxiv.org/abs/1905.01208
AUTHORS:        Rémi Gribonval ; Gitta Kutyniok ; Morten Nielsen ; Felix Voigtlaender
HIGHLIGHT:      We study the expressivity of deep neural networks.

18, TITLE:      Interpreting video features: a comparison of 3D convolutional networks and convolutional LSTM networks
http://arxiv.org/abs/2002.00367
AUTHORS:        Joonatan Mänttäri ; Sofia Broomé ; John Folkesson ; Hedvig Kjellström
HIGHLIGHT:      In this paper, we present a study comparing how 3D convolutional networks and convolutional LSTM networks learn features across temporally dependent frames.

19, TITLE:      Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
http://arxiv.org/abs/2003.05856
AUTHORS:        Massimo Caccia ; Pau Rodriguez ; Oleksiy Ostapenko ; Fabrice Normandin ; Min Lin ; Lucas Caccia ; Issam Laradji ; Irina Rish ; Alexandre Lacoste ; David Vazquez ; Laurent Charlin
HIGHLIGHT:      We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario.

20, TITLE:      Randomized Smoothing of All Shapes and Sizes
http://arxiv.org/abs/2002.08118
AUTHORS:        Greg Yang ; Tony Duan ; J. Edward Hu ; Hadi Salman ; Ilya Razenshteyn ; Jerry Li
COMMENTS:       9 pages main text, 49 pages total
HIGHLIGHT:      We propose a novel framework for devising and analyzing randomized smoothing schemes, and validate its effectiveness in practice.

21, TITLE:      Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France
http://arxiv.org/abs/2006.06251
AUTHORS:        Paul Boniol ; George Panagopoulos ; Christos Xypolopoulos ; Rajaa El Hamdani ; David Restrepo Amariles ; Michalis Vazirgiannis
HIGHLIGHT:      We propose metrics to rank lawyers based on their experience, wins/loss ratio and their importance in the network of lawyers.

22, TITLE:      Frustratingly Simple Domain Generalization via Image Stylization
http://arxiv.org/abs/2006.11207
AUTHORS:        Nathan Somavarapu ; Chih-Yao Ma ; Zsolt Kira
COMMENTS:       Code: https://github.com/GT-RIPL/DomainGeneralization-Stylization
HIGHLIGHT:      In this work, we address the Domain Generalization problem, where the classifier must generalize to an unknown target domain.

23, TITLE:      Asynchronous effects
http://arxiv.org/abs/2003.02110
AUTHORS:        Danel Ahman ; Matija Pretnar
COMMENTS:       POPL 2021 submission
HIGHLIGHT:      We explore asynchronous programming with algebraic effects.

24, TITLE:      AI Assisted Apparel Design
http://arxiv.org/abs/2007.04950
AUTHORS:        Alpana Dubey ; Nitish Bhardwaj ; Kumar Abhinav ; Suma Mani Kuriakose ; Sakshi Jain ; Veenu Arora
HIGHLIGHT:      In this paper, we propose a system of AI assistants that assists designers in their design journey.

25, TITLE:      Summarizing and Exploring Tabular Data in Conversational Search
http://arxiv.org/abs/2005.11490
AUTHORS:        Shuo Zhang ; Zhuyun Dai ; Krisztian Balog ; Jamie Callan
COMMENTS:       Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), 2020
HIGHLIGHT:      We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset.

26, TITLE:      High-Fidelity Generative Image Compression
http://arxiv.org/abs/2006.09965
AUTHORS:        Fabian Mentzer ; George Toderici ; Michael Tschannen ; Eirikur Agustsson
COMMENTS:       Project page: https://hific.github.io
HIGHLIGHT:      We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system.

27, TITLE:      Dynamic Group Convolution for Accelerating Convolutional Neural Networks
http://arxiv.org/abs/2007.04242
AUTHORS:        Zhuo Su ; Linpu Fang ; Wenxiong Kang ; Dewen Hu ; Matti Pietikäinen ; Li Liu
COMMENTS:       21 pages, 10 figures
HIGHLIGHT:      In this paper, we propose dynamic group convolution (DGC) that adaptively selects which part of input channels to be connected within each group for individual samples on the fly.

28, TITLE:      FathomNet: An underwater image training database for ocean exploration and discovery
http://arxiv.org/abs/2007.00114
AUTHORS:        Océane Boulais ; Ben Woodward ; Brian Schlining ; Lonny Lundsten ; Kevin Barnard ; Katy Croff Bell ; Kakani Katija
COMMENTS:       8 pages, 6 figures
HIGHLIGHT:      FathomNet: An underwater image training database for ocean exploration and discovery

29, TITLE:      EVO-RL: Evolutionary-Driven Reinforcement Learning
http://arxiv.org/abs/2007.04725
AUTHORS:        Ahmed Hallawa ; Thorsten Born ; Anke Schmeink ; Guido Dartmann ; Arne Peine ; Lukas Martin ; Giovanni Iacca ; A. E. Eiben ; Gerd Ascheid
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      In this work, we propose a novel approach for reinforcement learning driven by evolutionary computation.

30, TITLE:      Learning Distributional Programs for Relational Autocompletion
http://arxiv.org/abs/2001.08603
AUTHORS:        Kumar Nitesh ; Kuzelka Ondrej ; De Raedt Luc
HIGHLIGHT:      Within this framework, we introduce DiceML -- an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data).

31, TITLE:      Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes
http://arxiv.org/abs/2003.06877
AUTHORS:        Liang Liao ; Jing Xiao ; Zheng Wang ; Chia-Wen Lin ; Shin'ichi Satoh
HIGHLIGHT:      In this paper, we propose a Semantic Guidance and Evaluation Network (SGE-Net) to iteratively update the structural priors and the inpainted image in an interplay framework of semantics extraction and image inpainting.

32, TITLE:      Point Proposal Network for Reconstructing 3D Particle Endpoints with Sub-Pixel Precision in Liquid Argon Time Projection Chambers
http://arxiv.org/abs/2006.14745
AUTHORS:        Laura Dominé ; Pierre Côte de Soux ; François Drielsma ; Dae Heun Koh ; Ran Itay ; Qing Lin ; Kazuhiro Terao ; Ka Vang Tsang ; Tracy L. Usher
HIGHLIGHT:      Using as a benchmark the PILArNet public LArTPC data sample in which the voxel resolution is 3mm/voxel, our algorithm successfully predicted 96.8% and 97.8% of 3D points within a distance of 3 and 10~voxels from the provided true point locations respectively.

33, TITLE:      On the Complexity and Approximability of Optimal Sensor Selection and Attack for Kalman Filtering
http://arxiv.org/abs/2003.11951
AUTHORS:        Lintao Ye ; Nathaniel Woodford ; Sandip Roy ; Shreyas Sundaram
COMMENTS:       arXiv admin note: text overlap with arXiv:1711.01920
HIGHLIGHT:      Given a linear dynamical system affected by stochastic noise, we consider the problem of selecting an optimal set of sensors (at design-time) to minimize the trace of the steady state a priori or a posteriori error covariance of the Kalman filter, subject to certain selection budget constraints.

34, TITLE:      Real-time Semantic Segmentation with Fast Attention
http://arxiv.org/abs/2007.03815
AUTHORS:        Ping Hu ; Federico Perazzi ; Fabian Caba Heilbron ; Oliver Wang ; Zhe Lin ; Kate Saenko ; Stan Sclaroff
COMMENTS:       project page: https://cs-people.bu.edu/pinghu/FANet.html
HIGHLIGHT:      In this paper, we propose a novel architecture that addresses both challenges and achieves state-of-the-art performance for semantic segmentation of high-resolution images and videos in real-time.

35, TITLE:      Nested Named Entity Recognition via Second-best Sequence Learning and Decoding
http://arxiv.org/abs/1909.02250
AUTHORS:        Takashi Shibuya ; Eduard Hovy
COMMENTS:       Accepted to TACL
HIGHLIGHT:      We propose a new method to recognize not only outermost named entities but also inner nested ones.

36, TITLE:      Implementations and the independent set polynomial below the Shearer threshold
http://arxiv.org/abs/1612.05832
AUTHORS:        Andreas Galanis ; Leslie Ann Goldberg ; Daniel Stefankovic
COMMENTS:       Updated to clarify the contribution, in particular that it is used by arXiv:1711.00282 (and is not superseded by it)
HIGHLIGHT:      Informally, an implementation of a real number $\lambda'$ is a graph whose hard-core partition function, evaluated at~$\lambda$, simulates a vertex-weight of~$\lambda'$ in the sense that $\lambda'$ is the ratio between the contribution to the partition function from independent sets containing a certain vertex and the contribution from independent sets that do not contain that vertex.

37, TITLE:      Edge Preserving CNN SAR Despeckling Algorithm
http://arxiv.org/abs/2001.04716
AUTHORS:        Sergio Vitale ; Giampaolo Ferraioli ; Vito Pascazio
COMMENTS:       Accepted to LAGIRS 2020
HIGHLIGHT:      Based on the results of our previous solution KL-DNN, in this work we define a new cost function for training a convolutional neural network for despeckling.

38, TITLE:      Estimating People Flows to Better Count Them in Crowded Scenes
http://arxiv.org/abs/1911.10782
AUTHORS:        Weizhe Liu ; Mathieu Salzmann ; Pascal Fua
COMMENTS:       ECCV 2020
HIGHLIGHT:      In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing.

39, TITLE:      Global Distance-distributions Separation for Unsupervised Person Re-identification
http://arxiv.org/abs/2006.00752
AUTHORS:        Xin Jin ; Cuiling Lan ; Wenjun Zeng ; Zhibo Chen
COMMENTS:       Accepted by ECCV2020
HIGHLIGHT:      To address this problem, we introduce a global distance-distributions separation (GDS) constraint over the two distributions to encourage the clear separation of positive and negative samples from a global view.

40, TITLE:      Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
http://arxiv.org/abs/2004.01019
AUTHORS:        Philipp Terhörst ; Jan Niklas Kolf ; Naser Damer ; Florian Kirchbuchner ; Arjan Kuijper
COMMENTS:       Accepted at IJCB2020
HIGHLIGHT:      In this work, we present an in-depth analysis of the correlation between bias in face recognition and face quality assessment.

41, TITLE:      Polarimetric image augmentation
http://arxiv.org/abs/2005.11044
AUTHORS:        Marc Blanchon ; Olivier Morel ; Fabrice Meriaudeau ; Ralph Seulin ; Désiré Sidibé
COMMENTS:       7 pages, submitted to ICPR2020 second round
HIGHLIGHT:      We propose to enhance deep learning models through a regularized augmentation procedure applied to polarimetric data in order to characterize scenes more effectively under challenging conditions.

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

43, TITLE:      Resonator Networks outperform optimization methods at solving high-dimensional vector factorization
http://arxiv.org/abs/1906.11684
AUTHORS:        Spencer J. Kent ; E. Paxon Frady ; Friedrich T. Sommer ; Bruno A. Olshausen
COMMENTS:       Updated 6/9/2020 to reflect revisions made during review process with Neural Computation. To appear. Two-part series on Resonator Networks starts with arXiv:2007.03748, followed by this paper
HIGHLIGHT:      We develop theoretical foundations of Resonator Networks, a new type of recurrent neural network introduced in Frady et al. (2020) to solve a high-dimensional vector factorization problem arising in Vector Symbolic Architectures.

44, TITLE:      MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
http://arxiv.org/abs/2004.14525
AUTHORS:        Yunyang Xiong ; Hanxiao Liu ; Suyog Gupta ; Berkin Akin ; Gabriel Bender ; Pieter-Jan Kindermans ; Mingxing Tan ; Vikas Singh ; Bo Chen
COMMENTS:       Code and models are available in the TensorFlow Object Detection API: https://github.com/tensorflow/models/tree/master/research/object_detection
HIGHLIGHT:      In this work, we question the optimality of this design pattern over a broad range of mobile accelerators by revisiting the usefulness of regular convolutions.

45, TITLE:      Deep Reinforcement Learning with Smooth Policy
http://arxiv.org/abs/2003.09534
AUTHORS:        Qianli Shen ; Yan Li ; Haoming Jiang ; Zhaoran Wang ; Tuo Zhao
COMMENTS:       ICML 2020
HIGHLIGHT:      Motivated by the fact that many environments with continuous state space have smooth transitions, we propose to learn a smooth policy that behaves smoothly with respect to states.

46, TITLE:      VisImages: A Large-scale, High-quality Image Corpus in Visualization Publications
http://arxiv.org/abs/2007.04584
AUTHORS:        Dazhen Deng ; Yihong Wu ; Xinhuan Shu ; Mengye Xu ; Jiang Wu ; Siwei Fu ; Yingcai Wu
HIGHLIGHT:      This study presents VisImages, a high-quality and large-scale image corpus collected from visualization publications.

47, TITLE:      Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks
http://arxiv.org/abs/1906.11479
AUTHORS:        Hongruixuan Chen ; Chen Wu ; Bo Du ; Liangpei Zhang
HIGHLIGHT:      In this paper, a powerful feature extraction model entitled multi-scale feature convolution unit (MFCU) is adopted for change detection in multi-temporal VHR images.

48, TITLE:      U-Bubble Model for Mixed Unit Interval Graphs and its Applications: The MaxCut Problem Revisited
http://arxiv.org/abs/2002.08311
AUTHORS:        Jan Kratochvíl ; Tomáš Masařík ; Jana Novotná
COMMENTS:       Accepted to Mathematical Foundations of Computer Science (MFCS 2020), 25 pages, 4 figures
HIGHLIGHT:      Interval graphs, intersection graphs of segments on a real line (intervals), play a key role in the study of algorithms and special structural properties.

49, TITLE:      Options as responses: Grounding behavioural hierarchies in multi-agent RL
http://arxiv.org/abs/1906.01470
AUTHORS:        Alexander Sasha Vezhnevets ; Yuhuai Wu ; Remi Leblond ; Joel Z. Leibo
COMMENTS:       First two authors contributed equally
HIGHLIGHT:      We propose two new games with concealed information and complex, non-transitive reward structure (think rock/paper/scissors).