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2020.07.06.txt
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2020.07.06.txt
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==========New Papers==========
1, TITLE: Deep learning for scene recognition from visual data: a survey
http://arxiv.org/abs/2007.01806
AUTHORS: Alina Matei ; Andreea Glavan ; Estefania Talavera
HIGHLIGHT: Later, we describe ensemble techniques introduced by research papers in the field.
2, TITLE: Exploration and Discovery of the COVID-19 Literature through Semantic Visualization
http://arxiv.org/abs/2007.01800
AUTHORS: Jingxuan Tu ; Marc Verhagen ; Brent Cochran ; James Pustejovsky
HIGHLIGHT: We are developing semantic visualization techniques in order to enhance exploration and enable discovery over large datasets of complex networks of relations.
3, TITLE: AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot
http://arxiv.org/abs/2007.01813
AUTHORS: Tong Qin ; Tongqing Chen ; Yilun Chen ; Qing Su
COMMENTS: The IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
HIGHLIGHT: In this paper, we exploit robust semantic features to build the map and localize vehicles in parking lots.
4, TITLE: Image-based Vehicle Re-identification Model with Adaptive Attention Modules and Metadata Re-ranking
http://arxiv.org/abs/2007.01818
AUTHORS: Quang Truong ; Hy Dang ; Zhankai Ye ; Minh Nguyen ; Bo Mei
HIGHLIGHT: In this paper, we propose a model powered by adaptive attention modules that requires fewer label annotations but still out-performs the previous models.
5, TITLE: Improving auto-encoder novelty detection using channel attention and entropy minimization
http://arxiv.org/abs/2007.01682
AUTHORS: Dongyan Guo ; Miao Tian ; Ying Cui ; Xiang Pan ; Shengyong Chen
HIGHLIGHT: Improving auto-encoder novelty detection using channel attention and entropy minimization
6, TITLE: On girth and the parameterized complexity of token sliding and token jumping
http://arxiv.org/abs/2007.01673
AUTHORS: Valentin Bartier ; Nicolas Bousquet ; Clément Dallard ; Kyle Lomer ; Amer E. Mouawad
HIGHLIGHT: We investigate the parameterized complexity of both problems on several graph classes, focusing on the effect of excluding certain cycles from the input graph.
7, TITLE: Learning intuitive physics and one-shot imitation using state-action-prediction self-organizing maps
http://arxiv.org/abs/2007.01647
AUTHORS: Martin Stetter ; Elmar W. Lang
COMMENTS: 22 pages, 4 figures
HIGHLIGHT: We suggest a simple but effective unsupervised model which develops such characteristics.
8, TITLE: Learn Faster and Forget Slower via Fast and Stable Task Adaptation
http://arxiv.org/abs/2007.01388
AUTHORS: Farshid Varno ; Lucas May Petry ; Lisa Di Jorio ; Stan Matwin
COMMENTS: 52 pages, 15 figures, 1 table
HIGHLIGHT: We investigate the source of this problem from different perspectives and to alleviate it, introduce Fast And Stable Task-adaptation (FAST), an easy to apply fine-tuning algorithm.
9, TITLE: Posterior Model Adaptation With Updated Priors
http://arxiv.org/abs/2007.01386
AUTHORS: Jim Davis
HIGHLIGHT: We prove that a unique (up to scale) solution is possible to recover the data likelihoods for a test example from its original class posteriors and dataset priors.
10, TITLE: Learning Orientation Distributions for Object Pose Estimation
http://arxiv.org/abs/2007.01418
AUTHORS: Brian Okorn ; Mengyun Xu ; Martial Hebert ; David Held
HIGHLIGHT: In this work, we propose two learned methods for estimating a distribution over an object's orientation.
11, TITLE: Persistent Neurons
http://arxiv.org/abs/2007.01419
AUTHORS: Yimeng Min
COMMENTS: 10 pages, 20 figures
HIGHLIGHT: Here, we propose persistent neurons, a strategy that optimizes the learning trajectory using information from previous converged solutions.
12, TITLE: Playing with Words at the National Library of Sweden -- Making a Swedish BERT
http://arxiv.org/abs/2007.01658
AUTHORS: Martin Malmsten ; Love Börjeson ; Chris Haffenden
HIGHLIGHT: This paper introduces the Swedish BERT ("KB-BERT") developed by the KBLab for data-driven research at the National Library of Sweden (KB).
13, TITLE: Generating Informative Dialogue Responses with Keywords-Guided Networks
http://arxiv.org/abs/2007.01652
AUTHORS: Heng-Da Xu ; Xian-Ling Mao ; Zewen Chi ; Jing-Jing Zhu ; Fanshu Sun ; Heyan Huang
HIGHLIGHT: In this paper, we propose a simple but effective keywords-guided Sequence-to-Sequence model (KW-Seq2Seq) which uses keywords information as guidance to generate open-domain dialogue responses.
14, TITLE: Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification
http://arxiv.org/abs/2007.01556
AUTHORS: Bin Wang ; Bing Xue ; Mengjie Zhang
HIGHLIGHT: A new effective and efficient surrogate-assisted particle swarm optimisation algorithm is proposed to automatically evolve convolutional neural networks.
15, TITLE: HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions
http://arxiv.org/abs/2007.01628
AUTHORS: Yuzhen Niu ; Jianbin Wu ; Wenxi Liu ; Wenzhong Guo ; Rynson W. H. Lau
HIGHLIGHT: To address these two problems, we propose in this paper a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images.
16, TITLE: Complex Network Construction for Interactive Image Segmentation using Particle Competition and Cooperation: A New Approach
http://arxiv.org/abs/2007.01625
AUTHORS: Jefferson Antonio Ribeiro Passerini ; Fabricio Aparecido Breve
COMMENTS: The 20th International Conference on Computational Science and its Applications (ICCSA2020)
HIGHLIGHT: The present paper proposes the elimination of the weight vector through modifications in the network construction phase.
17, TITLE: On-The-Fly Information Retrieval Augmentation for Language Models
http://arxiv.org/abs/2007.01528
AUTHORS: Hai Wang ; David McAllester
COMMENTS: ACL 2020 NUSE Workshop
HIGHLIGHT: By augmenting GPT 2.0 with information retrieval we achieve a zero shot 15% relative reduction in perplexity on Gigaword corpus without any re-training.
18, TITLE: End-to-end Interpretable Learning of Non-blind Image Deblurring
http://arxiv.org/abs/2007.01769
AUTHORS: Thomas Eboli ; Jian Sun ; Jean Ponce
COMMENTS: Accepted at ECCV2020 (poster)
HIGHLIGHT: We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels.
19, TITLE: Explainable Deep One-Class Classification
http://arxiv.org/abs/2007.01760
AUTHORS: Philipp Liznerski ; Lukas Ruff ; Robert A. Vandermeulen ; Billy Joe Franks ; Marius Kloft ; Klaus-Robert Müller
COMMENTS: 24 pages, preprint
HIGHLIGHT: In this paper we present an explainable deep one-class classification method, Fully Convolutional Data Description (FCDD), where the mapped samples are themselves also an explanation heatmap.
20, TITLE: Domain Adaptation without Source Data
http://arxiv.org/abs/2007.01524
AUTHORS: Youngeun Kim ; Sungeun Hong ; Donghyeon Cho ; Hyoungseob Park ; Priyadarshini Panda
COMMENTS: 13 pages
HIGHLIGHT: To avoid accessing source data which may contain sensitive information, we introduce source data-free domain adaptation (SFDA).
21, TITLE: Self-Supervised GAN Compression
http://arxiv.org/abs/2007.01491
AUTHORS: Chong Yu ; Jeff Pool
HIGHLIGHT: In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods.
22, TITLE: Visual Question Answering as a Multi-Task Problem
http://arxiv.org/abs/2007.01780
AUTHORS: Amelia Elizabeth Pollard ; Jonathan L. Shapiro
HIGHLIGHT: In this paper, we present the hypothesis that Visual Question Answering should be viewed as a multi-task problem, and provide evidence to support this hypothesis.
23, TITLE: On Symbolically Encoding the Behavior of Random Forests
http://arxiv.org/abs/2007.01493
AUTHORS: Arthur Choi ; Andy Shih ; Anchal Goyanka ; Adnan Darwiche
COMMENTS: Presented at the 3rd Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), 2020
HIGHLIGHT: We show some key distinctions with encodings for satisfiability, and propose an encoding that is sound and complete for the given task.
24, TITLE: The combined basic LP and affine IP relaxation for promise VCSPs on infinite domains
http://arxiv.org/abs/2007.01779
AUTHORS: Caterina Viola ; Stanislav Zivny
COMMENTS: Full version of an MFCS'20 paper
HIGHLIGHT: In this work, we extend an existing tractability result to the three generalisations of CSPs combined: We give a sufficient condition for the combined basic linear programming and affine integer programming relaxation for exact solvability of promise valued CSPs over infinite-domains.
25, TITLE: On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
http://arxiv.org/abs/2007.01488
AUTHORS: Jianing Li ; Yanyan Lan ; Jiafeng Guo ; Xueqi Cheng
COMMENTS: 16 pages, 7 figures. Accepted by ICML2020
HIGHLIGHT: In this paper, we try to reveal such relation in a theoretical approach.
26, TITLE: Learning to Prune in Training via Dynamic Channel Propagation
http://arxiv.org/abs/2007.01486
AUTHORS: Shibo Shen ; Rongpeng Li ; Zhifeng Zhao ; Honggang Zhang ; Yugeng Zhou
COMMENTS: accepted by ICPR-2020
HIGHLIGHT: In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the neural networks during the training period.
27, TITLE: Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation
http://arxiv.org/abs/2007.01771
AUTHORS: Bin-Bin Gao ; Xin-Xin Liu ; Hong-Yu Zhou ; Jianxin Wu ; Xin Geng
COMMENTS: submitted to Pattern Recognition
HIGHLIGHT: Our method achieves new state-of-the-art results using the single model with 36$\times$(6$\times$) fewer parameters and 2.6$\times$(2.1$\times$) faster inference speed on facial age (attractiveness) estimation.
28, TITLE: Interpretable Sequence Classification Via Prototype Trajectory
http://arxiv.org/abs/2007.01777
AUTHORS: Dat Hong ; Stephen S. Baek ; Tong Wang
HIGHLIGHT: We propose a novel interpretable recurrent neural network (RNN) model, called ProtoryNet, in which we introduce a new concept of prototype trajectories.
29, TITLE: A Similarity Inference Metric for RGB-Infrared Cross-Modality Person Re-identification
http://arxiv.org/abs/2007.01504
AUTHORS: Mengxi Jia ; Yunpeng Zhai ; Shijian Lu ; Siwei Ma ; Jian Zhang
COMMENTS: Accepted by IJCAI2020
HIGHLIGHT: This paper presents a novel similarity inference metric (SIM) that exploits the intra-modality sample similarities to circumvent the cross-modality discrepancy targeting optimal cross-modality image matching.
30, TITLE: Self-supervised Neural Architecture Search
http://arxiv.org/abs/2007.01500
AUTHORS: Sapir Kaplan ; Raja Giryes
HIGHLIGHT: In this work, we propose a self-supervised neural architecture search (SSNAS) that allows finding novel network models without the need for labeled data.
31, TITLE: Temporal-Logic-Based Reward Shaping for Continuing Learning Tasks
http://arxiv.org/abs/2007.01498
AUTHORS: Yuqian Jiang ; Sudarshanan Bharadwaj ; Bo Wu ; Rishi Shah ; Ufuk Topcu ; Peter Stone
HIGHLIGHT: In order to avoid the need for manual construction of the shaping function, we introduce a method for utilizing domain knowledge expressed as a temporal logic formula.
32, TITLE: A Competence-aware Curriculum for Visual Concepts Learning via Question Answering
http://arxiv.org/abs/2007.01499
AUTHORS: Qing Li ; Siyuan Huang ; Yining Hong ; Song-Chun Zhu
COMMENTS: ECCV 2020 Oral
HIGHLIGHT: To mimic this efficient learning ability, we propose a competence-aware curriculum for visual concept learning in a question-answering manner.
33, TITLE: Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations
http://arxiv.org/abs/2007.01496
AUTHORS: Shuo Lei ; Xuchao Zhang ; Jianfeng He ; Fanglan Chen ; Chang-Tien Lu
HIGHLIGHT: In this paper, we advance the few-shot segmentation paradigm towards a scenario where image-level annotations are available to help the training process of a few pixel-level annotations.
34, TITLE: Balanced Symmetric Cross Entropy for Large Scale Imbalanced and Noisy Data
http://arxiv.org/abs/2007.01618
AUTHORS: Feifei Huang ; Jie Li ; Xuelin Zhu
HIGHLIGHT: In this paper, we explore many kinds of deep convolution neural network architectures for large-scale product recognition task, which is heavily class-imbalanced and noisy labeled data, making it more challenged.
35, TITLE: Swoosh! Rattle! Thump! -- Actions that Sound
http://arxiv.org/abs/2007.01851
AUTHORS: Dhiraj Gandhi ; Abhinav Gupta ; Lerrel Pinto
COMMENTS: To be presented at Robotics: Science and Systems 2020
HIGHLIGHT: In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.
36, TITLE: Language-agnostic BERT Sentence Embedding
http://arxiv.org/abs/2007.01852
AUTHORS: Fangxiaoyu Feng ; Yinfei Yang ; Daniel Cer ; Naveen Arivazhagan ; Wei Wang
HIGHLIGHT: We adapt multilingual BERT to produce language-agnostic sentence embeddings for 109 languages.
37, TITLE: Logical Separability of Incomplete Data under Ontologies
http://arxiv.org/abs/2007.01610
AUTHORS: Jean Christoph Jung ; Carsten Lutz ; Hadrien Pulcini ; Frank Wolter
COMMENTS: Full Version of KR'20 paper
HIGHLIGHT: In this paper, we investigate the existence of a separating formula for incomplete data in the presence of an ontology.
38, TITLE: Task-agnostic Temporally Consistent Facial Video Editing
http://arxiv.org/abs/2007.01466
AUTHORS: Meng Cao ; Haozhi Huang ; Hao Wang ; Xuan Wang ; Li Shen ; Sheng Wang ; Linchao Bao ; Zhifeng Li ; Jiebo Luo
HIGHLIGHT: In this paper, we propose a task-agnostic temporally consistent facial video editing framework.
39, TITLE: Anatomy-Aware Siamese Network: Exploiting Semantic Asymmetry for Accurate Pelvic Fracture Detection in X-ray Images
http://arxiv.org/abs/2007.01464
AUTHORS: Haomin Chen ; Yirui Wang ; Kang Zheng ; Weijian Li ; Chi-Tung Cheng ; Adam P. Harrison ; Jing Xiao ; Gregory D. Hager ; Le Lu ; Chien-Hung Liao ; Shun Miao
COMMENTS: ECCV 2020
HIGHLIGHT: In this work, we exploit semantic anatomical symmetry or asymmetry analysis in a complex CAD scenario, i.e., anterior pelvic fracture detection in trauma PXRs, where semantically pathological (refer to as fracture) and non-pathological (e.g., pose) asymmetries both occur.
40, TITLE: RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning
http://arxiv.org/abs/2007.01480
AUTHORS: Chih-Hsing Ho ; Shang-Ho ; Tsai
HIGHLIGHT: In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning.
41, TITLE: Pretrained Semantic Speech Embeddings for End-to-End Spoken Language Understanding via Cross-Modal Teacher-Student Learning
http://arxiv.org/abs/2007.01836
AUTHORS: Pavel Denisov ; Ngoc Thang Vu
COMMENTS: Submitted to Interspeech 2020
HIGHLIGHT: In this paper, we propose a novel training method that enables pretrained contextual embeddings such as BERT to process acoustic features.
42, TITLE: LOOC: Localize Overlapping Objects with Count Supervision
http://arxiv.org/abs/2007.01837
AUTHORS: Issam H. Laradji ; Rafael Pardinas ; Pau Rodriguez ; David Vazquez
HIGHLIGHT: We propose LOOC, a method to Localize Overlapping Objects with Count supervision.
43, TITLE: Interactive Knowledge Distillation
http://arxiv.org/abs/2007.01476
AUTHORS: Shipeng Fu ; Zhen Li ; Jun Xu ; Ming-Ming Cheng ; Gwanggil Jeon ; Xiaomin Yang
HIGHLIGHT: In this work, we propose an InterActive Knowledge Distillation (IAKD) scheme to leverage the interactive teaching strategy for efficient knowledge distillation.
44, TITLE: ODE-CNN: Omnidirectional Depth Extension Networks
http://arxiv.org/abs/2007.01475
AUTHORS: Xinjing Cheng ; Peng Wang ; Yanqi Zhou ; Chenye Guan ; Ruigang Yang
COMMENTS: Accepted by ICRA 2020, 7 pages, 5 figures
HIGHLIGHT: In this paper, we propose a low-cost 3D sensing system that combines an omnidirectional camera with a calibrated projective depth camera, where the depth from the limited FoV can be automatically extended to the rest of the recorded omnidirectional image.
45, TITLE: Expected Eligibility Traces
http://arxiv.org/abs/2007.01839
AUTHORS: Hado van Hasselt ; Sephora Madjiheurem ; Matteo Hessel ; David Silver ; André Barreto ; Diana Borsa
HIGHLIGHT: In this work, we introduce expected eligibility traces.
46, TITLE: PsychFM: Predicting your next gamble
http://arxiv.org/abs/2007.01833
AUTHORS: Prakash Rajan ; Krishna P. Miyapuram
COMMENTS: To be published in International Joint Conference on Neural Networks (IJCNN) 2020 conference
HIGHLIGHT: A novel hybrid model namely psychological factorisation machine ( PsychFM ) has been proposed that involves concepts from machine learning as well as psychological theories.
47, TITLE: Collaborative Learning for Faster StyleGAN Embedding
http://arxiv.org/abs/2007.01758
AUTHORS: Shanyan Guan ; Ying Tai ; Bingbing Ni ; Feida Zhu ; Feiyue Huang ; Xiaokang Yang
COMMENTS: 10 pages, 11 figures
HIGHLIGHT: In this work, we propose a novel collaborative learning framework that consists of an efficient embedding network and an optimization-based iterator.
48, TITLE: MIRA: Leveraging Multi-Intention Co-click Information in Web-scale Document Retrieval using Deep Neural Networks
http://arxiv.org/abs/2007.01510
AUTHORS: Yusi Zhang ; Chuanjie Liu ; Angen Luo ; Hui Xue ; Xuan Shan ; Yuxiang Luo ; Yiqian Xia ; Yuanchi Yan ; Haidong Wang
HIGHLIGHT: In this work we aim to leverage the additional information for each document from its co-click neighbour to help document retrieval.
49, TITLE: Multi-Label Image Recognition with Multi-Class Attentional Regions
http://arxiv.org/abs/2007.01755
AUTHORS: Bin-Bin Gao ; Hong-Yu Zhou
HIGHLIGHT: In this paper, we propose a simple but efficient two-stream framework to recognize multi-category objects from global image to local regions, similar to how human beings perceive objects.
50, TITLE: Joint Frequency- and Image-Space Learning for Fourier Imaging
http://arxiv.org/abs/2007.01441
AUTHORS: Nalini M. Singh ; Juan Eugenio Iglesias ; Elfar Adalsteinsson ; Adrian V. Dalca ; Polina Golland
COMMENTS: 14 pages, 15 figures, image reconstruction, motion correction, denoising, magnetic resonance imaging, deep learning
HIGHLIGHT: We propose a neural network layer structure that combines frequency and image feature representations for robust Fourier image reconstruction.
51, TITLE: Continuously Indexed Domain Adaptation
http://arxiv.org/abs/2007.01807
AUTHORS: Hao Wang ; Hao He ; Dina Katabi
COMMENTS: Accepted at ICML 2020
HIGHLIGHT: In this paper, we propose the first method for continuously indexed domain adaptation.
52, TITLE: Three-dimensional Human Tracking of a Mobile Robot by Fusion of Tracking Results of Two Cameras
http://arxiv.org/abs/2007.01514
AUTHORS: Shinya Matsubara ; Akihiko Honda ; Yonghoon Ji ; Kazunori Umeda
COMMENTS: 4 pages, 11 figures
HIGHLIGHT: This paper proposes a process that uses two cameras to obtain three-dimensional (3D) information of a target object for human tracking.
53, TITLE: Deep Fence Estimation using Stereo Guidance and Adversarial Learning
http://arxiv.org/abs/2007.01724
AUTHORS: Paritosh Mittal ; Shankar M Venkatesan ; Viswanath Veera ; Aloknath De
COMMENTS: It was previously submitted to IEEE ICIP 2020. A previous version was also submitted to BMVC 2019
HIGHLIGHT: This work aims to accurately segment fence using a novel fence guidance mask (FM) generated from stereo image pair.
54, TITLE: Video Prediction via Example Guidance
http://arxiv.org/abs/2007.01738
AUTHORS: Jingwei Xu ; Huazhe Xu ; Bingbing Ni ; Xiaokang Yang ; Trevor Darrell
COMMENTS: Project Page: https://sites.google.com/view/vpeg-supp/home
HIGHLIGHT: In this work, we propose a simple yet effective framework that can efficiently predict plausible future states.
55, TITLE: Reading Comprehension in Czech via Machine Translation and Cross-lingual Transfer
http://arxiv.org/abs/2007.01667
AUTHORS: Kateřina Macková ; Milan Straka
COMMENTS: Accepted at TSD 2020, 23rd International Conference on Text, Speech and Dialogue
HIGHLIGHT: The cross-lingual transfer approach is very flexible and provides a reading comprehension in any language, for which we have enough monolingual raw texts.
56, TITLE: Low-Power Object Counting with Hierarchical Neural Networks
http://arxiv.org/abs/2007.01369
AUTHORS: Abhinav Goel ; Caleb Tung ; Sara Aghajanzadeh ; Isha Ghodgaonkar ; Shreya Ghosh ; George K. Thiruvathukal ; Yung-Hsiang Lu
COMMENTS: Paper accepted to ISLPED 2020: ACM/IEEE International Symposium on Low Power Electronics and Design
HIGHLIGHT: To reduce these redundancies, we propose a hierarchical DNN architecture for object counting.
57, TITLE: Ground Truth Free Denoising by Optimal Transport
http://arxiv.org/abs/2007.01575
AUTHORS: Sören Dittmer ; Carola-Bibiane Schönlieb ; Peter Maass
HIGHLIGHT: We present a learned unsupervised denoising method for arbitrary types of data, which we explore on images and one-dimensional signals.
58, TITLE: Multi-agent Planning for thermalling gliders using multi level graph-search
http://arxiv.org/abs/2007.01334
AUTHORS: Muhammad Aneeq uz Zaman ; Aamer Iqbal Bhatti
HIGHLIGHT: The problem addressed in this paper is of path planning for the gliders such that, the total number of interest points visited by the gliders is maximized.
59, TITLE: Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints
http://arxiv.org/abs/2007.01380
AUTHORS: C. P. Andriotis ; K. G. Papakonstantinou
HIGHLIGHT: In this work, these challenges are addressed within a joint framework of constrained Partially Observable Markov Decision Processes (POMDP) and multi-agent Deep Reinforcement Learning (DRL).
60, TITLE: D-NetPAD: An Explainable and Interpretable Iris Presentation Attack Detector
http://arxiv.org/abs/2007.01381
AUTHORS: Renu Sharma ; Arun Ross
HIGHLIGHT: In this work, we propose an effective and robust iris PA detector called D-NetPAD based on the DenseNet convolutional neural network architecture.
61, TITLE: Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment
http://arxiv.org/abs/2007.01383
AUTHORS: David Joon Ho ; Narasimhan P. Agaram ; Peter J. Schueffler ; Chad M. Vanderbilt ; Marc-Henri Jean ; Meera R. Hameed ; Thomas J. Fuchs
COMMENTS: Accepted at MICCAI 2020
HIGHLIGHT: In this paper, we describe Deep Interactive Learning (DIaL) as an efficient labeling approach for training CNNs.
62, TITLE: Ensemble Regression Models for Software Development Effort Estimation: A Comparative Study
http://arxiv.org/abs/2007.01719
AUTHORS: Halcyon D. P. Carvalho ; Marília N. C. A. Lima ; Wylliams B. Santos ; Roberta A. de A. Fagunde
HIGHLIGHT: Therefore, the proposed ensemble models in this study will help the project managers working with development quality software.
63, TITLE: Synergistic saliency and depth prediction for RGB-D saliency detection
http://arxiv.org/abs/2007.01711
AUTHORS: Yue Wang ; Yuke Li ; James H Elder ; Huchuan Lu ; Runmin Wu
HIGHLIGHT: Here we demonstrate a system for RGB-D saliency detection that makes effective joint use of large RGB saliency datasets with hand-labelled saliency ground truth together, and smaller RGB-D saliency datasets {\em without} saliency ground truth.
64, TITLE: Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data
http://arxiv.org/abs/2007.01335
AUTHORS: Francois Drielsma ; Qing Lin ; Pierre Côte de Soux ; Laura Dominé ; Ran Itay ; Dae Heun Koh ; Bradley J. Nelson ; Kazuhiro Terao ; Ka Vang Tsang ; Tracy L. Usher
HIGHLIGHT: GNNs are first studied with the goal of predicting the adjacency matrix of EM shower fragments and to identify the origin of showers, i.e. primary fragments.
65, TITLE: DATE: Defense Against TEmperature Side-Channel Attacks in DVFS Enabled MPSoCs
http://arxiv.org/abs/2007.01377
AUTHORS: Somdip Dey ; Amit Kumar Singh ; Xiaohang Wang ; Klaus Dieter McDonald-Maier
COMMENTS: 13 pages, 18 figures, 3 tables
HIGHLIGHT: In our proposed methodology, DATE: Defense Against TEmperature side-channel attacks, we propose a novel approach of reducing spatial and temporal thermal gradient, which makes the system more secure against temperature side-channel attacks, and at the same time increases the reliability of the device in terms of lifespan.
66, TITLE: Improving Event Detection using Contextual Word and Sentence Embeddings
http://arxiv.org/abs/2007.01379
AUTHORS: Mariano Maisonnave ; Fernando Delbianco ; Fernando Tohmé ; Ana Maguitman ; Evangelos Milios
HIGHLIGHT: The main contribution of this paper is the design, implementation and evaluation of a recurrent neural network model for ED that combines several features.
67, TITLE: Segment as Points for Efficient Online Multi-Object Tracking and Segmentation
http://arxiv.org/abs/2007.01550
AUTHORS: Zhenbo Xu ; Wei Zhang ; Xiao Tan ; Wei Yang ; Huan Huang ; Shilei Wen ; Errui Ding ; Liusheng Huang
COMMENTS: ECCV2020 ORAL (top 2%). Code already available at https://github.com/detectRecog/PointTrack. A highly effective method for learning features based on instance segments
HIGHLIGHT: In this paper, we propose a highly effective method for learning instance embeddings based on segments by converting the compact image representation to un-ordered 2D point cloud representation. Moreover, based on the observation that current MOTS datasets lack crowded scenes, we build a more challenging MOTS dataset named APOLLO MOTS with higher instance density.
68, TITLE: Learning-based Defect Recognition for Quasi-Periodic Microscope Images
http://arxiv.org/abs/2007.01309
AUTHORS: Nik Dennler ; Antonio Foncubierta-Rodriguez ; Titus Neupert ; Marilyne Sousa
COMMENTS: 18 pages including references and appendix, 5 figures
HIGHLIGHT: Here we propose a semi-supervised machine learning method that assists in the detection of lattice defects from atomic resolution microscope images.
69, TITLE: Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset
http://arxiv.org/abs/2007.01593
AUTHORS: Sören Dittmer ; Tobias Kluth ; Mads Thorstein Roar Henriksen ; Peter Maass
HIGHLIGHT: We investigate a novel reconstruction approach based on a deep image prior, which builds on representing the solution by a deep neural network.
70, TITLE: Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding
http://arxiv.org/abs/2007.01548
AUTHORS: Ammar Mansoor Kamoona ; Amirali Khodadadian Gosta ; Alireza Bab-Hadiashar ; Reza Hoseinnezhad
COMMENTS: The paper is under review
HIGHLIGHT: In this paper, we propose a weakly supervised deep temporal encoding-decoding solution for anomaly detection in surveillance videos using multiple instance learning.
71, TITLE: PointTrack++ for Effective Online Multi-Object Tracking and Segmentation
http://arxiv.org/abs/2007.01549
AUTHORS: Zhenbo Xu ; Wei Zhang ; Xiao Tan ; Wei Yang ; Xiangbo Su ; Yuchen Yuan ; Hongwu Zhang ; Shilei Wen ; Errui Ding ; Liusheng Huang
COMMENTS: CVPR2020 MOTS Challenge Winner. PointTrack++ ranks first on KITTI MOTS (http://www.cvlibs.net/datasets/kitti/eval_mots.php)
HIGHLIGHT: In this work, we present PointTrack++, an effective on-line framework for MOTS, which remarkably extends our recently proposed PointTrack framework.
72, TITLE: Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games
http://arxiv.org/abs/2007.01542
AUTHORS: Jeppe Theiss Kristensen ; Paolo Burelli
COMMENTS: 10 pages, 8 figures, to be published in 2020 Foundations of Digital Games conference
HIGHLIGHT: In this research work we are investigating and evaluating strategies to apply the popular RL method Proximal Policy Optimization (PPO) in a casual mobile puzzle game with a specific focus on improving its reliability in training and generalization during game playing.
73, TITLE: TICO-19: the Translation Initiative for Covid-19
http://arxiv.org/abs/2007.01788
AUTHORS: Antonios Anastasopoulos ; Alessandro Cattelan ; Zi-Yi Dou ; Marcello Federico ; Christian Federman ; Dmitriy Genzel ; Francisco Guzmán ; Junjie Hu ; Macduff Hughes ; Philipp Koehn ; Rosie Lazar ; Will Lewis ; Graham Neubig ; Mengmeng Niu ; Alp Öktem ; Eric Paquin ; Grace Tang ; Sylwia Tur
HIGHLIGHT: TICO-19: the Translation Initiative for Covid-19
74, TITLE: Multiple Expert Brainstorming for Domain Adaptive Person Re-identification
http://arxiv.org/abs/2007.01546
AUTHORS: Yunpeng Zhai ; Qixiang Ye ; Shijian Lu ; Mengxi Jia ; Rongrong Ji ; Yonghong Tian
COMMENTS: Accepted at ECCV'20
HIGHLIGHT: In this paper, we propose a multiple expert brainstorming network (MEB-Net) for domain adaptive person re-ID, opening up a promising direction about model ensemble problem under unsupervised conditions.
75, TITLE: A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
http://arxiv.org/abs/2007.01544
AUTHORS: Adam Bignold ; Francisco Cruz ; Matthew E. Taylor ; Tim Brys ; Richard Dazeley ; Peter Vamplew ; Cameron Foale
COMMENTS: 33 pages, 8 figures
HIGHLIGHT: In this work, we propose a conceptual framework and taxonomy for assisted reinforcement learning, aimed at fostering such collaboration by classifying and comparing various methods that use external information in the learning process.
76, TITLE: Evaluating Uncertainty Estimation Methods on 3D Semantic Segmentation of Point Clouds
http://arxiv.org/abs/2007.01787
AUTHORS: Swaroop Bhandary K ; Nico Hochgeschwender ; Paul Plöger ; Frank Kirchner ; Matias Valdenegro-Toro
COMMENTS: 12 pages, 19 figures, ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning
HIGHLIGHT: In this work, we evaluate three uncertainty quantification methods namely Deep Ensembles, MC-Dropout and MC-DropConnect on the DarkNet21Seg 3D semantic segmentation model and comprehensively analyze the impact of various parameters such as number of models in ensembles or forward passes, and drop probability values, on task performance and uncertainty estimate quality.
77, TITLE: Weakly Supervised Temporal Action Localization with Segment-Level Labels
http://arxiv.org/abs/2007.01598
AUTHORS: Xinpeng Ding ; Nannan Wang ; Xinbo Gao ; Jie Li ; Xiaoyu Wang ; Tongliang Liu
COMMENTS: 18 pages,7 figures
HIGHLIGHT: In this paper, we introduce a new segment-level supervision setting: segments are labeled when annotators observe actions happening here.
78, TITLE: Decoder-free Robustness Disentanglement without (Additional) Supervision
http://arxiv.org/abs/2007.01356
AUTHORS: Yifei Wang ; Dan Peng ; Furui Liu ; Zhenguo Li ; Zhitang Chen ; Jiansheng Yang
HIGHLIGHT: Decoder-free Robustness Disentanglement without (Additional) Supervision
79, TITLE: LOL: Lidar-Only Odometry and Localization in 3D Point Cloud Maps
http://arxiv.org/abs/2007.01595
AUTHORS: David Rozenberszki ; Andras Majdik
COMMENTS: Accepted paper for ICRA 2020, Github repository for implementation at: https://github.com/RozDavid/LOL
HIGHLIGHT: In this paper we deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against.
80, TITLE: Bayesian multilingual topic model for zero-shot cross-lingual topic identification
http://arxiv.org/abs/2007.01359
AUTHORS: Santosh Kesiraju ; Sangeet Sagar ; Ondřej Glembek ; Lukáš Burget ; Suryakanth V Gangashetty
COMMENTS: 10 pages, 5 figures
HIGHLIGHT: This paper presents a Bayesian multilingual topic model for learning language-independent document embeddings.
81, TITLE: An Autonomous Free Airspace En-route Controller using Deep Reinforcement Learning Techniques
http://arxiv.org/abs/2007.01599
AUTHORS: Joris Mollinga ; Herke van Hoof
COMMENTS: Published at ICRAT2020
HIGHLIGHT: In this paper an air traffic control model is presented that guides an arbitrary number of aircraft across a three-dimensional, unstructured airspace while avoiding conflicts and collisions.
82, TITLE: Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN
http://arxiv.org/abs/2007.01571
AUTHORS: Zhenwei He ; Lei Zhang
COMMENTS: The paper is accepted in ECCV2020
HIGHLIGHT: Therefore, in order to avoid the source domain collapse risk caused by parameter sharing, we propose an asymmetric tri-way Faster-RCNN (ATF) for domain adaptive object detection.
==========Updates to Previous Papers==========
1, TITLE: A Brief Look at Generalization in Visual Meta-Reinforcement Learning
http://arxiv.org/abs/2006.07262
AUTHORS: Safa Alver ; Doina Precup
COMMENTS: Accepted to the 4th Lifelong Learning Workshop at ICML 2020
HIGHLIGHT: In this paper, we assess the generalization performance of these algorithms by leveraging high-dimensional, procedurally generated environments.
2, TITLE: Self-explaining AI as an alternative to interpretable AI
http://arxiv.org/abs/2002.05149
AUTHORS: Daniel C. Elton
COMMENTS: 10pgs, 2 column format
HIGHLIGHT: To show how we might be able to trust AI despite these problems we introduce the concept of self-explaining AI.
3, TITLE: On Tractable Representations of Binary Neural Networks
http://arxiv.org/abs/2004.02082
AUTHORS: Weijia Shi ; Andy Shih ; Adnan Darwiche ; Arthur Choi
COMMENTS: In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR) 2020
HIGHLIGHT: We consider the compilation of a binary neural network's decision function into tractable representations such as Ordered Binary Decision Diagrams (OBDDs) and Sentential Decision Diagrams (SDDs).
4, TITLE: Measuring Non-Expert Comprehension of Machine Learning Fairness Metrics
http://arxiv.org/abs/2001.00089
AUTHORS: Debjani Saha ; Candice Schumann ; Duncan C. McElfresh ; John P. Dickerson ; Michelle L. Mazurek ; Michael Carl Tschantz
HIGHLIGHT: We take initial steps toward bridging this gap between ML researchers and the public, by addressing the question: does a lay audience understand a basic definition of ML fairness?
5, TITLE: Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation
http://arxiv.org/abs/2003.04614
AUTHORS: Jinyu Yang ; Weizhi An ; Sheng Wang ; Xinliang Zhu ; Chaochao Yan ; Junzhou Huang
COMMENTS: Accepted by ECCV 2020
HIGHLIGHT: Here, we present an innovative framework, designed to mitigate the image translation bias and align cross-domain features with the same category.
6, TITLE: GRNet: Gridding Residual Network for Dense Point Cloud Completion
http://arxiv.org/abs/2006.03761
AUTHORS: Haozhe Xie ; Hongxun Yao ; Shangchen Zhou ; Jiageng Mao ; Shengping Zhang ; Wenxiu Sun
COMMENTS: ECCV 2020
HIGHLIGHT: To solve this problem, we introduce 3D grids as intermediate representations to regularize unordered point clouds.
7, TITLE: Long-tail learning with class descriptors
http://arxiv.org/abs/2004.02235
AUTHORS: Dvir Samuel ; Yuval Atzmon ; Gal Chechik
HIGHLIGHT: We describe DRAGON, a late-fusion architecture for long-tail learning with class descriptors.
8, TITLE: Monocular Depth Estimation Based On Deep Learning: An Overview
http://arxiv.org/abs/2003.06620
AUTHORS: Chaoqiang Zhao ; Qiyu Sun ; Chongzhen Zhang ; Yang Tang ; Feng Qian
COMMENTS: 14 pages, 4 figures
HIGHLIGHT: Therefore, we survey the current monocular depth estimation methods based on deep learning in this review.
9, TITLE: Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
http://arxiv.org/abs/2006.14984
AUTHORS: Chengliang Dai ; Shuo Wang ; Yuanhan Mo ; Kaichen Zhou ; Elsa Angelini ; Yike Guo ; Wenjia Bai
COMMENTS: Paper accepted by MICCAI 2020
HIGHLIGHT: In this paper, we propose an efficient annotation framework for brain tumour images that is able to suggest informative sample images for human experts to annotate.
10, TITLE: PrimA6D: Rotational Primitive Reconstruction for Enhanced and Robust 6D Pose Estimation
http://arxiv.org/abs/2006.07789
AUTHORS: MyungHwan Jeon ; Ayoung Kim
COMMENTS: RA-L and IROS 2020
HIGHLIGHT: In this paper, we introduce a rotational primitive prediction based 6D object pose estimation using a single image as an input.
11, TITLE: PraNet: Parallel Reverse Attention Network for Polyp Segmentation
http://arxiv.org/abs/2006.11392
AUTHORS: Deng-Ping Fan ; Ge-Peng Ji ; Tao Zhou ; Geng Chen ; Huazhu Fu ; Jianbing Shen ; Ling Shao
COMMENTS: Accepted to MICCAI 2020
HIGHLIGHT: To address these challenges, we propose a parallel reverse attention network (PraNet) for accurate polyp segmentation in colonoscopy images.
12, TITLE: Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
http://arxiv.org/abs/2003.03151
AUTHORS: Meiling Fang ; Naser Damer ; Florian Kirchbuchner ; Arjan Kuijper
COMMENTS: accepted for publication at EUSIPCO2020
HIGHLIGHT: Hence, we investigate and analyze the demographic bias in iris PAD algorithms in this paper.
13, TITLE: Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness
http://arxiv.org/abs/2005.00060
AUTHORS: Pu Zhao ; Pin-Yu Chen ; Payel Das ; Karthikeyan Natesan Ramamurthy ; Xue Lin
COMMENTS: accepted by ICLR 2020
HIGHLIGHT: In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.
14, TITLE: Rethinking Distributional Matching Based Domain Adaptation
http://arxiv.org/abs/2006.13352
AUTHORS: Bo Li ; Yezhen Wang ; Tong Che ; Shanghang Zhang ; Sicheng Zhao ; Pengfei Xu ; Wei Zhou ; Yoshua Bengio ; Kurt Keutzer
COMMENTS: Preprint version
HIGHLIGHT: We hope our intuitive yet effective method will serve as a useful new direction and increase the robustness of DA in real scenarios. In this paper, in order to devise robust DA algorithms, we first systematically analyze the limitations of DM based methods, and then build new benchmarks with more realistic domain shifts to evaluate the well-accepted DM methods.
15, TITLE: Am I Building a White Box Agent or Interpreting a Black Box Agent?
http://arxiv.org/abs/2007.01187
AUTHORS: Tom Bewley
COMMENTS: 6 pages; pre-print
HIGHLIGHT: The rule extraction literature contains the notion of a fidelity-accuracy dilemma: when building an interpretable model of a black box function, optimising for fidelity is likely to reduce performance on the underlying task, and vice versa.
16, TITLE: SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing
http://arxiv.org/abs/1906.07927
AUTHORS: Haonan Qiu ; Chaowei Xiao ; Lei Yang ; Xinchen Yan ; Honglak Lee ; Bo Li
COMMENTS: To appear at ECCV 2020
HIGHLIGHT: In this paper, we aim to explore the impact of semantic manipulation on DNNs predictions by manipulating the semantic attributes of images and generate "unrestricted adversarial examples".
17, TITLE: SimAug: Learning Robust Representations from 3D Simulation for Pedestrian Trajectory Prediction in Unseen Cameras
http://arxiv.org/abs/2004.02022
AUTHORS: Junwei Liang ; Lu Jiang ; Alexander Hauptmann
COMMENTS: Accepted by ECCV 2020. Project website: https://next.cs.cmu.edu/multiverse/
HIGHLIGHT: We propose a method to efficiently utilize multi-view 3D simulation data for training.
18, TITLE: JUMPS: Joints Upsampling Method for Pose Sequences
http://arxiv.org/abs/2007.01151
AUTHORS: Lucas Mourot ; François Le Clerc ; Cédric Thébault ; Pierre Hellier
COMMENTS: 7 pages, 7 figures
HIGHLIGHT: To this purpose, we propose a novel method called JUMPS for increasing the number of joints in 2D pose estimates and recovering occluded or missing joints.
19, TITLE: Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles
http://arxiv.org/abs/1803.10681
AUTHORS: Juan Castorena ; Gint Puskorius ; Gaurav Pandey
HIGHLIGHT: This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling.
20, TITLE: Lower Bounds for QBFs of Bounded Treewidth
http://arxiv.org/abs/1910.01047
AUTHORS: Johannes Klaus Fichte ; Markus Hecher ; Andreas Pfandler
HIGHLIGHT: In this work, we show lower bounds based on the ETH for arbitrary QBFs parameterized by treewidth (and quantifier depth).
21, TITLE: Lightme: Analysing Language in Internet Support Groups for Mental Health
http://arxiv.org/abs/2007.00824
AUTHORS: Gabriela Ferraro ; Brendan Loo Gee ; Shenjia Ji ; Luis Salvador-Carulla
HIGHLIGHT: Lightme: Analysing Language in Internet Support Groups for Mental Health
22, TITLE: SCAN: Learning to Classify Images without Labels
http://arxiv.org/abs/2005.12320
AUTHORS: Wouter Van Gansbeke ; Simon Vandenhende ; Stamatios Georgoulis ; Marc Proesmans ; Luc Van Gool
COMMENTS: Accepted at ECCV 2020. Includes supplementary. Code and pretrained models at https://github.com/wvangansbeke/Unsupervised-Classification
HIGHLIGHT: In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled.
23, TITLE: Early Exit Or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
http://arxiv.org/abs/2006.16581
AUTHORS: Qunliang Xing ; Mai Xu ; Tianyi Li ; Zhenyu Guan
COMMENTS: Accepted by ECCV 2020
HIGHLIGHT: In this paper, we propose a resource-efficient blind quality enhancement (RBQE) approach for compressed images.
24, TITLE: A Review of Emergency Incident Prediction, Resource Allocation and Dispatch Models
http://arxiv.org/abs/2006.04200
AUTHORS: Ayan Mukhopadhyay ; Geoffrey Pettet ; Sayyed Vazirizade ; Yevgeniy Vorobeychik ; Mykel Kochenderfer ; Abhishek Dubey
HIGHLIGHT: In this survey, we present models for incident prediction, resource allocation, and dispatch concerning urban emergency incidents like roadway accidents and crashes.
25, TITLE: Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning
http://arxiv.org/abs/1909.04134
AUTHORS: Arjun Manoharan ; Rahul Ramesh ; Balaraman Ravindran
COMMENTS: ECML-PKDD 2020
HIGHLIGHT: In this work, we propose Option Encoder, an auto-encoder based framework with intelligently constrained weights, that helps discover a collection of basis policies.
26, TITLE: Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation
http://arxiv.org/abs/1910.05370
AUTHORS: Ilkay Oksuz ; James R. Clough ; Bram Ruijsink ; Esther Puyol Anton ; Aurelien Bustin ; Gastao Cruz ; Claudia Prieto ; Andrew P. King ; Julia A. Schnabel
COMMENTS: Accepted for publication in IEEE TMI
HIGHLIGHT: In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.
27, TITLE: Indoor Scene Recognition in 3D
http://arxiv.org/abs/2002.12819
AUTHORS: Shengyu Huang ; Mikhail Usvyatsov ; Konrad Schindler
COMMENTS: IROS 2020 - Camera Ready
HIGHLIGHT: Here, we study scene recognition from 3D point cloud (or voxel) data, and show that it greatly outperforms methods based on 2D birds-eye views.
28, TITLE: Reinforcement Learning Generalization with Surprise Minimization
http://arxiv.org/abs/2004.12399
AUTHORS: Jerry Zikun Chen
COMMENTS: Inductive biases, invariances and generalization in RL Workshop, ICML 2020
HIGHLIGHT: In this work, we propose and evaluate a surprise minimizing agent on a generalization benchmark to show an additional reward learned from a simple density model can show robustness in procedurally generated game environments that provide constant source of entropy and stochasticity.
29, TITLE: Effective writing style imitation via combinatorial paraphrasing
http://arxiv.org/abs/1905.13464
AUTHORS: Tommi Gröndahl ; N. Asokan
COMMENTS: 16 pages, 1 figure, Accepted for publication in Privacy Enhancing Technologies (PETS2020)
HIGHLIGHT: To mitigate this problem we propose ParChoice: a technique based on the combinatorial application of multiple paraphrasing algorithms.
30, TITLE: On the Entanglement Cost of One-Shot Compression
http://arxiv.org/abs/1905.02110
AUTHORS: Shima Bab Hadiashar ; Ashwin Nayak
COMMENTS: 23 pages, 1 figure. The paper is the same as v3; only metadata are updated. In v3: Main body reorganized; Thm 1.1 rephrased; a discussion of the entanglement measure added to Sec. 4; several improvements to the text throughout; Cor. 3.5 and Cor. 3.6 merged; Appendix created for the proofs of Cor. 3.5 and another statement in the main body; typos fixed; three references added
HIGHLIGHT: We revisit the task of visible compression of an ensemble of quantum states with entanglement assistance in the one-shot setting.
31, TITLE: A Double Exponential Lower Bound for the Distinct Vectors Problem
http://arxiv.org/abs/2002.01293
AUTHORS: Marcin Pilipczuk ; Manuel Sorge
HIGHLIGHT: We show that this running time bound is essentially optimal by showing that there is a constant c such that the existence of an algorithm solving Distinct Vectors with running time 2^(O(2^(ck))) * poly(|A|) would contradict the Exponential Time Hypothesis.
32, TITLE: Emergence of Separable Manifolds in Deep Language Representations
http://arxiv.org/abs/2006.01095
AUTHORS: Jonathan Mamou ; Hang Le ; Miguel Del Rio ; Cory Stephenson ; Hanlin Tang ; Yoon Kim ; SueYeon Chung
COMMENTS: 9 pages. 10 figures. Accepted to ICML 2020
HIGHLIGHT: In this work, we utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience that connects geometry of feature representations with linear separability of classes, to analyze language representations from large-scale contextual embedding models.
33, TITLE: WaveNODE: A Continuous Normalizing Flow for Speech Synthesis
http://arxiv.org/abs/2006.04598
AUTHORS: Hyeongju Kim ; Hyeonseung Lee ; Woo Hyun Kang ; Sung Jun Cheon ; Byoung Jin Choi ; Nam Soo Kim
COMMENTS: 8 pages, 4 figures, Second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models (ICML 2020)
HIGHLIGHT: In this paper, we propose a novel generative model called WaveNODE which exploits a continuous normalizing flow for speech synthesis.
34, TITLE: Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture
http://arxiv.org/abs/2005.03454
AUTHORS: Christopher Brix ; Parnia Bahar ; Hermann Ney
COMMENTS: Accepted at ACL2020; evaluation corrected: magnitude pruning may be useful to find winning lottery tickets
HIGHLIGHT: Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs.
35, TITLE: Learning selection strategies in Buchberger's algorithm
http://arxiv.org/abs/2005.01917
AUTHORS: Dylan Peifer ; Michael Stillman ; Daniel Halpern-Leistner
COMMENTS: 14 pages, to appear in Proceedings of the 37th International Conference on Machine Learning (ICML 2020)
HIGHLIGHT: We introduce a new approach to Buchberger's algorithm that uses reinforcement learning agents to perform S-pair selection, a key step in the algorithm.
36, TITLE: Complexity of Computing the Anti-Ramsey Numbers for Paths
http://arxiv.org/abs/1810.08004
AUTHORS: Saeed Akhoondian Amiri ; Alexandru Popa ; Mohammad Roghani ; Golnoosh Shahkarami ; Reza Soltani ; Hossein Vahidi
HIGHLIGHT: Our first main contribution is to prove that computing $\textrm{ar}(G,P_k)$ for every integer $k>2$ is NP-hard.
37, TITLE: Public Willingness to Get Vaccinated Against COVID-19: How AI-Developed Vaccines Can Affect Acceptance
http://arxiv.org/abs/2006.08164
AUTHORS: Gabriel Lima ; Hyeyoung Hwang ; Chiyoung Cha ; Meeyoung Cha
HIGHLIGHT: We used a between-subjects study design (N=572 adults in the US and UK) to understand the public willingness towards vaccination against the novel coronavirus under various circumstances.
38, TITLE: AdvAug: Robust Adversarial Augmentation for Neural Machine Translation
http://arxiv.org/abs/2006.11834
AUTHORS: Yong Cheng ; Lu Jiang ; Wolfgang Macherey ; Jacob Eisenstein
COMMENTS: published at ACL2020
HIGHLIGHT: In this paper, we propose a new adversarial augmentation method for Neural Machine Translation (NMT).
39, TITLE: Person Re-identification: Implicitly Defining the Receptive Fields of Deep Learning Classification Frameworks
http://arxiv.org/abs/2001.11267
AUTHORS: Ehsan Yaghoubi ; Diana Borza ; Aruna Kumar ; Hugo Proença
COMMENTS: Submitted to PRL
HIGHLIGHT: This paper describes a solution for implicitly driving the inference of the networks' receptive fields, by creating synthetic learning data composed of interchanged segments that should be \emph{apriori} important/irrelevant for the network decision.
40, TITLE: Multimodal Prediction based on Graph Representations
http://arxiv.org/abs/1912.10314
AUTHORS: Icaro Cavalcante Dourado ; Salvatore Tabbone ; Ricardo da Silva Torres
HIGHLIGHT: This paper proposes a learning model, based on rank-fusion graphs, for general applicability in multimodal prediction tasks, such as multimodal regression and image classification.
41, TITLE: Object-Oriented Video Captioning with Trajectory Graph and Attribute Exploring
http://arxiv.org/abs/2003.03715
AUTHORS: Fangyi Zhu ; Jenq-Neng Hwang ; Zhanyu Ma ; Guang Chen ; Jun Guo
HIGHLIGHT: We propose a novel task, named object-oriented video captioning, focusing on understanding the videos in object-level. Thereafter, we construct a new dataset with explicit object-sentence pairs to facilitate effective cross-modal learning.
42, TITLE: Symmetric Regularization based BERT for Pair-wise Semantic Reasoning
http://arxiv.org/abs/1909.03405
AUTHORS: Weidi Xu ; Xingyi Cheng ; Kunlong Chen ; Wei Wang ; Bin Bi ; Ming Yan ; Chen Wu ; Luo Si ; Wei Chu ; Taifeng Wang
COMMENTS: 8 pages, 3 figures, 6 tables
HIGHLIGHT: To remedy this, we propose to augment the NSP task to a 3-class categorization task, which includes a category for previous sentence prediction (PSP).
43, TITLE: A new approach for pedestrian density estimation using moving sensors and computer vision
http://arxiv.org/abs/1811.05006
AUTHORS: Eric K. Tokuda ; Yitzchak Lockerman ; Gabriel B. A. Ferreira ; Ethan Sorrelgreen ; David Boyle ; Roberto M. Cesar-Jr. ; Claudio T. Silva
COMMENTS: Submitted to ACM-TSAS
HIGHLIGHT: In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density.
44, TITLE: UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree
http://arxiv.org/abs/2006.09102
AUTHORS: Kacper Kania ; Maciej Zięba ; Tomasz Kajdanowicz
COMMENTS: Under review at Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020), 13 pages, 7 figures; fix the reference to the CSG-Net work
HIGHLIGHT: On the contrary, we propose a model that extracts a CSG parse tree without any supervision - UCSG-Net.
45, TITLE: Reconstructing Sinus Anatomy from Endoscopic Video -- Towards a Radiation-free Approach for Quantitative Longitudinal Assessment
http://arxiv.org/abs/2003.08502
AUTHORS: Xingtong Liu ; Maia Stiber ; Jindan Huang ; Masaru Ishii ; Gregory D. Hager ; Russell H. Taylor ; Mathias Unberath
COMMENTS: Accepted to MICCAI 2020
HIGHLIGHT: We present a patient-specific, learning-based method for 3D reconstruction of sinus surface anatomy directly and only from endoscopic videos.
46, TITLE: MPLP: Learning a Message Passing Learning Protocol
http://arxiv.org/abs/2007.00970
AUTHORS: Ettore Randazzo ; Eyvind Niklasson ; Alexander Mordvintsev
COMMENTS: Code at https://github.com/google-research/self-organising-systems/tree/master/mplp; code base link fixed
HIGHLIGHT: We present a novel method for learning the weights of an artificial neural network - a Message Passing Learning Protocol (MPLP).
47, TITLE: J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
http://arxiv.org/abs/1911.02945
AUTHORS: Hemant Kumar Aggarwal ; Mathews Jacob
HIGHLIGHT: We introduce a continuous strategy to jointly optimize the sampling pattern and network parameters.
48, TITLE: Learning Permutation Invariant Representations using Memory Networks
http://arxiv.org/abs/1911.07984
AUTHORS: Shivam Kalra ; Mohammed Adnan ; Graham Taylor ; Hamid Tizhoosh
COMMENTS: Accepted at ECCV 2020
HIGHLIGHT: In this work, we present a permutation invariant neural network called Memory-based Exchangeable Model (MEM) for learning set functions.
49, 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.
50, TITLE: Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
http://arxiv.org/abs/2004.01613
AUTHORS: Gencer Sumbul ; Jian Kang ; Begüm Demir
COMMENTS: To appear as a book chapter in "Deep Learning for the Earth Sciences", John Wiley & Sons, 2020
HIGHLIGHT: This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives.
51, TITLE: A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms
http://arxiv.org/abs/1605.01177
AUTHORS: Ángel F. García-Fernández ; Abu Sajana Rahmathullah ; Lennart Svensson
COMMENTS: accepted in IEEE Transactions on Signal Processing. Matlab code for the metric can be found at https://github.com/Agarciafernandez/MTT
HIGHLIGHT: In this paper, we propose a metric on the space of finite sets of trajectories for assessing multi-target tracking algorithms in a mathematically sound way.
52, TITLE: Causal Discovery in Physical Systems from Videos
http://arxiv.org/abs/2007.00631
AUTHORS: Yunzhu Li ; Antonio Torralba ; Animashree Anandkumar ; Dieter Fox ; Animesh Garg
COMMENTS: Project page: https://yunzhuli.github.io/V-CDN/
HIGHLIGHT: In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of the dynamical system.
53, TITLE: Multi-View Broad Learning System for Primate Oculomotor Decision Decoding
http://arxiv.org/abs/1908.06180
AUTHORS: Zhenhua Shi ; Xiaomo Chen ; Changming Zhao ; He He ; Veit Stuphorn ; Dongrui Wu
HIGHLIGHT: In this paper, we extended broad learning system (BLS), a recently proposed wide neural network architecture, from single-view learning to multi-view learning, and validated its performance in decoding monkeys' oculomotor decision from medial frontal LFPs and spikes.
54, TITLE: Recurrent Independent Mechanisms
http://arxiv.org/abs/1909.10893
AUTHORS: Anirudh Goyal ; Alex Lamb ; Jordan Hoffmann ; Shagun Sodhani ; Sergey Levine ; Yoshua Bengio ; Bernhard Schölkopf
HIGHLIGHT: We propose Recurrent Independent Mechanisms (RIMs), a new recurrent architecture in which multiple groups of recurrent cells operate with nearly independent transition dynamics, communicate only sparingly through the bottleneck of attention, and are only updated at time steps where they are most relevant.
55, TITLE: MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform
http://arxiv.org/abs/2005.08365
AUTHORS: Xiang Gao ; Michel Galley ; Bill Dolan
COMMENTS: accepted at ACL 2020
HIGHLIGHT: We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.
56, TITLE: 3D Pipe Network Reconstruction Based on Structure from Motion with Incremental Conic Shape Detection and Cylindrical Constraint
http://arxiv.org/abs/2006.10383
AUTHORS: Sho kagami ; Hajime Taira ; Naoyuki Miyashita ; Akihiko Torii ; Masatoshi Okutomi
COMMENTS: This manuscript was accepted and presented in the 29th IEEE International Symposium on Industrial Electronics (ISIE2020)
HIGHLIGHT: In this paper, we propose a 3D pipe reconstruction system using sequential images captured by a monocular endoscopic camera.
57, TITLE: OmniNet: A unified architecture for multi-modal multi-task learning
http://arxiv.org/abs/1907.07804
AUTHORS: Subhojeet Pramanik ; Priyanka Agrawal ; Aman Hussain
COMMENTS: Source code available at: https://github.com/subho406/OmniNet
HIGHLIGHT: We introduce an extended and unified architecture that can be used for tasks involving a variety of modalities like image, text, videos, etc.
58, TITLE: Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
http://arxiv.org/abs/2007.00487
AUTHORS: Timothée Lesort
COMMENTS: Thesis Manuscript
HIGHLIGHT: In this thesis, we propose to explore continual algorithms with replay processes.
59, TITLE: Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER
http://arxiv.org/abs/1908.11046
AUTHORS: Peng-Hsuan Li ; Tsu-Jui Fu ; Wei-Yun Ma
COMMENTS: In proceedings of AAAI 2020
HIGHLIGHT: We give in-depth analyses of the improvements across several aspects of NER, especially the identification of multi-token mentions.
60, TITLE: Don't Panic! Better, Fewer, Syntax Errors for LR Parsers
http://arxiv.org/abs/1804.07133
AUTHORS: Lukas Diekmann ; Laurence Tratt
COMMENTS: 32 pages, 18 figures
HIGHLIGHT: In this paper we introduce the CPCT+ algorithm, and an implementation of that algorithm, that address these issues.
61, TITLE: Traditional and accelerated gradient descent for neural architecture search
http://arxiv.org/abs/2006.15218
AUTHORS: Nicolas Garcia Trillos ; Felix Morales ; Javier Morales
HIGHLIGHT: In this paper, we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [4], which aimed at introducing the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for the optimization of a function on a semi-discrete space using ideas from optimal transport theory.
62, TITLE: A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty
http://arxiv.org/abs/2006.01031
AUTHORS: Valentin Peretroukhin ; Matthew Giamou ; David M. Rosen ; W. Nicholas Greene ; Nicholas Roy ; Jonathan Kelly
COMMENTS: In Proceedings of Robotics: Science and Systems (RSS'20), Corvallis , Oregon, USA, Jul. 12-16, 2020
HIGHLIGHT: In this work, we present a novel symmetric matrix representation of the 3D rotation group, SO(3), with two important properties that make it particularly suitable for learned models: (1) it satisfies a smoothness property that improves convergence and generalization when regressing large rotation targets, and (2) it encodes a symmetric Bingham belief over the space of unit quaternions, permitting the training of uncertainty-aware models.
63, TITLE: Testing linear-invariant properties
http://arxiv.org/abs/1911.06793
AUTHORS: Jonathan Tidor ; Yufei Zhao
COMMENTS: 40 pages; updated with significantly improved main result
HIGHLIGHT: This allows us to extend the work of Bhattacharyya, Fischer, Hatami, Hatami, and Lovett by removing the bounded complexity restriction in their work.
64, TITLE: Visual Transformers: Token-based Image Representation and Processing for Computer Vision
http://arxiv.org/abs/2006.03677
AUTHORS: Bichen Wu ; Chenfeng Xu ; Xiaoliang Dai ; Alvin Wan ; Peizhao Zhang ; Masayoshi Tomizuka ; Kurt Keutzer ; Peter Vajda
HIGHLIGHT: In this work, we challenge this paradigm: we instead (a) represent images as a set of visual tokens and (b) apply visual transformers to find relationships between visual semantic concepts.
65, TITLE: Lee-Yang zeros and the complexity of the ferromagnetic Ising Model on bounded-degree graphs
http://arxiv.org/abs/2006.14828
AUTHORS: Pjotr Buys ; Andreas Galanis ; Viresh Patel ; Guus Regts
COMMENTS: 38 pages, 1 figure
HIGHLIGHT: We study the computational complexity of approximating the partition function of the ferromagnetic Ising model in the Lee-Yang circle of zeros given by $|\lambda|=1$, where $\lambda$ is the external field of the model.
66, TITLE: On Sufficient and Necessary Conditions in Bounded CTL: A Forgetting Approach
http://arxiv.org/abs/2003.06492
AUTHORS: Renyan Feng ; Erman Acar ; Stefan Schlobach ; Yisong Wang ; Wanwei Liu
HIGHLIGHT: To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model and over a given signature.
67, TITLE: Instance-Invariant Adaptive Object Detection via Progressive Disentanglement
http://arxiv.org/abs/1911.08712
AUTHORS: Aming Wu ; Yahong Han ; Linchao Zhu ; Yi Yang
HIGHLIGHT: Particularly, base on disentangled learning used for feature decomposition, we devise two disentangled layers to decompose domain-invariant and domain-specific features.