cs.AI - 人工智能 cs.CE - 计算工程、 金融和科学 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.DS - 数据结构与算法 cs.ET - 新兴技术 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.OH - 其他CS cs.RO - 机器人学 cs.SD - 声音处理 econ.EM - 计量经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 physics.geo-ph - 地球物理学 q-bio.QM - 定量方法 quant-ph - 量子物理 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]AliMe KBQA: Question Answering over Structured Knowledge for E-commerce Customer Service
• [cs.AI]CLOSURE: Assessing Systematic Generalization of CLEVR Models
• [cs.AI]Learning Improvement Heuristics for Solving the Travelling Salesman Problem
• [cs.CE]Robust Data-driven Profile-based Pricing Schemes
• [cs.CL]Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering
• [cs.CL]Extending Machine Language Models toward Human-Level Language Understanding
• [cs.CL]Improving Interpretability of Word Embeddings by Generating Definition and Usage
• [cs.CL]Personalized Patent Claim Generation and Measurement
• [cs.CL]Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model
• [cs.CR]HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
• [cs.CV]Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques
• [cs.CV]CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting
• [cs.CV]CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems
• [cs.CV]Deep Structured Implicit Functions
• [cs.CV]Discriminative Dimension Reduction based on Mutual Information
• [cs.CV]Estimating 3D Camera Pose from 2D Pedestrian Trajectories
• [cs.CV]GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining
• [cs.CV]Hue-Net: Intensity-based Image-to-Image Translation with Differentiable Histogram Loss Functions
• [cs.CV]Human Motion Anticipation with Symbolic Label
• [cs.CV]Improved Activity Forecasting for Generating Trajectories
• [cs.CV]IoU-aware Single-stage Object Detector for Accurate Localization
• [cs.CV]LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices
• [cs.CV]Local Context Normalization: Revisiting Local Normalization
• [cs.CV]MAGSAC++, a fast, reliable and accurate robust estimator
• [cs.CV]Meaning guided video captioning
• [cs.CV]Neural Voice Puppetry: Audio-driven Facial Reenactment
• [cs.CV]Object Recognition with Human in the Loop Intelligent Frameworks
• [cs.CV]One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
• [cs.CV]Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
• [cs.CV]Photosequencing of Motion Blur using Short and Long Exposures
• [cs.CV]Robust Gabor Networks
• [cs.CV]Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis
• [cs.CV]Simultaneous Detection and Removal of Dynamic Objects in Multi-view Images
• [cs.CV]The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images
• [cs.CV]Totally Deep Support Vector Machines
• [cs.CV]Unified Generative Adversarial Networks for Controllable Image-to-Image Translation
• [cs.CV]VIBE: Video Inference for Human Body Pose and Shape Estimation
• [cs.CV]Variational Coupling Revisited: Simpler Models, Theoretical Connections, and Novel Applications
• [cs.CV]Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Classification
• [cs.CV]Zooming into Face Forensics: A Pixel-level Analysis
• [cs.CV]deepsing: Generating Sentiment-aware Visual Stories using Cross-modal Music Translation
• [cs.CY]Calculate the carbon footprint of your IT assets with EcoDiag, an EcoInfo service
• [cs.CY]Content Generation for Workforce Training
• [cs.CY]Taking Ethics, Fairness, and Bias Seriously in Machine Learning for Disaster Risk Management
• [cs.DC]EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure
• [cs.DC]From Hashgraph to a Family of Atomic Broadcast Algorithms
• [cs.DC]Towards Auction-Based Function Placement in Serverless Fog Platforms
• [cs.DS]Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space
• [cs.ET]A recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems
• [cs.IR]SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
• [cs.IT]An Integral Representation of the Logarithmic Function with Applications in Information Theory
• [cs.IT]Fundamental Limits of Lossless Data Compression with Side Information
• [cs.IT]Minimizing Age of Information with Power Constraints: Opportunistic Scheduling in Multi-State Time-Varying Networks
• [cs.IT]On depth spectra of constacyclic codes over finite commutative chain rings
• [cs.IT]Optimal Transmission Policies for Energy Harvesting Age of Information Systems with Battery Recovery
• [cs.IT]Optimization of Integer-Forcing Precoding for Multi-User MIMO Downlink
• [cs.IT]The Metagenomic Binning Problem: Clustering Markov Sequences
• [cs.LG]An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks
• [cs.LG]Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
• [cs.LG]Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
• [cs.LG]Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
• [cs.LG]Coloring graph neural networks for node disambiguation
• [cs.LG]Deep One-bit Compressive Autoencoding
• [cs.LG]Efficient Per-Example Gradient Computations in Convolutional Neural Networks
• [cs.LG]Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders
• [cs.LG]Game Design for Eliciting Distinguishable Behavior
• [cs.LG]Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS
• [cs.LG]Is Feature Diversity Necessary in Neural Network Initialization?
• [cs.LG]Large-scale Kernel Methods and Applications to Lifelong Robot Learning
• [cs.LG]Learning To Reach Goals Without Reinforcement Learning
• [cs.LG]Linear Mode Connectivity and the Lottery Ticket Hypothesis
• [cs.LG]On the relationship between multitask neural networks and multitask Gaussian Processes
• [cs.LG]Provably Efficient Exploration in Policy Optimization
• [cs.LG]REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks
• [cs.LG]Representation of Federated Learning via Worst-Case Robust Optimization Theory
• [cs.LG]Speech-driven facial animation using polynomial fusion of features
• [cs.LG]Sublinear Optimal Policy Value Estimation in Contextual Bandits
• [cs.LG]Sublinear Time Numerical Linear Algebra for Structured Matrices
• [cs.LG]Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
• [cs.LG]The PlayStation Reinforcement Learning Environment (PSXLE)
• [cs.LG]The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)
• [cs.LG]Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
• [cs.LG]Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples
• [cs.LG]Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
• [cs.LG]What it Thinks is Important is Important: Robustness Transfers through Input Gradients
• [cs.LO]Formal Verification of Debates in Argumentation Theory
• [cs.MA]Biases for Emergent Communication in Multi-agent Reinforcement Learning
• [cs.MA]Multi-Agent Task Allocation in Complementary Teams: A Hunter and Gatherer Approach
• [cs.NE]STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods
• [cs.NE]Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES
• [cs.NI]Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud
• [cs.OH]The Use of Machine Learning and Big Five Personality Taxonomy to Predict Construction Workers’ Safety Behaviour
• [cs.RO]0-Step Capturability, Motion Decomposition and Global Feedback Control of the 3D Variable Height-Inverted Pendulum
• [cs.RO]A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
• [cs.RO]Graph Neural Networks for Decentralized Multi-Robot Path Planning
• [cs.RO]Parareal with a Learned Coarse Model for Robotic Manipulation
• [cs.SD]Encoding Musical Style with Transformer Autoencoders
• [econ.EM]A Regularized Factor-augmented Vector Autoregressive Model
• [eess.AS]Leveraging End-to-End Speech Recognition with Neural Architecture Search
• [eess.AS]Measuring Mother-Infant Emotions By Audio Sensing
• [eess.AS]On Neural Phone Recognition of Mixed-Source ECoG Signals
• [eess.AS]Singing Synthesis: with a little help from my attention
• [eess.IV]A Saliency Dataset of Head and Eye Movements for Augmented Reality
• [eess.IV]An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks
• [eess.IV]CARP: Compression through Adaptive Recursive Partitioning for Multi-dimensional Images
• [eess.IV]Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics
• [eess.IV]EM-based approach to 3D reconstruction from single-waveform multispectral Lidar data
• [eess.IV]IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
• [eess.IV]Learned Variable-Rate Image Compression with Residual Divisive Normalization
• [eess.IV]SegTHOR: Segmentation of Thoracic Organs at Risk in CT images
• [eess.IV]Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation
• [eess.SP]Graph Theory and Metro Traffic Modelling
• [eess.SP]Terahertz Communications (TeraCom): Challenges and Impact on 6G Wireless Systems
• [eess.SY]Fundamental Entropic Laws and $\mathcal{L}_p$ Limitations of Feedback Systems: Implications for Machine-Learning-in-the-Loop Control
• [eess.SY]Gaussian Conditionally Markov Sequences: Dynamic Models and Representations of Reciprocal and Other Classes
• [math.NA]A numerical study of the pollution error and DPG adaptivity for long waveguide simulations
• [math.OC]Control-Tutored Reinforcement Learning
• [math.OC]Parallel Restarted SPIDER — Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
• [math.ST]Combining e-values and p-values
• [math.ST]Diffusion based Gaussian process regression via heat kernel reconstruction
• [math.ST]Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
• [math.ST]Graph quilting: graphical model selection from partially observed covariances
• [math.ST]Scale invariant proper scoring rules Scale dependence: Why the average CRPS often is inappropriate for ranking probabilistic forecasts
• [math.ST]Variable Selection Consistency of Gaussian Process Regression
• [physics.geo-ph]Unsupervised classification of acoustic emissions from catalogs and fault time-to-failure prediction
• [q-bio.QM]Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling
• [quant-ph]Bell Diagonal and Werner state generation: entanglement, non-locality, steering and discord on the IBM quantum computer
• [quant-ph]Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
• [stat.AP]A Hierarchical Modelling Framework for Correcting Delayed Reporting in Spatio-Temporal Disease Surveillance Data
• [stat.AP]A low-rank semiparametric Bayesian spatial model for estimating extreme Red Sea surface temperature hotspots
• [stat.AP]A state-space model for dynamic functional connectivity
• [stat.CO]Exploratory data analysis for large-scale multiple testing problems and its application in gene expression studies
• [stat.ME]Diagnosing model misspecification and performing generalized Bayes’ updates via probabilistic classifiers
• [stat.ME]Identifying and Responding to Outlier Demand in Revenue Management
• [stat.ME]Parametric mode regression for bounded data
• [stat.ME]Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models
• [stat.ME]Testing Independence under Biased Sampling
• [stat.ML]Adaptive Reticulum
• [stat.ML]Measuring the Reliability of Reinforcement Learning Algorithms
• [stat.ML]Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits
• [stat.ML]Normalizing Constant Estimation with Gaussianized Bridge Sampling
·····································
• [cs.AI]AliMe KBQA: Question Answering over Structured Knowledge for E-commerce Customer Service
Feng-Lin Li, Weijia Chen, Qi Huang, Yikun Guo
http://arxiv.org/abs/1912.05728v1
• [cs.AI]CLOSURE: Assessing Systematic Generalization of CLEVR Models
Dzmitry Bahdanau, Harm de Vries, Timothy J. O’Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, Aaron Courville
http://arxiv.org/abs/1912.05783v1
• [cs.AI]Learning Improvement Heuristics for Solving the Travelling Salesman Problem
Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim
http://arxiv.org/abs/1912.05784v1
• [cs.CE]Robust Data-driven Profile-based Pricing Schemes
Jingshi Cui, Haoxiang Wang, Chenye Wu, Yang Yu
http://arxiv.org/abs/1912.05731v1
• [cs.CL]Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering
Casimiro Pio Carrino, Marta R. Costa-jussà, José A. R. Fonollosa
http://arxiv.org/abs/1912.05200v2
• [cs.CL]Extending Machine Language Models toward Human-Level Language Understanding
James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze
http://arxiv.org/abs/1912.05877v1
• [cs.CL]Improving Interpretability of Word Embeddings by Generating Definition and Usage
Haitong Zhang, Yongping Du, Jiaxin Sun, Qingxiao Li
http://arxiv.org/abs/1912.05898v1
• [cs.CL]Personalized Patent Claim Generation and Measurement
Jieh-Sheng Lee
http://arxiv.org/abs/1912.03502v2
• [cs.CL]Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model
Hamid Mohammadi, Seyed Hossein Khasteh
http://arxiv.org/abs/1912.05957v1
• [cs.CR]HybridAlpha: An Efficient Approach for Privacy-Preserving Federated Learning
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, Heiko Ludwig
http://arxiv.org/abs/1912.05897v1
• [cs.CV]Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques
Liang Zhao, Brendan Odigwe, Susan Lessner, Daniel G. Clair, Firas Mussa, Homayoun Valafar
http://arxiv.org/abs/1912.06010v1
• [cs.CV]CCCNet: An Attention Based Deep Learning Framework for Categorized Crowd Counting
Sarkar Snigdha Sarathi Das, Syed Md. Mukit Rashid, Mohammed Eunus Ali
http://arxiv.org/abs/1912.05765v1
• [cs.CV]CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems
Sudheer Achary, Syed Ashar Javed, Nikita Shravan, K L Bhanu Moorthy, Vineet Gandhi, Anoop Namboodiri
http://arxiv.org/abs/1912.05636v1
• [cs.CV]Deep Structured Implicit Functions
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, Thomas Funkhouser
http://arxiv.org/abs/1912.06126v1
• [cs.CV]Discriminative Dimension Reduction based on Mutual Information
Orod Razeghi, Guoping Qiu
http://arxiv.org/abs/1912.05631v1
• [cs.CV]Estimating 3D Camera Pose from 2D Pedestrian Trajectories
Yan Xu, Vivek Roy, Kris Kitani
http://arxiv.org/abs/1912.05758v1
• [cs.CV]GPRInvNet: Deep Learning-Based Ground Penetrating Radar Data Inversion for Tunnel Lining
Bin Liu, Yuxiao Ren, Hanchi Liu, Hui Xu, Zhengfang Wang, Anthony G. Cohn, Peng Jiang
http://arxiv.org/abs/1912.05759v1
• [cs.CV]Hue-Net: Intensity-based Image-to-Image Translation with Differentiable Histogram Loss Functions
Mor Avi-Aharon, Assaf Arbelle, Tammy Riklin Raviv
http://arxiv.org/abs/1912.06044v1
• [cs.CV]Human Motion Anticipation with Symbolic Label
Julian Tanke, Juergen Gall
http://arxiv.org/abs/1912.06079v1
• [cs.CV]Improved Activity Forecasting for Generating Trajectories
Daisuke Ogawa, Toru Tamaki, Tsubasa Hirakawa, Bisser Raytchev, Kazufumi Kaneda, Ken Yoda
http://arxiv.org/abs/1912.05729v1
• [cs.CV]IoU-aware Single-stage Object Detector for Accurate Localization
Shengkai Wu, Xiaoping Li, Xinggang Wang
http://arxiv.org/abs/1912.05992v1
• [cs.CV]LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices
Radu Alexandru Rosu, Peer Schütt, Jan Quenzel, Sven Behnke
http://arxiv.org/abs/1912.05905v1
• [cs.CV]Local Context Normalization: Revisiting Local Normalization
Anthony Ortiz, Caleb Robinson, Mahmudulla Hassan, Dan Morris, Olac Fuentes, Christopher Kiekintveld, Nebojsa Jojic
http://arxiv.org/abs/1912.05845v1
• [cs.CV]MAGSAC++, a fast, reliable and accurate robust estimator
Daniel Barath, Jana Noskova, Maksym Ivashechkin, Jiri Matas
http://arxiv.org/abs/1912.05909v1
• [cs.CV]Meaning guided video captioning
Rushi J. Babariya, Toru Tamaki
http://arxiv.org/abs/1912.05730v1
• [cs.CV]Neural Voice Puppetry: Audio-driven Facial Reenactment
Justus Thies, Mohamed Elgharib, Ayush Tewari, Christian Theobalt, Matthias Nießner
http://arxiv.org/abs/1912.05566v1
• [cs.CV]Object Recognition with Human in the Loop Intelligent Frameworks
Orod Razeghi, Guoping Qiu
http://arxiv.org/abs/1912.05575v1
• [cs.CV]One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
Vinit Sarode, Xueqian Li, Hunter Goforth, Yasuhiro Aoki, Animesh Dhagat, Rangaprasad Arun Srivatsan, Simon Lucey, Howie Choset
http://arxiv.org/abs/1912.05766v1
• [cs.CV]Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdoor Environments
Bruna G. Maciel-Pearson, Letizia Marchegiani, Samet Akcay, Amir Atapour-Abarghouei, James Garforth, Toby P. Breckon
http://arxiv.org/abs/1912.05684v1
• [cs.CV]Photosequencing of Motion Blur using Short and Long Exposures
Vijay Rengarajan, Shuo Zhao, Ruiwen Zhen, John Glotzbach, Hamid Sheikh, Aswin C. Sankaranarayanan
http://arxiv.org/abs/1912.06102v1
• [cs.CV]Robust Gabor Networks
Juan C. Pérez, Motasem Alfarra, Guillaume Jeanneret, Adel Bibi, Ali Thabet, Bernard Ghanem, Pablo Arbeláez
http://arxiv.org/abs/1912.05661v1
• [cs.CV]Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis
Toru Tamaki, Daisuke Ogawa, Bisser Raytchev, Kazufumi Kaneda
http://arxiv.org/abs/1912.05727v1
• [cs.CV]Simultaneous Detection and Removal of Dynamic Objects in Multi-view Images
Gagan Kanojia, Shanmuganathan Raman
http://arxiv.org/abs/1912.05591v1
• [cs.CV]The Benefits of Close-Domain Fine-Tuning for Table Detection in Document Images
Ángela Casado-García, César Domínguez, Jónathan Heras, Eloy Mata, Vico Pascual
http://arxiv.org/abs/1912.05846v1
• [cs.CV]Totally Deep Support Vector Machines
Hichem Sahbi
http://arxiv.org/abs/1912.05864v1
• [cs.CV]Unified Generative Adversarial Networks for Controllable Image-to-Image Translation
Hao Tang, Hong Liu, Nicu Sebe
http://arxiv.org/abs/1912.06112v1
• [cs.CV]VIBE: Video Inference for Human Body Pose and Shape Estimation
Muhammed Kocabas, Nikos Athanasiou, Michael J. Black
http://arxiv.org/abs/1912.05656v1
• [cs.CV]Variational Coupling Revisited: Simpler Models, Theoretical Connections, and Novel Applications
Aaron Wewior, Joachim Weickert
http://arxiv.org/abs/1912.05888v1
• [cs.CV]Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Classification
Nelson Nauata, Yasutaka Furukawa
http://arxiv.org/abs/1912.05135v2
• [cs.CV]Zooming into Face Forensics: A Pixel-level Analysis
Jia Li, Tong Shen, Wei Zhang, Hui Ren, Dan Zeng, Tao Mei
http://arxiv.org/abs/1912.05790v1
• [cs.CV]deepsing: Generating Sentiment-aware Visual Stories using Cross-modal Music Translation
Nikolaos Passalis, Stavros Doropoulos
http://arxiv.org/abs/1912.05654v1
• [cs.CY]Calculate the carbon footprint of your IT assets with EcoDiag, an EcoInfo service
Béatrice Montbroussous, Jonathan Schaeffer, Gabriel Moreau, Francoise Berthoud, Gabrielle Feltin
http://arxiv.org/abs/1912.06038v1
• [cs.CY]Content Generation for Workforce Training
Holly Rushmeier, Kapil Chalil Madathil, Jessica Hodgins, Beth Mynatt, Tony Derose, Blair Macintyre, other workshop participants
http://arxiv.org/abs/1912.05606v1
• [cs.CY]Taking Ethics, Fairness, and Bias Seriously in Machine Learning for Disaster Risk Management
Robert Soden, Dennis Wagenaar, Dave Luo, Annegien Tjieesen
http://arxiv.org/abs/1912.05538v1
• [cs.DC]EPIC: An Energy-Efficient, High-Performance GPGPU Computing Research Infrastructure
Magnus Själander, Magnus Jahre, Gunnar Tufte, Nico Reissmann
http://arxiv.org/abs/1912.05848v1
• [cs.DC]From Hashgraph to a Family of Atomic Broadcast Algorithms
Trafim Lasy
http://arxiv.org/abs/1912.05895v1
• [cs.DC]Towards Auction-Based Function Placement in Serverless Fog Platforms
David Bermbach, Setareh Maghsudi, Jonathan Hasenburg, Tobias Pfandzelter
http://arxiv.org/abs/1912.06096v1
• [cs.DS]Graph Sparsification for Derandomizing Massively Parallel Computation with Low Space
Artur Czumaj, Peter Davies, Merav Parter
http://arxiv.org/abs/1912.05390v2
• [cs.ET]A recipe for creating ideal hybrid memristive-CMOS neuromorphic computing systems
Elisabetta Chicca, Giacomo Indiveri
http://arxiv.org/abs/1912.05637v1
• [cs.IR]SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval
Liang Pang, Jun Xu, Qingyao Ai, Yanyan Lan, Xueqi Cheng, Jirong Wen
http://arxiv.org/abs/1912.05891v1
• [cs.IT]An Integral Representation of the Logarithmic Function with Applications in Information Theory
Neri Merhav, Igal Sason
http://arxiv.org/abs/1912.05812v1
• [cs.IT]Fundamental Limits of Lossless Data Compression with Side Information
Lampros Gavalakis, Ioannis Kontoyiannis
http://arxiv.org/abs/1912.05734v1
• [cs.IT]Minimizing Age of Information with Power Constraints: Opportunistic Scheduling in Multi-State Time-Varying Networks
Haoyue Tang, Jintao Wang, Linqi Song, Jian Song
http://arxiv.org/abs/1912.05947v1
• [cs.IT]On depth spectra of constacyclic codes over finite commutative chain rings
Anuradha Sharma, Tania Sidana
http://arxiv.org/abs/1912.05815v1
• [cs.IT]Optimal Transmission Policies for Energy Harvesting Age of Information Systems with Battery Recovery
Caglar Tunc, Shivendra Panwar
http://arxiv.org/abs/1912.06119v1
• [cs.IT]Optimization of Integer-Forcing Precoding for Multi-User MIMO Downlink
Ricardo Bohaczuk Venturelli, Danilo Silva
http://arxiv.org/abs/1912.05609v1
• [cs.IT]The Metagenomic Binning Problem: Clustering Markov Sequences
G. Greenberg, I. Shomorony
http://arxiv.org/abs/1912.05741v1
• [cs.LG]An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks
Giyoung Jeon, Haedong Jeong, Jaesik Choi
http://arxiv.org/abs/1912.05827v1
• [cs.LG]Automatic Layout Generation with Applications in Machine Learning Engine Evaluation
Haoyu Yang, Wen Chen, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, Bei Yu
http://arxiv.org/abs/1912.05796v1
• [cs.LG]Bayesian Hyperparameter Optimization with BoTorch, GPyTorch and Ax
Daniel T. Chang
http://arxiv.org/abs/1912.05686v1
• [cs.LG]Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
Erik Daxberger, José Miguel Hernández-Lobato
http://arxiv.org/abs/1912.05651v1
• [cs.LG]Coloring graph neural networks for node disambiguation
George Dasoulas, Ludovic Dos Santos, Kevin Scaman, Aladin Virmaux
http://arxiv.org/abs/1912.06058v1
• [cs.LG]Deep One-bit Compressive Autoencoding
Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
http://arxiv.org/abs/1912.05539v1
• [cs.LG]Efficient Per-Example Gradient Computations in Convolutional Neural Networks
Gaspar Rochette, Andre Manoel, Eric W. Tramel
http://arxiv.org/abs/1912.06015v1
• [cs.LG]Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders
Johan Medrano, Fuchun Joseph Lin
http://arxiv.org/abs/1912.05879v1
• [cs.LG]Game Design for Eliciting Distinguishable Behavior
Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom Mitchell
http://arxiv.org/abs/1912.06074v1
• [cs.LG]Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS
Petro Liashchynskyi, Pavlo Liashchynskyi
http://arxiv.org/abs/1912.06059v1
• [cs.LG]Is Feature Diversity Necessary in Neural Network Initialization?
Yaniv Blumenfeld, Dar Gilboa, Daniel Soudry
http://arxiv.org/abs/1912.05137v2
• [cs.LG]Large-scale Kernel Methods and Applications to Lifelong Robot Learning
Raffaello Camoriano
http://arxiv.org/abs/1912.05629v1
• [cs.LG]Learning To Reach Goals Without Reinforcement Learning
Dibya Ghosh, Abhishek Gupta, Justin Fu, Ashwin Reddy, Coline Devine, Benjamin Eysenbach, Sergey Levine
http://arxiv.org/abs/1912.06088v1
• [cs.LG]Linear Mode Connectivity and the Lottery Ticket Hypothesis
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
http://arxiv.org/abs/1912.05671v1
• [cs.LG]On the relationship between multitask neural networks and multitask Gaussian Processes
Karthikeyan K, Shubham Kumar Bharti, Piyush Rai
http://arxiv.org/abs/1912.05723v1
• [cs.LG]Provably Efficient Exploration in Policy Optimization
Qi Cai, Zhuoran Yang, Chi Jin, Zhaoran Wang
http://arxiv.org/abs/1912.05830v1
• [cs.LG]REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks
Omid Bazgir, Ruibo Zhang, Saugato Rahman Dhruba, Raziur Rahman, Souparno Ghosh, Ranadip Pal
http://arxiv.org/abs/1912.05687v1
• [cs.LG]Representation of Federated Learning via Worst-Case Robust Optimization Theory
Saeedeh Parsaeefard, Iman Tabrizian, Alberto Leon Garcia
http://arxiv.org/abs/1912.05571v1
• [cs.LG]Speech-driven facial animation using polynomial fusion of features
Triantafyllos Kefalas, Konstantinos Vougioukas, Yannis Panagakis, Stavros Petridis, Jean Kossaifi, Maja Pantic
http://arxiv.org/abs/1912.05833v1
• [cs.LG]Sublinear Optimal Policy Value Estimation in Contextual Bandits
Weihao Kong, Gregory Valiant, Emma Brunskill
http://arxiv.org/abs/1912.06111v1
• [cs.LG]Sublinear Time Numerical Linear Algebra for Structured Matrices
Xiaofei Shi, David P. Woodruff
http://arxiv.org/abs/1912.06060v1
• [cs.LG]Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Ziyue Li, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, Fugee Tsung
http://arxiv.org/abs/1912.05693v1
• [cs.LG]The PlayStation Reinforcement Learning Environment (PSXLE)
Carlos Purves, Cătălina Cangea, Petar Veličković
http://arxiv.org/abs/1912.06101v1
• [cs.LG]The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)
Ernest Davis
http://arxiv.org/abs/1912.05752v1
• [cs.LG]Towards Expressive Priors for Bayesian Neural Networks: Poisson Process Radial Basis Function Networks
Beau Coker, Melanie F. Pradier, Finale Doshi-Velez
http://arxiv.org/abs/1912.05779v1
• [cs.LG]Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples
Behzad Asadi, Vijay Varadharajan
http://arxiv.org/abs/1912.05945v1
• [cs.LG]Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
Menghan Wang, Kun Zhang, Gulin Li, Keping Yang, Luo Si
http://arxiv.org/abs/1912.05977v1
• [cs.LG]What it Thinks is Important is Important: Robustness Transfers through Input Gradients
Alvin Chan, Yi Tay, Yew-Soon Ong
http://arxiv.org/abs/1912.05699v1
• [cs.LO]Formal Verification of Debates in Argumentation Theory
Ria Jha, Francesco Belardinelli, Francesca Toni
http://arxiv.org/abs/1912.05828v1
• [cs.MA]Biases for Emergent Communication in Multi-agent Reinforcement Learning
Tom Eccles, Yoram Bachrach, Guy Lever, Angeliki Lazaridou, Thore Graepel
http://arxiv.org/abs/1912.05676v1
• [cs.MA]Multi-Agent Task Allocation in Complementary Teams: A Hunter and Gatherer Approach
Mehdi Dadvar, Saeed Moazami, Harley R. Myler, Hassan Zargarzadeh
http://arxiv.org/abs/1912.05748v1
• [cs.NE]STEERAGE: Synthesis of Neural Networks Using Architecture Search and Grow-and-Prune Methods
Shayan Hassantabar, Xiaoliang Dai, Niraj K. Jha
http://arxiv.org/abs/1912.05831v1
• [cs.NE]Sequential vs. Integrated Algorithm Selection and Configuration: A Case Study for the Modular CMA-ES
Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck
http://arxiv.org/abs/1912.05899v1
• [cs.NI]Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud
Hang Guo, Xun Fan, Anh Cao, Geoff Outhred, John Heidemann
http://arxiv.org/abs/1912.05590v1
• [cs.OH]The Use of Machine Learning and Big Five Personality Taxonomy to Predict Construction Workers’ Safety Behaviour
Yifan Gao, Vicente A. Gonzalez, Tak Wing Yiu, Guillermo Cabrera-Guerrerod
http://arxiv.org/abs/1912.05944v1
• [cs.RO]0-Step Capturability, Motion Decomposition and Global Feedback Control of the 3D Variable Height-Inverted Pendulum
Gabriel Garcia, Robert Griffin, Jerry Pratt
http://arxiv.org/abs/1912.06078v1
• [cs.RO]A Billion Ways to Grasp: An Evaluation of Grasp Sampling Schemes on a Dense, Physics-based Grasp Data Set
Clemens Eppner, Arsalan Mousavian, Dieter Fox
http://arxiv.org/abs/1912.05604v1
• [cs.RO]Graph Neural Networks for Decentralized Multi-Robot Path Planning
Qingbiao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok
http://arxiv.org/abs/1912.06095v1
• [cs.RO]Parareal with a Learned Coarse Model for Robotic Manipulation
Wisdom Agboh, Oliver Grainger, Daniel Ruprecht, Mehmet Dogar
http://arxiv.org/abs/1912.05958v1
• [cs.SD]Encoding Musical Style with Transformer Autoencoders
Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel
http://arxiv.org/abs/1912.05537v1
• [econ.EM]A Regularized Factor-augmented Vector Autoregressive Model
Maurizio Daniele, Julie Schnaitmann
http://arxiv.org/abs/1912.06049v1
• [eess.AS]Leveraging End-to-End Speech Recognition with Neural Architecture Search
Ahmed Baruwa, Mojeed Abisiga, Ibrahim Gbadegesin, Afeez Fakunle
http://arxiv.org/abs/1912.05946v1
• [eess.AS]Measuring Mother-Infant Emotions By Audio Sensing
Xuewen Yao, Dong He, Tiancheng Jing, Kaya de Barbaro
http://arxiv.org/abs/1912.05920v1
• [eess.AS]On Neural Phone Recognition of Mixed-Source ECoG Signals
Ahmed Hussen Abdelaziz, Shuo-Yiin Chang, Nelson Morgan, Erik Edwards, Dorothea Kolossa, Dan Ellis, David A. Moses, Edward F. Chang
http://arxiv.org/abs/1912.05869v1
• [eess.AS]Singing Synthesis: with a little help from my attention
Orazio Angelini, Alexis Moinet, Kayoko Yanagisawa, Thomas Drugman
http://arxiv.org/abs/1912.05881v1
• [eess.IV]A Saliency Dataset of Head and Eye Movements for Augmented Reality
Yucheng Zhu, Dandan Zhu, Yiwei Yang, Huiyu Duan, Qiangqiang Zhou, Xiongkuo Min, Jiantao Zhou, Guangtao Zhai, Xiaokang Yang
http://arxiv.org/abs/1912.05971v1
• [eess.IV]An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks
Kexin Zhang, Gencer Sumbul, Begüm Demir
http://arxiv.org/abs/1912.06013v1
• [eess.IV]CARP: Compression through Adaptive Recursive Partitioning for Multi-dimensional Images
Rongjie Liu, Meng Li, Li Ma
http://arxiv.org/abs/1912.05622v1
• [eess.IV]Coronary Artery Plaque Characterization from CCTA Scans using Deep Learning and Radiomics
Felix Denzinger, Michael Wels, Nishant Ravikumar, Katharina Breininger, Anika Reidelshöfer, Joachim Eckert, Michael Sühling, Axel Schmermund, Andreas Maier
http://arxiv.org/abs/1912.06075v1
• [eess.IV]EM-based approach to 3D reconstruction from single-waveform multispectral Lidar data
Quentin Legros, Sylvain Meignen, Stephen McLaughlin, Yoann Altmann
http://arxiv.org/abs/1912.06092v1
• [eess.IV]IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Liangzhi Li, Manisha Verma, Yuta Nakashima, Hajime Nagahara, Ryo Kawasaki
http://arxiv.org/abs/1912.05763v1
• [eess.IV]Learned Variable-Rate Image Compression with Residual Divisive Normalization
Mohammad Akbari, Jie Liang, Jingning Han, Chengjie Tu
http://arxiv.org/abs/1912.05688v1
• [eess.IV]SegTHOR: Segmentation of Thoracic Organs at Risk in CT images
Z. Lambert, C. Petitjean, B. Dubray, S. Ruan
http://arxiv.org/abs/1912.05950v1
• [eess.IV]Understanding Important Features of Deep Learning Models for Transmission Electron Microscopy Image Segmentation
James P. Horwath, Dmitri N. Zakharov, Remi Megret, Eric A. Stach
http://arxiv.org/abs/1912.06077v1
• [eess.SP]Graph Theory and Metro Traffic Modelling
Bruno Scalzo Dees, Anthony G. Constantinides, Danilo P. Mandic
http://arxiv.org/abs/1912.05964v1
• [eess.SP]Terahertz Communications (TeraCom): Challenges and Impact on 6G Wireless Systems
Chong Han, Yongzhi Wu, Zhi Chen, Xudong Wang
http://arxiv.org/abs/1912.06040v1
• [eess.SY]Fundamental Entropic Laws and $\mathcal{L}_p$ Limitations of Feedback Systems: Implications for Machine-Learning-in-the-Loop Control
Song Fang, Quanyan Zhu
http://arxiv.org/abs/1912.05541v1
• [eess.SY]Gaussian Conditionally Markov Sequences: Dynamic Models and Representations of Reciprocal and Other Classes
Reza Rezaie, X. Rong Li
http://arxiv.org/abs/1912.05739v1
• [math.NA]A numerical study of the pollution error and DPG adaptivity for long waveguide simulations
Stefan Henneking, Leszek Demkowicz
http://arxiv.org/abs/1912.05716v1
• [math.OC]Control-Tutored Reinforcement Learning
Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Piero De Lellis, Mario di Bernardo
http://arxiv.org/abs/1912.06085v1
• [math.OC]Parallel Restarted SPIDER — Communication Efficient Distributed Nonconvex Optimization with Optimal Computation Complexity
Pranay Sharma, Prashant Khanduri, Saikiran Bulusu, Ketan Rajawat, Pramod K. Varshney
http://arxiv.org/abs/1912.06036v1
• [math.ST]Combining e-values and p-values
Vladimir Vovk, Ruodu Wang
http://arxiv.org/abs/1912.06116v1
• [math.ST]Diffusion based Gaussian process regression via heat kernel reconstruction
David B Dunson, Hau-Tieng Wu, Nan Wu
http://arxiv.org/abs/1912.05680v1
• [math.ST]Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
Badr-Eddine Chérief-Abdellatif, Pierre Alquier
http://arxiv.org/abs/1912.05737v1
• [math.ST]Graph quilting: graphical model selection from partially observed covariances
Giuseppe Vinci, Gautam Dasarathy, Genevera I. Allen
http://arxiv.org/abs/1912.05573v1
• [math.ST]Scale invariant proper scoring rules Scale dependence: Why the average CRPS often is inappropriate for ranking probabilistic forecasts
David Bolin, Jonas Wallin
http://arxiv.org/abs/1912.05642v1
• [math.ST]Variable Selection Consistency of Gaussian Process Regression
Sheng Jiang, Surya T. Tokdar
http://arxiv.org/abs/1912.05738v1
• [physics.geo-ph]Unsupervised classification of acoustic emissions from catalogs and fault time-to-failure prediction
Hope Jasperson, Chas Bolton, Paul Johnson, Chris Marone, Maarten V. de Hoop
http://arxiv.org/abs/1912.06087v1
• [q-bio.QM]Pathway Activity Analysis and Metabolite Annotation for Untargeted Metabolomics using Probabilistic Modeling
Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu, Soha Hassoun
http://arxiv.org/abs/1912.05753v1
• [quant-ph]Bell Diagonal and Werner state generation: entanglement, non-locality, steering and discord on the IBM quantum computer
Elias Riedel Gårding, Nicolas Schwaller, Su Yeon Chang, Samuel Bosch, Willy Robert Laborde, Javier Naya Hernandez, Chun Lam Chan, Frédéric Gessler, Xinyu Si, Marc-André Dupertuis, Nicolas Macris
http://arxiv.org/abs/1912.06105v1
• [quant-ph]Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier, Aritra Sarkar, Thomas Gabor, Koen Bertels
http://arxiv.org/abs/1912.06032v1
• [stat.AP]A Hierarchical Modelling Framework for Correcting Delayed Reporting in Spatio-Temporal Disease Surveillance Data
Oliver Stoner, Theo Economou
http://arxiv.org/abs/1912.05965v1
• [stat.AP]A low-rank semiparametric Bayesian spatial model for estimating extreme Red Sea surface temperature hotspots
Arnab Hazra, Raphaël Huser
http://arxiv.org/abs/1912.05657v1
• [stat.AP]A state-space model for dynamic functional connectivity
Sourish Chakravarty, Zachary D. Threlkeld, Yelena G. Bodien, Brian L. Edlow, Emery N. Brown
http://arxiv.org/abs/1912.05595v1
• [stat.CO]Exploratory data analysis for large-scale multiple testing problems and its application in gene expression studies
Paramita Chakraborty, Chong Ma, John Grego, James Lynch
http://arxiv.org/abs/1912.06030v1
• [stat.ME]Diagnosing model misspecification and performing generalized Bayes’ updates via probabilistic classifiers
Owen Thomas, Jukka Corander
http://arxiv.org/abs/1912.05810v1
• [stat.ME]Identifying and Responding to Outlier Demand in Revenue Management
Nicola Rennie, Catherine Cleophas, Adam Sykulski, Florian Dost
http://arxiv.org/abs/1912.05974v1
• [stat.ME]Parametric mode regression for bounded data
Haiming Zhou, Xianzheng Huang
http://arxiv.org/abs/1912.05588v1
• [stat.ME]Sensitivity analysis for bias due to a misclassfied confounding variable in marginal structural models
Linda Nab, Rolf H. H. Groenwold, Maarten van Smeden, Ruth H. Keogh
http://arxiv.org/abs/1912.05800v1
• [stat.ME]Testing Independence under Biased Sampling
Yaniv Tenzer, Micha Mandel, Or Zuk
http://arxiv.org/abs/1912.05769v1
• [stat.ML]Adaptive Reticulum
Giuseppe Nuti, Lluís Antoni Jiménez Rugama, Kaspar Thommen
http://arxiv.org/abs/1912.05901v1
• [stat.ML]Measuring the Reliability of Reinforcement Learning Algorithms
Stephanie C. Y. Chan, Sam Fishman, John Canny, Anoop Korattikara, Sergio Guadarrama
http://arxiv.org/abs/1912.05663v1
• [stat.ML]Near-optimal Oracle-efficient Algorithms for Stationary and Non-Stationary Stochastic Linear Bandits
Baekjin Kim, Ambuj Tewari
http://arxiv.org/abs/1912.05695v1
• [stat.ML]Normalizing Constant Estimation with Gaussianized Bridge Sampling
He Jia, Uroš Seljak
http://arxiv.org/abs/1912.06073v1