cs.AI - 人工智能

    cs.AR - 硬件体系结构 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 econ.GN - 一般经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.soc-ph - 物理学与社会 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]An Argumentation-based Approach for Identifying and Dealing with Incompatibilities among Procedural Goals
    • [cs.AI]Counterfactual Explanations & Adversarial Examples — Common Grounds, Essential Differences, and Potential Transfers
    • [cs.AI]Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
    • [cs.AI]Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
    • [cs.AI]Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity
    • [cs.AI]Systematic Generalization on gSCAN with Language Conditioned Embedding
    • [cs.AR]Accelerating Recommender Systems via Hardware “scale-in”
    • [cs.AR]An Open-Source Platform for High-Performance Non-Coherent On-Chip Communication
    • [cs.CL]A Comparison of LSTM and BERT for Small Corpus
    • [cs.CL]Accelerating Real-Time Question Answering via Question Generation
    • [cs.CL]Deep Learning for Semantic Relations
    • [cs.CL]FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
    • [cs.CL]IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
    • [cs.CL]RadLex Normalization in Radiology Reports
    • [cs.CL]Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
    • [cs.CL]Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation
    • [cs.CL]Sparsifying Transformer Models with Differentiable Representation Pooling
    • [cs.CL]UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT
    • [cs.CL]WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets
    • [cs.CL]Weakly Supervised Content Selection for Improved Image Captioning
    • [cs.CR]A Smart Home System based on Internet of Things
    • [cs.CR]Accelerating 2PC-based ML with Limited Trusted Hardware
    • [cs.CR]Federated Model Distillation with Noise-Free Differential Privacy
    • [cs.CR]Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
    • [cs.CV]A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds
    • [cs.CV]AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
    • [cs.CV]Adversarial Learning for Zero-shot Domain Adaptation
    • [cs.CV]An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling
    • [cs.CV]Attribute-conditioned Layout GAN for Automatic Graphic Design
    • [cs.CV]Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
    • [cs.CV]Devil’s in the Detail: Graph-based Key-point Alignment and Embedding for Person Re-ID
    • [cs.CV]Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN
    • [cs.CV]Evaluation of the Robustness of Visual SLAM Methods in Different Environments
    • [cs.CV]Fairness Matters — A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems
    • [cs.CV]HAA500: Human-Centric Atomic Action Dataset with Curated Videos
    • [cs.CV]Heterogeneous Domain Generalization via Domain Mixup
    • [cs.CV]Hybrid Space Learning for Language-based Video Retrieval
    • [cs.CV]Image Conditioned Keyframe-Based Video Summarization Using Object Detection
    • [cs.CV]MRZ code extraction from visa and passport documents using convolutional neural networks
    • [cs.CV]Meta Learning for Few-Shot One-class Classification
    • [cs.CV]Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps
    • [cs.CV]Optimizing Convolutional Neural Network Architecture via Information Field
    • [cs.CV]PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
    • [cs.CV]SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks
    • [cs.CV]Spectral Analysis Network for Deep Representation Learning and Image Clustering
    • [cs.CV]TP-LSD: Tri-Points Based Line Segment Detector
    • [cs.CV]The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
    • [cs.CV]Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
    • [cs.CV]Variance Loss: A Confidence-Based Reweighting Strategy for Coarse Semantic Segmentation
    • [cs.CY]”Is it a Qoincidence?”: A First Step Towards Understanding and Characterizing the QAnon Movement on Voat.co
    • [cs.CY]A Review on Security and Privacy of Internet of Medical Things
    • [cs.CY]Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse
    • [cs.CY]On the Fairness of ‘Fake’ Data in Legal AI
    • [cs.DC]CASH: A Credit Aware Scheduling for Public Cloud Platforms
    • [cs.DC]Hierarchical Roofline Performance Analysis for Deep Learning Applications
    • [cs.DC]Kvik: A task based middleware with composable scheduling policies
    • [cs.HC]Visually Analyzing and Steering Zero Shot Learning
    • [cs.IR]Patient Cohort Retrieval using Transformer Language Models
    • [cs.IR]TRec: Sequential Recommender Based On Latent Item Trend Information
    • [cs.IT]An Improved Multi-access Coded Caching with Uncoded Placement
    • [cs.IT]Capacity-Approaching Autoencoders for Communications
    • [cs.IT]Construction of Hyperbolic Signal Sets from the Uniformization of Hyperelliptic Curves
    • [cs.IT]Efficient Detectors for Telegram Splitting based Transmission in Low Power Wide Area Networks with Bursty Interference
    • [cs.IT]End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
    • [cs.IT]Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems
    • [cs.IT]Generalized Energy Detection Under Generalized Noise Channels
    • [cs.LG]A First Step Towards Distribution Invariant Regression Metrics
    • [cs.LG]A kernel function for Signal Temporal Logic formulae
    • [cs.LG]Accurate and Intuitive Contextual Explanations using Linear Model Trees
    • [cs.LG]Achieving Adversarial Robustness via Sparsity
    • [cs.LG]Adversarial score matching and improved sampling for image generation
    • [cs.LG]CatGCN: Graph Convolutional Networks with Categorical Node Features
    • [cs.LG]CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets
    • [cs.LG]DART: Data Addition and Removal Trees
    • [cs.LG]Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting
    • [cs.LG]Defending Against Multiple and Unforeseen Adversarial Videos
    • [cs.LG]Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification
    • [cs.LG]Extending Label Smoothing Regularization with Self-Knowledge Distillation
    • [cs.LG]GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation
    • [cs.LG]Learning Product Rankings Robust to Fake Users
    • [cs.LG]Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA
    • [cs.LG]TREX: Tree-Ensemble Representer-Point Explanations
    • [cs.LG]Towards Interpretable Multi-Task Learning Using Bilevel Programming
    • [cs.LG]Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
    • [cs.LG]Visual Neural Decomposition to Explain Multivariate Data Sets
    • [cs.NE]Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
    • [cs.NI]Graph Neural Network based Service Function Chaining for Automatic Network Control
    • [cs.RO]A Toolkit to Generate Social Navigation Datasets
    • [cs.RO]Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
    • [cs.RO]Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning
    • [cs.RO]Sentinel: An Onboard System for Intelligent Vehicles to Reduce Traffic Delay during Freeway Incidents
    • [cs.RO]The Robotic Vision Scene Understanding Challenge
    • [cs.RO]Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain
    • [cs.SD]SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
    • [cs.SE]The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs
    • [cs.SI]A deep-learning model for evaluating and predicting the impact of lockdown policies on COVID-19 cases
    • [cs.SI]Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
    • [cs.SI]CasGCN: Predicting future cascade growth based on information diffusion graph
    • [cs.SI]Sequential seeding in multilayer networks
    • [econ.EM]Inference for high-dimensional exchangeable arrays
    • [econ.GN]Object Recognition for Economic Development from Daytime Satellite Imagery
    • [eess.AS]On Multitask Loss Function for Audio Event Detection and Localization
    • [eess.AS]RECOApy: Data recording, pre-processing and phonetic transcription for end-to-end speech-based applications
    • [eess.IV]Boosting the Sliding Frank-Wolfe solver for 3D deconvolution
    • [eess.IV]Phase Sampling Profilometry
    • [eess.SP]Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
    • [eess.SP]Energy-Efficient Design of IRS-NOMA Networks
    • [eess.SP]Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
    • [eess.SP]Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
    • [math.ST]Bootstrap method for misspecified ergodic Lévy driven stochastic differential equation models
    • [math.ST]Estimation of all parameters in the reflected Orntein-Uhlenbeck process from discrete observations
    • [math.ST]Families of discrete circular distributions with some novel applications
    • [math.ST]On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms
    • [physics.comp-ph]Symplectic Gaussian Process Regression of Hamiltonian Flow Maps
    • [physics.soc-ph]Community detection in networks using graph embeddings
    • [physics.soc-ph]Information flow in political elections: a stochastic perspective
    • [stat.AP]A Bayesian hierarchical model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images
    • [stat.AP]A Study on the Possible Effects of the Implementation of the Nordic Model in India on Crime Rates and Sexually Transmitted Diseases
    • [stat.AP]Bayesian Beta-Binomial Prevalence Estimation Using an Imperfect Test
    • [stat.AP]Explaining the Decline of Child Mortality in 44 Developing Countries: A Bayesian Extension of Oaxaca Decomposition Methods
    • [stat.AP]Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization
    • [stat.AP]Modelling COVID-19 — I A dynamic SIR(D) with application to Indian data
    • [stat.AP]Portfolio Decisions and Brain Reactions via the CEAD method
    • [stat.AP]Power and sample size for cluster randomized and stepped wedge trials: Comparing estimates obtained by applying design effects or by direct estimation in GLMM
    • [stat.AP]Probabilistic and mean-field model of COVID-19 epidemics with user mobility and contact tracing
    • [stat.CO]Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes
    • [stat.ME]A Family of Mixture Models for Biclustering
    • [stat.ME]A simulation study of semiparametric estimation in copula models based on minimum Alpha-Divergence
    • [stat.ME]Directional quantile classifiers
    • [stat.ME]Finding Stable Groups of Cross-Correlated Features in Multi-View data
    • [stat.ME]Narrowest Significance Pursuit: inference for multiple change-points in linear models
    • [stat.ME]TCA and TLRA: A comparison on contingency tables and compositional data
    • [stat.ML]Bayesian Screening: Multi-test Bayesian Optimization Applied to in silico Material Screening
    • [stat.ML]Deducing neighborhoods of classes from a fitted model
    • [stat.ML]Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion

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    • [cs.AI]An Argumentation-based Approach for Identifying and Dealing with Incompatibilities among Procedural Goals
    Mariela Morveli-Espinoza, Juan Carlos Nieves, Ayslan Possebom, Josep Puyol-Gruart, Cesar Augusto Tacla
    http://arxiv.org/abs/2009.05186v1

    • [cs.AI]Counterfactual Explanations & Adversarial Examples — Common Grounds, Essential Differences, and Potential Transfers
    Timo Freiesleben
    http://arxiv.org/abs/2009.05487v1

    • [cs.AI]Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
    Pavel Surynek
    http://arxiv.org/abs/2009.05161v1

    • [cs.AI]Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
    Mehdi Mirza, Andrew Jaegle, Jonathan J. Hunt, Arthur Guez, Saran Tunyasuvunakool, Alistair Muldal, Théophane Weber, Peter Karkus, Sébastien Racanière, Lars Buesing, Timothy Lillicrap, Nicolas Heess
    http://arxiv.org/abs/2009.05524v1

    • [cs.AI]Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity
    Pedro Barrios, Davy Monticolo, Sahbi Sidhom
    http://arxiv.org/abs/2009.05282v1

    • [cs.AI]Systematic Generalization on gSCAN with Language Conditioned Embedding
    Tong Gao, Qi Huang, Raymond J. Mooney
    http://arxiv.org/abs/2009.05552v1

    • [cs.AR]Accelerating Recommender Systems via Hardware “scale-in”
    Suresh Krishna, Ravi Krishna
    http://arxiv.org/abs/2009.05230v1

    • [cs.AR]An Open-Source Platform for High-Performance Non-Coherent On-Chip Communication
    Andreas Kurth, Wolfgang Rönninger, Thomas Benz, Matheus Cavalcante, Fabian Schuiki, Florian Zaruba, Luca Benini
    http://arxiv.org/abs/2009.05334v1

    • [cs.CL]A Comparison of LSTM and BERT for Small Corpus
    Aysu Ezen-Can
    http://arxiv.org/abs/2009.05451v1

    • [cs.CL]Accelerating Real-Time Question Answering via Question Generation
    Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, Jingjing Liu
    http://arxiv.org/abs/2009.05167v1

    • [cs.CL]Deep Learning for Semantic Relations
    Vivi Nastase, Stan Szpakowicz
    http://arxiv.org/abs/2009.05426v1

    • [cs.CL]FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding
    Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, Jingjing Liu
    http://arxiv.org/abs/2009.05166v1

    • [cs.CL]IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding
    Bryan Wilie, Karissa Vincentio, Genta Indra Winata, Samuel Cahyawijaya, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti
    http://arxiv.org/abs/2009.05387v1

    • [cs.CL]RadLex Normalization in Radiology Reports
    Surabhi Datta, Jordan Godfrey-Stovall, Kirk Roberts
    http://arxiv.org/abs/2009.05128v1

    • [cs.CL]Rank over Class: The Untapped Potential of Ranking in Natural Language Processing
    Amir Atapour-Abarghouei, Stephen Bonner, Andrew Stephen McGough
    http://arxiv.org/abs/2009.05160v1

    • [cs.CL]Robust Neural Machine Translation: Modeling Orthographic and Interpunctual Variation
    Toms Bergmanis, Artūrs Stafanovičs, Mārcis Pinnis
    http://arxiv.org/abs/2009.05460v1

    • [cs.CL]Sparsifying Transformer Models with Differentiable Representation Pooling
    Michał Pietruszka, Łukasz Borchmann, Filip Graliński
    http://arxiv.org/abs/2009.05169v1

    • [cs.CL]UPB at SemEval-2020 Task 11: Propaganda Detection with Domain-Specific Trained BERT
    Andrei Paraschiv, Dumitru-Clementin Cercel, Mihai Dascalu
    http://arxiv.org/abs/2009.05289v1

    • [cs.CL]WOLI at SemEval-2020 Task 12: Arabic Offensive Language Identification on Different Twitter Datasets
    Yasser Otiefy, Ahmed Abdelmalek, Islam El Hosary
    http://arxiv.org/abs/2009.05456v1

    • [cs.CL]Weakly Supervised Content Selection for Improved Image Captioning
    Khyathi Raghavi Chandu, Piyush Sharma, Soravit Changpinyo, Ashish Thapliyal, Radu Soricut
    http://arxiv.org/abs/2009.05175v1

    • [cs.CR]A Smart Home System based on Internet of Things
    Rihab Fahd Al-Mutawa, Fathy Albouraey Eassa
    http://arxiv.org/abs/2009.05328v1

    • [cs.CR]Accelerating 2PC-based ML with Limited Trusted Hardware
    Muqsit Nawaz, Aditya Gulati, Kunlong Liu, Vishwajeet Agrawal, Prabhanjan Ananth, Trinabh Gupta
    http://arxiv.org/abs/2009.05566v1

    • [cs.CR]Federated Model Distillation with Noise-Free Differential Privacy
    Lichao Sun, Lingjuan Lyu
    http://arxiv.org/abs/2009.05537v1

    • [cs.CR]Improving Robustness to Model Inversion Attacks via Mutual Information Regularization
    Tianhao Wang, Yuheng Zhang, Ruoxi Jia
    http://arxiv.org/abs/2009.05241v1

    • [cs.CV]A Density-Aware PointRCNN for 3D Objection Detection in Point Clouds
    Jie Li, Yu Hu
    http://arxiv.org/abs/2009.05307v1

    • [cs.CV]AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
    Shujaat Khan, Muhammad Usman
    http://arxiv.org/abs/2009.05277v1

    • [cs.CV]Adversarial Learning for Zero-shot Domain Adaptation
    Jinghua Wang, Jianmin Jiang
    http://arxiv.org/abs/2009.05214v1

    • [cs.CV]An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling
    Jinghua Wang, Jianmin Jiang
    http://arxiv.org/abs/2009.05234v1

    • [cs.CV]Attribute-conditioned Layout GAN for Automatic Graphic Design
    Jianan Li, Jimei Yang, Jianming Zhang, Christina Wang, Tingfa Xu
    http://arxiv.org/abs/2009.05284v1

    • [cs.CV]Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
    Jinghua Wang, Jianmin Jiang
    http://arxiv.org/abs/2009.05228v1

    • [cs.CV]Devil’s in the Detail: Graph-based Key-point Alignment and Embedding for Person Re-ID
    Xinyang Jiang, Fufu Yu, Yifei Gong, Shizhen Zhao, Xiaowei Guo, Feiyue Huang, Wei-Shi Zheng, Xing Sun
    http://arxiv.org/abs/2009.05250v1

    • [cs.CV]Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN
    August Lidfelt, Daniel Isaksson, Ludwig Hedlund, Simon Åberg, Markus Borg, Erik Larsson
    http://arxiv.org/abs/2009.05300v1

    • [cs.CV]Evaluation of the Robustness of Visual SLAM Methods in Different Environments
    Joonas Lomps, Artjom Lind, Amnir Hadachi
    http://arxiv.org/abs/2009.05427v1

    • [cs.CV]Fairness Matters — A Data-Driven Framework Towards Fair and High Performing Facial Recognition Systems
    Yushi Cao, David Berend, Palina Tolmach, Moshe Levy, Guy Amit, Asaf Shabtai, Yuval Elovici, Yang Liu
    http://arxiv.org/abs/2009.05283v1

    • [cs.CV]HAA500: Human-Centric Atomic Action Dataset with Curated Videos
    Jihoon Chung, Cheng-hsin Wuu, Hsuan-ru Yang, Yu-Wing Tai, Chi-Keung Tang
    http://arxiv.org/abs/2009.05224v1

    • [cs.CV]Heterogeneous Domain Generalization via Domain Mixup
    Yufei Wang, Haoliang Li, Alex C. Kot
    http://arxiv.org/abs/2009.05448v1

    • [cs.CV]Hybrid Space Learning for Language-based Video Retrieval
    Jianfeng Dong, Xirong Li, Chaoxi Xu, Gang Yang, Xun Wang
    http://arxiv.org/abs/2009.05381v1

    • [cs.CV]Image Conditioned Keyframe-Based Video Summarization Using Object Detection
    Neeraj Baghel, Suresh C. Raikwar, Charul Bhatnagar
    http://arxiv.org/abs/2009.05269v1

    • [cs.CV]MRZ code extraction from visa and passport documents using convolutional neural networks
    Yichuan Liu, Hailey James, Otkrist Gupta, Dan Raviv
    http://arxiv.org/abs/2009.05489v1

    • [cs.CV]Meta Learning for Few-Shot One-class Classification
    Gabriel Dahia, Maurício Pamplona Segundo
    http://arxiv.org/abs/2009.05353v1

    • [cs.CV]Novel and Effective CNN-Based Binarization for Historically Degraded As-built Drawing Maps
    Kuo-Liang Chung, De-Wei Hsieh
    http://arxiv.org/abs/2009.05252v1

    • [cs.CV]Optimizing Convolutional Neural Network Architecture via Information Field
    Yuke Wang, Boyuan Feng, Xueqiao Peng, Yufei Ding
    http://arxiv.org/abs/2009.05236v1

    • [cs.CV]PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans
    Weihua Liu, Xiabi Liua, Xiongbiao Luo, Murong Wang, Guangyuan Zheng, Guanghui Han
    http://arxiv.org/abs/2009.05267v1

    • [cs.CV]SoFAr: Shortcut-based Fractal Architectures for Binary Convolutional Neural Networks
    Zhu Baozhou, Peter Hofstee, Jinho Lee, Zaid Al-Ars
    http://arxiv.org/abs/2009.05317v1

    • [cs.CV]Spectral Analysis Network for Deep Representation Learning and Image Clustering
    Jinghua Wang, Adrian Hilton, Jianmin Jiang
    http://arxiv.org/abs/2009.05235v1

    • [cs.CV]TP-LSD: Tri-Points Based Line Segment Detector
    Siyu Huang, Fangbo Qin, Pengfei Xiong, Ning Ding, Yijia He, Xiao Liu
    http://arxiv.org/abs/2009.05505v1

    • [cs.CV]The PREVENTION Challenge: How Good Are Humans Predicting Lane Changes?
    A. Quintanar, R. Izquierdo, I. Parra, D. Fernández-Llorca, M. A. Sotelo
    http://arxiv.org/abs/2009.05331v1

    • [cs.CV]Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
    Xiang Li, Lingjing Wang, Yi Fang
    http://arxiv.org/abs/2009.05290v1

    • [cs.CV]Variance Loss: A Confidence-Based Reweighting Strategy for Coarse Semantic Segmentation
    Jingchao Liu, Ye Du, Qingjie Liu, Yunhong Wang
    http://arxiv.org/abs/2009.05205v1

    • [cs.CY]“Is it a Qoincidence?”: A First Step Towards Understanding and Characterizing the QAnon Movement on Voat.co
    Antonis Papasavva, Jeremy Blackburn, Gianluca Stringhini, Savvas Zannettou, Emiliano De Cristofaro
    http://arxiv.org/abs/2009.04885v2

    • [cs.CY]A Review on Security and Privacy of Internet of Medical Things
    Mohan Krishna Kagita, Navod Thilakarathne, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta
    http://arxiv.org/abs/2009.05394v1

    • [cs.CY]Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discourse
    Ancil Crayton, João Fonseca, Kanav Mehra, Michelle Ng, Jared Ross, Marcelo Sandoval-Castañeda, Rachel von Gnechten
    http://arxiv.org/abs/2009.05560v1

    • [cs.CY]On the Fairness of ‘Fake’ Data in Legal AI
    Lauren Boswell, Arjun Prakash
    http://arxiv.org/abs/2009.04640v2

    • [cs.DC]CASH: A Credit Aware Scheduling for Public Cloud Platforms
    Aakash Sharma, Saravanan Dhakshinamurthy, George Kesidis, Chita R. Das
    http://arxiv.org/abs/2009.04561v2

    • [cs.DC]Hierarchical Roofline Performance Analysis for Deep Learning Applications
    Yunsong Wang, Charlene Yang, Steven Farrell, Thorsten Kurth, Samuel Williams
    http://arxiv.org/abs/2009.05257v1

    • [cs.DC]Kvik: A task based middleware with composable scheduling policies
    Saurabh Raje, Frédéric Wagner
    http://arxiv.org/abs/2009.05504v1

    • [cs.HC]Visually Analyzing and Steering Zero Shot Learning
    Saroj Sahoo, Matthew Berger
    http://arxiv.org/abs/2009.05254v1

    • [cs.IR]Patient Cohort Retrieval using Transformer Language Models
    Sarvesh Soni, Kirk Roberts
    http://arxiv.org/abs/2009.05121v1

    • [cs.IR]TRec: Sequential Recommender Based On Latent Item Trend Information
    Ye Tao, Can Wang, Lina Yao, Weimin Li, Yonghong Yu
    http://arxiv.org/abs/2009.05183v1

    • [cs.IT]An Improved Multi-access Coded Caching with Uncoded Placement
    Shanuja Sasi, B. Sundar Rajan
    http://arxiv.org/abs/2009.05377v1

    • [cs.IT]Capacity-Approaching Autoencoders for Communications
    Nunzio A. Letizia, Andrea M. Tonello
    http://arxiv.org/abs/2009.05273v1

    • [cs.IT]Construction of Hyperbolic Signal Sets from the Uniformization of Hyperelliptic Curves
    Erika Patricia Dantas de Oliveira Guazzi, Reginaldo Palazzo Junior
    http://arxiv.org/abs/2009.05563v1

    • [cs.IT]Efficient Detectors for Telegram Splitting based Transmission in Low Power Wide Area Networks with Bursty Interference
    Steven Kisseleff, Jakob Kneissl, Gerd Kilian, Wolfgang H. Gerstacker
    http://arxiv.org/abs/2009.05072v1

    • [cs.IT]End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
    Fayçal Ait Aoudia, Jakob Hoydis
    http://arxiv.org/abs/2009.05261v1

    • [cs.IT]Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems
    Oscar Castañeda, Sven Jacobsson, Giuseppe Durisi, Tom Goldstein, Christoph Studer
    http://arxiv.org/abs/2009.05133v1

    • [cs.IT]Generalized Energy Detection Under Generalized Noise Channels
    Nikolaos I. Miridakis, Theodoros A. Tsiftsis, Guanghua Yang
    http://arxiv.org/abs/2009.04564v2

    • [cs.LG]A First Step Towards Distribution Invariant Regression Metrics
    Mario Michael Krell, Bilal Wehbe
    http://arxiv.org/abs/2009.05176v1

    • [cs.LG]A kernel function for Signal Temporal Logic formulae
    Luca Bortolussi, Giuseppe Maria Gallo, Laura Nenzi
    http://arxiv.org/abs/2009.05484v1

    • [cs.LG]Accurate and Intuitive Contextual Explanations using Linear Model Trees
    Aditya Lahiri, Narayanan Unny Edakunni
    http://arxiv.org/abs/2009.05322v1

    • [cs.LG]Achieving Adversarial Robustness via Sparsity
    Shufan Wang, Ningyi Liao, Liyao Xiang, Nanyang Ye, Quanshi Zhang
    http://arxiv.org/abs/2009.05423v1

    • [cs.LG]Adversarial score matching and improved sampling for image generation
    Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, Ioannis Mitliagkas
    http://arxiv.org/abs/2009.05475v1

    • [cs.LG]CatGCN: Graph Convolutional Networks with Categorical Node Features
    Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling, Yongdong Zhang
    http://arxiv.org/abs/2009.05303v1

    • [cs.LG]CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets
    Daniel Nemirovsky, Nicolas Thiebaut, Ye Xu, Abhishek Gupta
    http://arxiv.org/abs/2009.05199v1

    • [cs.LG]DART: Data Addition and Removal Trees
    Jonathan Brophy, Daniel Lowd
    http://arxiv.org/abs/2009.05567v1

    • [cs.LG]Deep Switching Auto-Regressive Factorization:Application to Time Series Forecasting
    Amirreza Farnoosh, Bahar Azari, Sarah Ostadabbas
    http://arxiv.org/abs/2009.05135v1

    • [cs.LG]Defending Against Multiple and Unforeseen Adversarial Videos
    Shao-Yuan Lo, Vishal M. Patel
    http://arxiv.org/abs/2009.05244v1

    • [cs.LG]Disentangling Neural Architectures and Weights: A Case Study in Supervised Classification
    Nicolo Colombo, Yang Gao
    http://arxiv.org/abs/2009.05346v1

    • [cs.LG]Extending Label Smoothing Regularization with Self-Knowledge Distillation
    Ji-Yue Wang, Pei Zhang, Wen-feng Pang, Jie Li
    http://arxiv.org/abs/2009.05226v1

    • [cs.LG]GTEA: Representation Learning for Temporal Interaction Graphs via Edge Aggregation
    Yiming Li, Da Sun Handason Tam, Siyue Xie, Xiaxin Liu, Qiu Fang Ying, Wing Cheong Lau, Dah Ming Chiu, Shou Zhi Chen
    http://arxiv.org/abs/2009.05266v1

    • [cs.LG]Learning Product Rankings Robust to Fake Users
    Negin Golrezaei, Vahideh Manshadi, Jon Schneider, Shreyas Sekar
    http://arxiv.org/abs/2009.05138v1

    • [cs.LG]Machine Learning and Data Science approach towards trend and predictors analysis of CDC Mortality Data for the USA
    Yasir Nadeem, Awais Ahmed
    http://arxiv.org/abs/2009.05400v1

    • [cs.LG]TREX: Tree-Ensemble Representer-Point Explanations
    Jonathan Brophy, Daniel Lowd
    http://arxiv.org/abs/2009.05530v1

    • [cs.LG]Towards Interpretable Multi-Task Learning Using Bilevel Programming
    Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin
    http://arxiv.org/abs/2009.05483v1

    • [cs.LG]Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization
    Qi Zhu, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han, Carl Yang
    http://arxiv.org/abs/2009.05204v1

    • [cs.LG]Visual Neural Decomposition to Explain Multivariate Data Sets
    Johannes Knittel, Andres Lalama, Steffen Koch, Thomas Ertl
    http://arxiv.org/abs/2009.05502v1

    • [cs.NE]Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
    Beren Millidge, Alexander Tschantz, Christopher L Buckley, Anil Seth
    http://arxiv.org/abs/2009.05359v1

    • [cs.NI]Graph Neural Network based Service Function Chaining for Automatic Network Control
    DongNyeong Heo, Stanislav Lange, Hee-Gon Kim, Heeyoul Choi
    http://arxiv.org/abs/2009.05240v1

    • [cs.RO]A Toolkit to Generate Social Navigation Datasets
    Rishabh Baghel, Aditya Kapoor, Pilar Bachiller, Ronit R. Jorvekar, Daniel Rodriguez-Criado, Luis J. Manso
    http://arxiv.org/abs/2009.05345v1

    • [cs.RO]Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
    Steven D. Morad, Roberto Mecca, Rudra P. K. Poudel, Stephan Liwicki, Roberto Cipolla
    http://arxiv.org/abs/2009.05429v1

    • [cs.RO]Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning
    Lucas Manuelli, Yunzhu Li, Pete Florence, Russ Tedrake
    http://arxiv.org/abs/2009.05085v1

    • [cs.RO]Sentinel: An Onboard System for Intelligent Vehicles to Reduce Traffic Delay during Freeway Incidents
    Goodarz Mehr, Azim Eskandarian
    http://arxiv.org/abs/2009.05165v1

    • [cs.RO]The Robotic Vision Scene Understanding Challenge
    David Hall, Ben Talbot, Suman Raj Bista, Haoyang Zhang, Rohan Smith, Feras Dayoub, Niko Sünderhauf
    http://arxiv.org/abs/2009.05246v1

    • [cs.RO]Towards Safe Locomotion Navigation in Partially Observable Environments with Uneven Terrain
    Jonas Warnke, Abdulaziz Shamsah, Yingke Li, Ye Zhao
    http://arxiv.org/abs/2009.05168v1

    • [cs.SD]SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context
    Mark Cartwright, Jason Cramer, Ana Elisa Mendez Mendez, Yu Wang, Ho-Hsiang Wu, Vincent Lostanlen, Magdalena Fuentes, Graham Dove, Charlie Mydlarz, Justin Salamon, Oded Nov, Juan Pablo Bello
    http://arxiv.org/abs/2009.05188v1

    • [cs.SE]The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs
    Markus Borg
    http://arxiv.org/abs/2009.05260v1

    • [cs.SI]A deep-learning model for evaluating and predicting the impact of lockdown policies on COVID-19 cases
    Ahmed Ben Said, Abdelkarim Erradi, Hussein Aly, Abdelmonem Mohamed
    http://arxiv.org/abs/2009.05481v1

    • [cs.SI]Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
    Aravind Sankar, Junting Wang, Adit Krishnan, Hari Sundaram
    http://arxiv.org/abs/2009.05197v1

    • [cs.SI]CasGCN: Predicting future cascade growth based on information diffusion graph
    Zhixuan Xu, Minghui Qian, Xiaowei Huang, Jie Meng
    http://arxiv.org/abs/2009.05152v1

    • [cs.SI]Sequential seeding in multilayer networks
    Piotr Bródka, Jarosław Jankowski, Radosław Michalski
    http://arxiv.org/abs/2009.05335v1

    • [econ.EM]Inference for high-dimensional exchangeable arrays
    Harold D. Chiang, Kengo Kato, Yuya Sasaki
    http://arxiv.org/abs/2009.05150v1

    • [econ.GN]Object Recognition for Economic Development from Daytime Satellite Imagery
    Klaus Ackermann, Alexey Chernikov, Nandini Anantharama, Miethy Zaman, Paul A Raschky
    http://arxiv.org/abs/2009.05455v1

    • [eess.AS]On Multitask Loss Function for Audio Event Detection and Localization
    Huy Phan, Lam Pham, Philipp Koch, Ngoc Q. K. Duong, Ian McLoughlin, Alfred Mertins
    http://arxiv.org/abs/2009.05527v1

    • [eess.AS]RECOApy: Data recording, pre-processing and phonetic transcription for end-to-end speech-based applications
    Adriana Stan
    http://arxiv.org/abs/2009.05493v1

    • [eess.IV]Boosting the Sliding Frank-Wolfe solver for 3D deconvolution
    Jean-Baptiste Courbot, Bruno Colicchio
    http://arxiv.org/abs/2009.05473v1

    • [eess.IV]Phase Sampling Profilometry
    Zhenzhou Wang
    http://arxiv.org/abs/2009.05406v1

    • [eess.SP]Deep Transfer Learning for Signal Detection in Ambient Backscatter Communications
    Chang Liu, Zhiqiang Wei, Derrick Wing Kwan Ng, Jinhong Yuan, Ying-Chang Liang
    http://arxiv.org/abs/2009.05231v1

    • [eess.SP]Energy-Efficient Design of IRS-NOMA Networks
    Fang Fang, Yanqing Xu, Quoc-Viet Pham, Zhiguo Ding
    http://arxiv.org/abs/2009.05344v1

    • [eess.SP]Machine learning-based EDFA Gain Model Generalizable to Multiple Physical Devices
    Francesco Da Ros, Uiara Celine de Moura, Metodi P. Yankov
    http://arxiv.org/abs/2009.05326v1

    • [eess.SP]Power Evolution Prediction and Optimization in a Multi-span System Based on Component-wise System Modeling
    Metodi P. Yankov, Uiara Celine de Moura, Francesco Da Ros
    http://arxiv.org/abs/2009.05348v1

    • [math.ST]Bootstrap method for misspecified ergodic Lévy driven stochastic differential equation models
    Yuma Uehara
    http://arxiv.org/abs/2009.05232v1

    • [math.ST]Estimation of all parameters in the reflected Orntein-Uhlenbeck process from discrete observations
    Yaozhong Hu, Yuejuan Xi
    http://arxiv.org/abs/2009.05162v1

    • [math.ST]Families of discrete circular distributions with some novel applications
    Kanti V. Mardia, Karthik Sriram
    http://arxiv.org/abs/2009.05437v1

    • [math.ST]On polynomial-time computation of high-dimensional posterior measures by Langevin-type algorithms
    Richard Nickl, Sven Wang
    http://arxiv.org/abs/2009.05298v1

    • [physics.comp-ph]Symplectic Gaussian Process Regression of Hamiltonian Flow Maps
    Katharina Rath, Christopher G. Albert, Bernd Bischl, Udo von Toussaint
    http://arxiv.org/abs/2009.05569v1

    • [physics.soc-ph]Community detection in networks using graph embeddings
    Aditya Tandon, Aiiad Albeshri, Vijey Thayananthan, Wadee Alhalabi, Filippo Radicchi, Santo Fortunato
    http://arxiv.org/abs/2009.05265v1

    • [physics.soc-ph]Information flow in political elections: a stochastic perspective
    Santosh Kumar Radha
    http://arxiv.org/abs/2009.05198v1

    • [stat.AP]A Bayesian hierarchical model to estimate land surface phenology parameters with harmonized Landsat 8 and Sentinel-2 images
    Chad Babcock, Andrew O. Finley, Nathaniel Looker
    http://arxiv.org/abs/2009.05203v1

    • [stat.AP]A Study on the Possible Effects of the Implementation of the Nordic Model in India on Crime Rates and Sexually Transmitted Diseases
    Sabarinath Vinod Nair, Shreya Sharma, Swarnava Ghosh
    http://arxiv.org/abs/2009.05319v1

    • [stat.AP]Bayesian Beta-Binomial Prevalence Estimation Using an Imperfect Test
    Jonathan Baxter
    http://arxiv.org/abs/2009.05446v1

    • [stat.AP]Explaining the Decline of Child Mortality in 44 Developing Countries: A Bayesian Extension of Oaxaca Decomposition Methods
    Antonio P. Ramos, Martin J. Flores, Leiwen Gao, Patrick Heuveline, Robert E. Weiss
    http://arxiv.org/abs/2009.05417v1

    • [stat.AP]Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization
    Peng Liu, Ying Chen, Chung-Piaw Teo
    http://arxiv.org/abs/2009.05422v1

    • [stat.AP]Modelling COVID-19 — I A dynamic SIR(D) with application to Indian data
    Madhuchhanda Bhattacharjee, Arup Bose
    http://arxiv.org/abs/2009.05044v1

    • [stat.AP]Portfolio Decisions and Brain Reactions via the CEAD method
    Piotr Majer, Peter N. C. Mohr, Hauke R. Heekeren, Wolfgang K. Härdle
    http://arxiv.org/abs/2009.05142v1

    • [stat.AP]Power and sample size for cluster randomized and stepped wedge trials: Comparing estimates obtained by applying design effects or by direct estimation in GLMM
    David M. Thompson
    http://arxiv.org/abs/2009.05171v1

    • [stat.AP]Probabilistic and mean-field model of COVID-19 epidemics with user mobility and contact tracing
    M. Akian, L. Ganassali, S. Gaubert, L. Massoulié
    http://arxiv.org/abs/2009.05304v1

    • [stat.CO]Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes
    Andrew Golightly, Chris Sherlock
    http://arxiv.org/abs/2009.05318v1

    • [stat.ME]A Family of Mixture Models for Biclustering
    Wangshu Tu, Sanjeena Subedi
    http://arxiv.org/abs/2009.05098v1

    • [stat.ME]A simulation study of semiparametric estimation in copula models based on minimum Alpha-Divergence
    Morteza Mohammadi, Mohammad Amini, Mahdi Emadi
    http://arxiv.org/abs/2009.05247v1

    • [stat.ME]Directional quantile classifiers
    Alessio Farcomeni, Marco Geraci, Cinzia Viroli
    http://arxiv.org/abs/2009.05007v2

    • [stat.ME]Finding Stable Groups of Cross-Correlated Features in Multi-View data
    Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew Nobel
    http://arxiv.org/abs/2009.05079v1

    • [stat.ME]Narrowest Significance Pursuit: inference for multiple change-points in linear models
    Piotr Fryzlewicz
    http://arxiv.org/abs/2009.05431v1

    • [stat.ME]TCA and TLRA: A comparison on contingency tables and compositional data
    J. Allard, S. Champigny, V. Choulakian, S. Mahdi
    http://arxiv.org/abs/2009.05482v1

    • [stat.ML]Bayesian Screening: Multi-test Bayesian Optimization Applied to in silico Material Screening
    James Hook, Calum Hand, Emma Whitfield
    http://arxiv.org/abs/2009.05418v1

    • [stat.ML]Deducing neighborhoods of classes from a fitted model
    Alexander Gerharz, Andreas Groll, Gunther Schauberger
    http://arxiv.org/abs/2009.05516v1

    • [stat.ML]Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion
    Divish Rengasamy, Benjamin Rothwell, Grazziela Figueredo
    http://arxiv.org/abs/2009.05501v1