cond-mat.str-el - 强关联电子系统

    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.GR - 计算机图形学 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MA - 多代理系统 cs.RO - 机器人学 cs.SE - 软件工程 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.OC - 优化与控制 math.ST - 统计理论 physics.chem-ph -化学物理 physics.geo-ph - 地球物理学 physics.soc-ph - 物理学与社会 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cond-mat.str-el]Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
    • [cs.AI]A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
    • [cs.AI]KompaRe: A Knowledge Graph Comparative Reasoning System
    • [cs.AR]ReFloat: Low-Cost Floating-Point Processing in ReRAM
    • [cs.CL]Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
    • [cs.CL]Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
    • [cs.CL]An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish
    • [cs.CL]Answer Span Correction in Machine Reading Comprehension
    • [cs.CL]Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora
    • [cs.CL]EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
    • [cs.CL]Fighting an Infodemic: COVID-19 Fake News Dataset
    • [cs.CL]From Dataset Recycling to Multi-Property Extraction and Beyond
    • [cs.CL]Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
    • [cs.CL]OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
    • [cs.CL]Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons
    • [cs.CL]Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation
    • [cs.CL]The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation
    • [cs.CL]Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English
    • [cs.CL]Unleashing the Power of Neural Discourse Parsers — A Context and Structure Aware Approach Using Large Scale Pretraining
    • [cs.CL]What’s New? Summarizing Contributions in Scientific Literature
    • [cs.CR]Federated Crowdsensing: Framework and Challenges
    • [cs.CR]Towards Obfuscated Malware Detection for Low Powered IoT Devices
    • [cs.CV]”What’s This?” — Learning to Segment Unknown Objects from Manipulation Sequences
    • [cs.CV]Affinity LCFCN: Learning to Segment Fish with Weak Supervision
    • [cs.CV]Can Human Sex Be Learned Using Only 2D Body Keypoint Estimations?
    • [cs.CV]Channel Pruning via Multi-Criteria based on Weight Dependency
    • [cs.CV]Confusable Learning for Large-class Few-Shot Classification
    • [cs.CV]Deep Cross-modal Proxy Hashing
    • [cs.CV]Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
    • [cs.CV]Domain Adaptive Person Re-Identification via Coupling Optimization
    • [cs.CV]Efficient image retrieval using multi neural hash codes and bloom filters
    • [cs.CV]Ellipse Loss for Scene-Compliant Motion Prediction
    • [cs.CV]Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition
    • [cs.CV]GHFP: Gradually Hard Filter Pruning
    • [cs.CV]Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery
    • [cs.CV]Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
    • [cs.CV]Large-scale multilingual audio visual dubbing
    • [cs.CV]Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption
    • [cs.CV]Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss
    • [cs.CV]Learning to Orient Surfaces by Self-supervised Spherical CNNs
    • [cs.CV]Self Supervised Learning for Object Localisation in 3D Tomographic Images
    • [cs.CV]Smart Time-Multiplexing of Quads Solves the Multicamera Interference Problem
    • [cs.CV]Towards Efficient Scene Understanding via Squeeze Reasoning
    • [cs.CV]ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
    • [cs.CV]Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
    • [cs.CY]Artificial Intelligence and its impact on the Fourth Industrial Revolution: A Review
    • [cs.CY]Behavioral Use Licensing for Responsible AI
    • [cs.DC]Power-Aware Run-Time Scheduler for Mixed-Criticality Systems on Multi-Core Platform
    • [cs.DC]Sandslash: A Two-Level Framework for Efficient Graph Pattern Mining
    • [cs.DC]Task-Graph Scheduling Extensions for Efficient Synchronization and Communication
    • [cs.DC]Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data
    • [cs.GR]Modular Primitives for High-Performance Differentiable Rendering
    • [cs.HC]Explaining Differences in Classes of Discrete Sequences
    • [cs.IR]PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data
    • [cs.IR]Session-aware Recommendation: A Surprising Quest for the State-of-the-art
    • [cs.IT]5G Embraces Satellites for 6G Ubiquitous IoT: Basic Models for Integrated Satellite Terrestrial Networks
    • [cs.IT]Hybrid Multicast/Unicast Design in NOMA-based Vehicular Caching System
    • [cs.IT]Joint Sensing and Communication over Memoryless Broadcast Channels
    • [cs.IT]On Systematic Polarization-Adjusted Convolutional (PAC) Codes
    • [cs.IT]Secure Performance Analysis and Optimization for FD-NOMA Vehicular Communications
    • [cs.IT]Transforming Fading Channel From Fast to Slow: IRS-Assisted High-Mobility Communication
    • [cs.IT]User-Centric Secure Cell Formation for Visible Light Networks with Statistical Delay Guarantees
    • [cs.LG]A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
    • [cs.LG]ASFGNN: Automated Separated-Federated Graph Neural Network
    • [cs.LG]Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
    • [cs.LG]Communication-efficient Decentralized Local SGD over Undirected Networks
    • [cs.LG]Complex Query Answering with Neural Link Predictors
    • [cs.LG]Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation
    • [cs.LG]Deep coastal sea elements forecasting using U-Net based models
    • [cs.LG]Deep learning architectures for inference of AC-OPF solutions
    • [cs.LG]Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
    • [cs.LG]Explainable AI meets Healthcare: A Study on Heart Disease Dataset
    • [cs.LG]FDNAS: Improving Data Privacy and Model Diversity in AutoML
    • [cs.LG]FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks
    • [cs.LG]Identifying and interpreting tuning dimensions in deep networks
    • [cs.LG]Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
    • [cs.LG]Learning Online Data Association
    • [cs.LG]Learning with Molecules beyond Graph Neural Networks
    • [cs.LG]Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
    • [cs.LG]Massively Parallel Graph Drawing and Representation Learning
    • [cs.LG]Measuring Data Collection Quality for Community Healthcare
    • [cs.LG]Noise2Sim — Similarity-based Self-Learning for Image Denoising
    • [cs.LG]Parametric Flatten-T Swish: An Adaptive Non-linear Activation Function For Deep Learning
    • [cs.LG]Predicting special care during the COVID-19 pandemic: A machine learning approach
    • [cs.LG]Resource-Constrained Federated Learning with Heterogeneous Labels and Models
    • [cs.LG]Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
    • [cs.LG]Sequential Minimal Optimization for One-Class Slab Support Vector Machine
    • [cs.LG]The Value Equivalence Principle for Model-Based Reinforcement Learning
    • [cs.LG]Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
    • [cs.LG]Underspecification Presents Challenges for Credibility in Modern Machine Learning
    • [cs.MA]Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
    • [cs.RO]Accelerating combinatorial filter reduction through constraints
    • [cs.RO]Adversarial Skill Learning for Robust Manipulation
    • [cs.RO]Do We Need to Compensate for Motion Distortion and Doppler Effects in Radar-Based Navigation?
    • [cs.RO]Gamma Radiation Source Localization for Micro Aerial Vehicles with a Miniature Single-Detector Compton Event Camera
    • [cs.RO]Generative adversarial training of product of policies for robust and adaptive movement primitives
    • [cs.RO]HAVEN: A Unity-based Virtual Robot Environment to Showcase HRI-based Augmented Reality
    • [cs.RO]Learning Behavior Trees with Genetic Programming in Unpredictable Environments
    • [cs.RO]Occlusion-Aware Search for Object Retrieval in Clutter
    • [cs.RO]Optimization-based Trajectory Planning for Tethered Marsupial Robots
    • [cs.RO]RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
    • [cs.RO]Sample-efficient Reinforcement Learning in Robotic Table Tennis
    • [cs.RO]Task-relevant Representation Learning for Networked Robotic Perception
    • [cs.SE]Analyzing the Productivity of GitHub Teams based on Formation Phase Activity
    • [econ.EM]Conditional quantile estimators: A small sample theory
    • [econ.GN]Did Hurricane Katrina Reduce Mortality?
    • [eess.AS]Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement
    • [eess.IV]A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods
    • [eess.IV]Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network
    • [eess.SP]Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines
    • [math.OC]Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
    • [math.OC]Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19
    • [math.ST]Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
    • [math.ST]Statistical analysis of Wasserstein GANs with applications to time series forecasting
    • [physics.chem-ph]Physic-informed Neural-Network Software for Molecular Dynamics Applications
    • physics.geo-phReal-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network
    • [physics.soc-ph]COVID-19 Plateau: A Phenomenon of Epidemic Development under Adaptive Prevention Strategies
    • [stat.ME]A Bayesian Functional Data Model for Surveys Collected under Informative Sampling with Application to Mortality Estimation using NHANES
    • [stat.ME]A Novel Statistical Test for Treatment Differences in Clinical Trials using a Response Adaptive Forward Looking Gittins Index Rule
    • [stat.ME]Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions
    • [stat.ME]Causal Imputation via Synthetic Interventions
    • [stat.ME]Detecting spatial clusters on functional data: a parametric scan statistic approach
    • [stat.ME]Hidden Markov Pólya trees for high-dimensional distributions
    • [stat.ME]Methods to Compute Prediction Intervals: A Review and New Results
    • [stat.ME]Prediction of Future Failures for Heterogeneous Reliability Field Data
    • [stat.ME]Semi-Markov Survival Regression Models
    • [stat.ML]Multi-output Gaussian Process Modulated Poisson Processes for Event Prediction
    • [stat.ML]On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
    • [stat.ML]There is no trade-off: enforcing fairness can improve accuracy
    • [stat.ML]User-Dependent Neural Sequence Models for Continuous-Time Event Data

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    • [cond-mat.str-el]Correlator Convolutional Neural Networks: An Interpretable Architecture for Image-like Quantum Matter Data
    Cole Miles, Annabelle Bohrdt, Ruihan Wu, Christie Chiu, Muqing Xu, Geoffrey Ji, Markus Greiner, Kilian Q. Weinberger, Eugene Demler, Eun-Ah Kim
    http://arxiv.org/abs/2011.03474v1

    • [cs.AI]A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
    Hao Nie, Qin Zhang
    http://arxiv.org/abs/2011.03359v1

    • [cs.AI]KompaRe: A Knowledge Graph Comparative Reasoning System
    Lihui Liu, Boxin Du, Heng Ji, Hanghang Tong
    http://arxiv.org/abs/2011.03189v1

    • [cs.AR]ReFloat: Low-Cost Floating-Point Processing in ReRAM
    Linghao Song, Fan Chen, Xuehai Qian, Hai Li, Yiran Chen
    http://arxiv.org/abs/2011.03190v1

    • [cs.CL]Alquist 2.0: Alexa Prize Socialbot Based on Sub-Dialogue Models
    Jan Pichl, Petr Marek, Jakub Konrád, Martin Matulík, Jan Šedivý
    http://arxiv.org/abs/2011.03259v1

    • [cs.CL]Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
    Jan Pichl, Petr Marek, Jakub Konrád, Petr Lorenc, Van Duy Ta, Jan Šedivý
    http://arxiv.org/abs/2011.03261v1

    • [cs.CL]An Unsupervised method for OCR Post-Correction and Spelling Normalisation for Finnish
    Quan Duong, Mika Hämäläinen, Simon Hengchen
    http://arxiv.org/abs/2011.03502v1

    • [cs.CL]Answer Span Correction in Machine Reading Comprehension
    Revanth Gangi Reddy, Md Arafat Sultan, Efsun Sarioglu Kayi, Rong Zhang, Vittorio Castelli, Avirup Sil
    http://arxiv.org/abs/2011.03435v1

    • [cs.CL]Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora
    Tosin P. Adewumi, Foteini Liwicki, Marcus Liwicki
    http://arxiv.org/abs/2011.03281v1

    • [cs.CL]EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering
    Momchil Hardalov, Todor Mihaylov, Dimitrina Zlatkova, Yoan Dinkov, Ivan Koychev, Preslav Nakov
    http://arxiv.org/abs/2011.03080v1

    • [cs.CL]Fighting an Infodemic: COVID-19 Fake News Dataset
    Parth Patwa, Shivam Sharma, Srinivas PYKL, Vineeth Guptha, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das, Tanmoy Chakraborty
    http://arxiv.org/abs/2011.03327v1

    • [cs.CL]From Dataset Recycling to Multi-Property Extraction and Beyond
    Tomasz Dwojak, Michał Pietruszka, Łukasz Borchmann, Jakub Chłędowski, Filip Graliński
    http://arxiv.org/abs/2011.03228v1

    • [cs.CL]Improving Machine Reading Comprehension with Single-choice Decision and Transfer Learning
    Yufan Jiang, Shuangzhi Wu, Jing Gong, Yahui Cheng, Peng Meng, Weiliang Lin, Zhibo Chen, Mu li
    http://arxiv.org/abs/2011.03292v1

    • [cs.CL]OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still rocks Semantic Change Detection
    Jens Kaiser, Dominik Schlechtweg, Sabine Schulte im Walde
    http://arxiv.org/abs/2011.03258v1

    • [cs.CL]Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons
    Saif M. Mohammad
    http://arxiv.org/abs/2011.03492v1

    • [cs.CL]Semi-Supervised Low-Resource Style Transfer of Indonesian Informal to Formal Language with Iterative Forward-Translation
    Haryo Akbarianto Wibowo, Tatag Aziz Prawiro, Muhammad Ihsan, Alham Fikri Aji, Radityo Eko Prasojo, Rahmad Mahendra
    http://arxiv.org/abs/2011.03286v1

    • [cs.CL]The ApposCorpus: A new multilingual, multi-domain dataset for factual appositive generation
    Yova Kementchedjhieva, Di Lu, Joel Tetreault
    http://arxiv.org/abs/2011.03287v1

    • [cs.CL]Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English
    Gongbo Tang, Rico Sennrich, Joakim Nivre
    http://arxiv.org/abs/2011.03469v1

    • [cs.CL]Unleashing the Power of Neural Discourse Parsers — A Context and Structure Aware Approach Using Large Scale Pretraining
    Grigorii Guz, Patrick Huber, Giuseppe Carenini
    http://arxiv.org/abs/2011.03203v1

    • [cs.CL]What’s New? Summarizing Contributions in Scientific Literature
    Hiroaki Hayashi, Wojciech Kryściński, Bryan McCann, Nazneen Rajani, Caiming Xiong
    http://arxiv.org/abs/2011.03161v1

    • [cs.CR]Federated Crowdsensing: Framework and Challenges
    Leye Wang, Han Yu, Xiao Han
    http://arxiv.org/abs/2011.03208v1

    • [cs.CR]Towards Obfuscated Malware Detection for Low Powered IoT Devices
    Daniel Park, Hannah Powers, Benji Prashker, Leland Liu, Bülent Yener
    http://arxiv.org/abs/2011.03476v1

    • [cs.CV]“What’s This?” — Learning to Segment Unknown Objects from Manipulation Sequences
    Wout Boerdijk, Martin Sundermeyer, Maximilian Durner, Rudolph Triebel
    http://arxiv.org/abs/2011.03279v1

    • [cs.CV]Affinity LCFCN: Learning to Segment Fish with Weak Supervision
    Issam Laradji, Alzayat Saleh, Pau Rodriguez, Derek Nowrouzezahrai, Mostafa Rahimi Azghadi, David Vazquez
    http://arxiv.org/abs/2011.03149v1

    • [cs.CV]Can Human Sex Be Learned Using Only 2D Body Keypoint Estimations?
    Kristijan Bartol, Tomislav Pribanic, David Bojanic, Tomislav Petkovic
    http://arxiv.org/abs/2011.03104v1

    • [cs.CV]Channel Pruning via Multi-Criteria based on Weight Dependency
    Yangchun Yan, Chao Li, Rongzuo Guo, Kang Yang, Yongjun Xu
    http://arxiv.org/abs/2011.03240v1

    • [cs.CV]Confusable Learning for Large-class Few-Shot Classification
    Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long
    http://arxiv.org/abs/2011.03154v1

    • [cs.CV]Deep Cross-modal Proxy Hashing
    Rong-Cheng Tu, Xian-Ling Mao, Rongxin Tu, Wei Wei, Heyan Huang
    http://arxiv.org/abs/2011.03451v1

    • [cs.CV]Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
    Mihir Prabhudesai, Shamit Lal, Darshan Patil, Hsiao-Yu Tung, Adam W Harley, Katerina Fragkiadaki
    http://arxiv.org/abs/2011.03367v1

    • [cs.CV]Domain Adaptive Person Re-Identification via Coupling Optimization
    Xiaobin Liu, Shiliang Zhang
    http://arxiv.org/abs/2011.03363v1

    • [cs.CV]Efficient image retrieval using multi neural hash codes and bloom filters
    Sourin Chakrabarti
    http://arxiv.org/abs/2011.03234v1

    • [cs.CV]Ellipse Loss for Scene-Compliant Motion Prediction
    Henggang Cui, Hoda Shajari, Sai Yalamanchi, Nemanja Djuric
    http://arxiv.org/abs/2011.03139v1

    • [cs.CV]Event-VPR: End-to-End Weakly Supervised Network Architecture for Event-based Visual Place Recognition
    Delei Kong, Zheng Fang, Haojia Li, Kuanxu Hou, Sonya Coleman, Dermot Kerr
    http://arxiv.org/abs/2011.03290v1

    • [cs.CV]GHFP: Gradually Hard Filter Pruning
    Linhang Cai, Zhulin An, Yongjun Xu
    http://arxiv.org/abs/2011.03170v1

    • [cs.CV]Hi-UCD: A Large-scale Dataset for Urban Semantic Change Detection in Remote Sensing Imagery
    Shiqi Tian, Yanfei Zhong, Ailong Ma, Zhuo Zheng
    http://arxiv.org/abs/2011.03247v1

    • [cs.CV]Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
    Steve Dias Da Cruz, Bertram Taetz, Thomas Stifter, Didier Stricker
    http://arxiv.org/abs/2011.03428v1

    • [cs.CV]Large-scale multilingual audio visual dubbing
    Yi Yang, Brendan Shillingford, Yannis Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, Nando de Freitas
    http://arxiv.org/abs/2011.03530v1

    • [cs.CV]Learning Rolling Shutter Correction from Real Data without Camera Motion Assumption
    Jiawei Mo, Md Jahidul Islam, Junaed Sattar
    http://arxiv.org/abs/2011.03106v1

    • [cs.CV]Learning a Geometric Representation for Data-Efficient Depth Estimation via Gradient Field and Contrastive Loss
    Dongseok Shim, H. Jin Kim
    http://arxiv.org/abs/2011.03207v1

    • [cs.CV]Learning to Orient Surfaces by Self-supervised Spherical CNNs
    Riccardo Spezialetti, Federico Stella, Marlon Marcon, Luciano Silva, Samuele Salti, Luigi Di Stefano
    http://arxiv.org/abs/2011.03298v1

    • [cs.CV]Self Supervised Learning for Object Localisation in 3D Tomographic Images
    Yaroslav Zharov, Alexey Ershov, Tilo Baumbach
    http://arxiv.org/abs/2011.03353v1

    • [cs.CV]Smart Time-Multiplexing of Quads Solves the Multicamera Interference Problem
    Tomislav Pribanic, Tomislav Petkovic, David Bojanic, Kristijan Bartol
    http://arxiv.org/abs/2011.03102v1

    • [cs.CV]Towards Efficient Scene Understanding via Squeeze Reasoning
    Xiangtai Li, Xia Li, Ansheng You, Li Zhang, Guangliang Cheng, Kuiyuan Yang, Yunhai Tong, Zhouchen Lin
    http://arxiv.org/abs/2011.03308v1

    • [cs.CV]ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras
    Hao Li, Huai Yu, Wen Yang, Lei Yu, Sebastian Scherer
    http://arxiv.org/abs/2011.03174v1

    • [cs.CV]Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
    Henggang Cui, Fang-Chieh Chou, Jake Charland, Carlos Vallespi-Gonzalez, Nemanja Djuric
    http://arxiv.org/abs/2011.03114v1

    • [cs.CY]Artificial Intelligence and its impact on the Fourth Industrial Revolution: A Review
    Gissel Velarde
    http://arxiv.org/abs/2011.03044v1

    • [cs.CY]Behavioral Use Licensing for Responsible AI
    Danish Contractor, Daniel McDuff, Julia Haines, Jenny Lee, Christopher Hines, Brent Hecht
    http://arxiv.org/abs/2011.03116v1

    • [cs.DC]Power-Aware Run-Time Scheduler for Mixed-Criticality Systems on Multi-Core Platform
    Behnaz Ranjbar, Tuan D. A. Nguyen, Alireza Ejlali, Akash Kumar
    http://arxiv.org/abs/2011.03262v1

    • [cs.DC]Sandslash: A Two-Level Framework for Efficient Graph Pattern Mining
    Xuhao Chen, Roshan Dathathri, Gurbinder Gill, Loc Hoang, Keshav Pingali
    http://arxiv.org/abs/2011.03135v1

    • [cs.DC]Task-Graph Scheduling Extensions for Efficient Synchronization and Communication
    Seonmyeong Bak, Oscar Hernandez, Mark Gates, Piotr Luszczek, Vivek Sarkar
    http://arxiv.org/abs/2011.03196v1

    • [cs.DC]Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data
    Rafael Vescovi, Hanyu Li, Jeffery Kinnison, Murat Keceli, Misha Salim, Narayanan Kasthuri, Thomas D. Uram, Nicola Ferrier
    http://arxiv.org/abs/2011.03204v1

    • [cs.GR]Modular Primitives for High-Performance Differentiable Rendering
    Samuli Laine, Tero Karras, Yeongho Seol, Jaakko Lehtinen, Timo Aila
    http://arxiv.org/abs/2011.03277v1

    • [cs.HC]Explaining Differences in Classes of Discrete Sequences
    Samaneh Saadat, Gita Sukthankar
    http://arxiv.org/abs/2011.03371v1

    • [cs.IR]PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data
    Alberto Calderone
    http://arxiv.org/abs/2011.03123v1

    • [cs.IR]Session-aware Recommendation: A Surprising Quest for the State-of-the-art
    Sara Latifi, Noemi Mauro, Dietmar Jannach
    http://arxiv.org/abs/2011.03424v1

    • [cs.IT]5G Embraces Satellites for 6G Ubiquitous IoT: Basic Models for Integrated Satellite Terrestrial Networks
    Xinran Fang, Te Wei, Wei Feng, Hongxin Wei, Yunfei Chen, Ning Ge, Cheng-Xiang Wang
    http://arxiv.org/abs/2011.03182v1

    • [cs.IT]Hybrid Multicast/Unicast Design in NOMA-based Vehicular Caching System
    Xinyue Pei, Yingyang Chen, Miaowen Wen, Gaojie Chen, Senior Member, IEEE, Hua Yu
    http://arxiv.org/abs/2011.03232v1

    • [cs.IT]Joint Sensing and Communication over Memoryless Broadcast Channels
    Mehrasa Ahmadipour, Michèle Wigger, Mari Kobayashi
    http://arxiv.org/abs/2011.03379v1

    • [cs.IT]On Systematic Polarization-Adjusted Convolutional (PAC) Codes
    Thibaud Tonnellier, Warren J. Gross
    http://arxiv.org/abs/2011.03177v1

    • [cs.IT]Secure Performance Analysis and Optimization for FD-NOMA Vehicular Communications
    Lai Wei, Yingyang Chen, Dongsheng Zheng, Bingli Jiao
    http://arxiv.org/abs/2011.03199v1

    • [cs.IT]Transforming Fading Channel From Fast to Slow: IRS-Assisted High-Mobility Communication
    Zixuan Huang, Beixiong Zheng, Rui Zhang
    http://arxiv.org/abs/2011.03147v1

    • [cs.IT]User-Centric Secure Cell Formation for Visible Light Networks with Statistical Delay Guarantees
    Lei Qian, Xuefen Chi, Linlin Zhao, Mohanad Obeed, Anas Chaaban
    http://arxiv.org/abs/2011.03210v1

    • [cs.LG]A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
    Haoran Zhu, Pavankumar Murali, Dzung T. Phan, Lam M. Nguyen, Jayant R. Kalagnanam
    http://arxiv.org/abs/2011.03375v1

    • [cs.LG]ASFGNN: Automated Separated-Federated Graph Neural Network
    Longfei Zheng, Jun Zhou, Chaochao Chen, Bingzhe Wu, Li Wang, Benyu Zhang
    http://arxiv.org/abs/2011.03248v1

    • [cs.LG]Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?
    Chaoqi Wang, Shengyang Sun, Roger Grosse
    http://arxiv.org/abs/2011.03178v1

    • [cs.LG]Communication-efficient Decentralized Local SGD over Undirected Networks
    Tiancheng Qin, S. Rasoul Etesami, César A. Uribe
    http://arxiv.org/abs/2011.03255v1

    • [cs.LG]Complex Query Answering with Neural Link Predictors
    Erik Arakelyan, Daniel Daza, Pasquale Minervini, Michael Cochez
    http://arxiv.org/abs/2011.03459v1

    • [cs.LG]Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation
    Seyedeh Faezeh Hosseini Noorbin, Siamak Layeghy, Brano Kusy, Raja Jurdak, Greg Bishop-hurley, Marius Portmann
    http://arxiv.org/abs/2011.03381v1

    • [cs.LG]Deep coastal sea elements forecasting using U-Net based models
    Jesús García Fernández, Ismail Alaoui Abdellaoui, Siamak Mehrkanoon
    http://arxiv.org/abs/2011.03303v1

    • [cs.LG]Deep learning architectures for inference of AC-OPF solutions
    Thomas Falconer, Letif Mones
    http://arxiv.org/abs/2011.03352v1

    • [cs.LG]Efficient Hyperparameter Tuning with Dynamic Accuracy Derivative-Free Optimization
    Matthias J. Ehrhardt, Lindon Roberts
    http://arxiv.org/abs/2011.03151v1

    • [cs.LG]Explainable AI meets Healthcare: A Study on Heart Disease Dataset
    Devam Dave, Het Naik, Smiti Singhal, Pankesh Patel
    http://arxiv.org/abs/2011.03195v1

    • [cs.LG]FDNAS: Improving Data Privacy and Model Diversity in AutoML
    Chunhui Zhang, Yongyuan Liang, Xiaoming Yuan, Lei Cheng
    http://arxiv.org/abs/2011.03372v1

    • [cs.LG]FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks
    Ali Abedi, Shehroz S. Khan
    http://arxiv.org/abs/2011.03180v1

    • [cs.LG]Identifying and interpreting tuning dimensions in deep networks
    Nolan S. Dey, J. Eric Taylor, Bryan P. Tripp, Alexander Wong, Graham W. Taylor
    http://arxiv.org/abs/2011.03043v1

    • [cs.LG]Improving Sales Forecasting Accuracy: A Tensor Factorization Approach with Demand Awareness
    Xuan Bi, Gediminas Adomavicius, William Li, Annie Qu
    http://arxiv.org/abs/2011.03452v1

    • [cs.LG]Learning Online Data Association
    Yilun Du, Joshua Tenenbaum, Tomas Lozano-Perez, Leslie Kaelbling
    http://arxiv.org/abs/2011.03183v1

    • [cs.LG]Learning with Molecules beyond Graph Neural Networks
    Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
    http://arxiv.org/abs/2011.03488v1

    • [cs.LG]Leveraging an Efficient and Semantic Location Embedding to Seek New Ports of Bike Share Services
    Yuan Wang, Chenwei Wang, Yinan Ling, Keita Yokoyama, Hsin-Tai Wu, Yi Fang
    http://arxiv.org/abs/2011.03158v1

    • [cs.LG]Massively Parallel Graph Drawing and Representation Learning
    Christian Böhm, Claudia Plant
    http://arxiv.org/abs/2011.03479v1

    • [cs.LG]Measuring Data Collection Quality for Community Healthcare
    Ramesha Karunasena, Mohammad Sarparajul Ambiya, Arunesh Sinha, Ruchit Nagar, Saachi Dalal, Divy Thakkar, Milind Tambe
    http://arxiv.org/abs/2011.02962v2

    • [cs.LG]Noise2Sim — Similarity-based Self-Learning for Image Denoising
    Chuang Niu, Ge Wang
    http://arxiv.org/abs/2011.03384v1

    • [cs.LG]Parametric Flatten-T Swish: An Adaptive Non-linear Activation Function For Deep Learning
    Hock Hung Chieng, Noorhaniza Wahid, Pauline Ong
    http://arxiv.org/abs/2011.03155v1

    • [cs.LG]Predicting special care during the COVID-19 pandemic: A machine learning approach
    Vitor Bezzan, Cleber D. Rocco
    http://arxiv.org/abs/2011.03143v1

    • [cs.LG]Resource-Constrained Federated Learning with Heterogeneous Labels and Models
    Gautham Krishna Gudur, Bala Shyamala Balaji, Satheesh K. Perepu
    http://arxiv.org/abs/2011.03206v1

    • [cs.LG]Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning
    Chong Liu, Yuqing Zhu, Kamalika Chaudhuri, Yu-Xiang Wang
    http://arxiv.org/abs/2011.03186v1

    • [cs.LG]Sequential Minimal Optimization for One-Class Slab Support Vector Machine
    Bagesh Kumar, Ayush Sinha, Sourin Chakrabarti, Aashutosh Khandelwal, Harsh Jain, Prof. O. P. Vyas
    http://arxiv.org/abs/2011.03243v1

    • [cs.LG]The Value Equivalence Principle for Model-Based Reinforcement Learning
    Christopher Grimm, André Barreto, Satinder Singh, David Silver
    http://arxiv.org/abs/2011.03506v1

    • [cs.LG]Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
    Dennis Ulmer, Lotta Meijerink, Giovanni Cinà
    http://arxiv.org/abs/2011.03274v1

    • [cs.LG]Underspecification Presents Challenges for Credibility in Modern Machine Learning
    Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
    http://arxiv.org/abs/2011.03395v1

    • [cs.MA]Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
    Jun Ma, Zilong Cheng, Xiaoxue Zhang, Abdullah Al Mamun, Clarence W. de Silva, Tong Heng Lee
    http://arxiv.org/abs/2011.03213v1

    • [cs.RO]Accelerating combinatorial filter reduction through constraints
    Yulin Zhang, Hazhar Rahmani, Dylan A. Shell, Jason M. O’Kane
    http://arxiv.org/abs/2011.03471v1

    • [cs.RO]Adversarial Skill Learning for Robust Manipulation
    Pingcheng Jian, Chao Yang, Di Guo, Huaping Liu, Fuchun Sun
    http://arxiv.org/abs/2011.03383v1

    • [cs.RO]Do We Need to Compensate for Motion Distortion and Doppler Effects in Radar-Based Navigation?
    Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot
    http://arxiv.org/abs/2011.03512v1

    • [cs.RO]Gamma Radiation Source Localization for Micro Aerial Vehicles with a Miniature Single-Detector Compton Event Camera
    Tomas Baca, Petr Stibinger, Daniela Doubravova, Daniel Turecek, Jaroslav Solc, Jan Rusnak, Martin Saska, Jan Jakubek
    http://arxiv.org/abs/2011.03356v1

    • [cs.RO]Generative adversarial training of product of policies for robust and adaptive movement primitives
    Emmanuel Pignat, Hakan Girgin, Sylvain Calinon
    http://arxiv.org/abs/2011.03316v1

    • [cs.RO]HAVEN: A Unity-based Virtual Robot Environment to Showcase HRI-based Augmented Reality
    Andre Cleaver, Darren Tang, Victoria Chen, Jivko Sinapov
    http://arxiv.org/abs/2011.03464v1

    • [cs.RO]Learning Behavior Trees with Genetic Programming in Unpredictable Environments
    Matteo Iovino, Jonathan Styrud, Pietro Falco, Christian Smith
    http://arxiv.org/abs/2011.03252v1

    • [cs.RO]Occlusion-Aware Search for Object Retrieval in Clutter
    Wissam Bejjani, Wisdom C. Agboh, Mehmet R. Dogar, Matteo Leonetti
    http://arxiv.org/abs/2011.03334v1

    • [cs.RO]Optimization-based Trajectory Planning for Tethered Marsupial Robots
    S. Martinez-Rozas, D. Alejo, F. Caballero, L. Merino
    http://arxiv.org/abs/2011.03491v1

    • [cs.RO]RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
    Daniel Ho, Kanishka Rao, Zhuo Xu, Eric Jang, Mohi Khansari, Yunfei Bai
    http://arxiv.org/abs/2011.03148v1

    • [cs.RO]Sample-efficient Reinforcement Learning in Robotic Table Tennis
    Jonas Tebbe, Lukas Krauch, Yapeng Gao, Andreas Zell
    http://arxiv.org/abs/2011.03275v1

    • [cs.RO]Task-relevant Representation Learning for Networked Robotic Perception
    Manabu Nakanoya, Sandeep Chinchali, Alexandros Anemogiannis, Akul Datta, Sachin Katti, Marco Pavone
    http://arxiv.org/abs/2011.03216v1

    • [cs.SE]Analyzing the Productivity of GitHub Teams based on Formation Phase Activity
    Samaneh Saadat, Olivia B. Newton, Gita Sukthankar, Stephen M. Fiore
    http://arxiv.org/abs/2011.03423v1

    • [econ.EM]Conditional quantile estimators: A small sample theory
    Grigory Franguridi, Bulat Gafarov, Kaspar Wuthrich
    http://arxiv.org/abs/2011.03073v1

    • [econ.GN]Did Hurricane Katrina Reduce Mortality?
    Robert Kaestner
    http://arxiv.org/abs/2011.03392v1

    • [eess.AS]Self-Supervised Learning from Contrastive Mixtures for Personalized Speech Enhancement
    Aswin Sivaraman, Minje Kim
    http://arxiv.org/abs/2011.03426v1

    • [eess.IV]A Comprehensive Comparison of Multi-Dimensional Image Denoising Methods
    Zhaoming Kong, Xiaowei Yang, Lifang He
    http://arxiv.org/abs/2011.03462v1

    • [eess.IV]Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network
    Yading Yuan
    http://arxiv.org/abs/2011.03188v1

    • [eess.SP]Fault Location Estimation by Using Machine Learning Methods in Mixed Transmission Lines
    Serkan Budak, Bahadir Akbal
    http://arxiv.org/abs/2011.03238v1

    • [math.OC]Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems
    Rickard Karlsson, Laurens Bliek, Sicco Verwer, Mathijs de Weerdt
    http://arxiv.org/abs/2011.03431v1

    • [math.OC]Optimal Resource and Demand Redistribution for Healthcare Systems Under Stress from COVID-19
    Felix Parker, Hamilton Sawczuk, Fardin Ganjkhanloo, Farzin Ahmadi, Kimia Ghobadi
    http://arxiv.org/abs/2011.03528v1

    • [math.ST]Local Two-Sample Testing over Graphs and Point-Clouds by Random-Walk Distributions
    Boris Landa, Rihao Qu, Joseph Chang, Yuval Kluger
    http://arxiv.org/abs/2011.03418v1

    • [math.ST]Statistical analysis of Wasserstein GANs with applications to time series forecasting
    Moritz Haas, Stefan Richter
    http://arxiv.org/abs/2011.03074v1

    • [physics.chem-ph]Physic-informed Neural-Network Software for Molecular Dynamics Applications
    Taufeq Mohammed Razakh, Beibei Wang, Shane Jackson, Rajiv K. Kalia, Aiichiro Nakano, Ken-ichi Nomura, Priya Vashishta
    http://arxiv.org/abs/2011.03490v1

    • [physics.geo-ph](Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network
    Peng Bai, Giulio Vignoli, Andrea Viezzoli, Jouni Nevalainen, Giuseppina Vacca
    http://arxiv.org/abs/2011.03522v1

    • [physics.soc-ph]COVID-19 Plateau: A Phenomenon of Epidemic Development under Adaptive Prevention Strategies
    Ziqiang Wu, Hao Liao, Alexandre Vidmer, Mingyang Zhou, Wei Chen
    http://arxiv.org/abs/2011.03376v1

    • [stat.ME]A Bayesian Functional Data Model for Surveys Collected under Informative Sampling with Application to Mortality Estimation using NHANES
    Paul A. Parker, Scott H. Holan
    http://arxiv.org/abs/2011.03515v1

    • [stat.ME]A Novel Statistical Test for Treatment Differences in Clinical Trials using a Response Adaptive Forward Looking Gittins Index Rule
    Helen Yvette Barnett, Sofia S Villar, Helena Geys, Thomas Jaki
    http://arxiv.org/abs/2011.03270v1

    • [stat.ME]Bayesian Regression and Classification Using Gaussian Process Priors Indexed by Probability Density Functions
    A. Fradi, Y. Feunteun, C. Samir, M. Baklouti, F. Bachoc, J-M. Loubes
    http://arxiv.org/abs/2011.03282v1

    • [stat.ME]Causal Imputation via Synthetic Interventions
    Chandler Squires, Dennis Shen, Anish Agarwal, Devavrat Shah, Caroline Uhler
    http://arxiv.org/abs/2011.03127v1

    • [stat.ME]Detecting spatial clusters on functional data: a parametric scan statistic approach
    Camille Frévent, Mohamed-Salem Ahmed, Matthieu Marbac, Michaël Genin
    http://arxiv.org/abs/2011.03482v1

    • [stat.ME]Hidden Markov Pólya trees for high-dimensional distributions
    Naoki Awaya, Li Ma
    http://arxiv.org/abs/2011.03121v1

    • [stat.ME]Methods to Compute Prediction Intervals: A Review and New Results
    Qinglong Tian, Daniel J. Nordman, William Q. Meeker
    http://arxiv.org/abs/2011.03065v1

    • [stat.ME]Prediction of Future Failures for Heterogeneous Reliability Field Data
    Colin Lewis-Beck, Qinglong Tian, William Q. Meeker
    http://arxiv.org/abs/2011.03140v1

    • [stat.ME]Semi-Markov Survival Regression Models
    Martin Bladt, Jorge Yslas
    http://arxiv.org/abs/2011.03219v1

    • [stat.ML]Multi-output Gaussian Process Modulated Poisson Processes for Event Prediction
    Salman Jahani, Shiyu Zhou, Dharmaraj Veeramani, Jeff Schmidt
    http://arxiv.org/abs/2011.03172v1

    • [stat.ML]On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method
    Ye He, Krishnakumar Balasubramanian, Murat A. Erdogdu
    http://arxiv.org/abs/2011.03176v1

    • [stat.ML]There is no trade-off: enforcing fairness can improve accuracy
    Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin, Yuekai Sun
    http://arxiv.org/abs/2011.03173v1

    • [stat.ML]User-Dependent Neural Sequence Models for Continuous-Time Event Data
    Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth
    http://arxiv.org/abs/2011.03231v1