astro-ph.IM - 仪器仪表和天体物理学方法
cond-mat.stat-mech - 统计数学
cs.AI - 人工智能
cs.CL - 计算与语言
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.DM - 离散数学
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.PL - 编程语言
cs.RO - 机器人学
cs.SI - 社交网络与信息网络
econ.GN - 一般经济学
eess.IV - 图像与视频处理
math.CO - 组合数学
math.PR - 概率
math.ST - 统计理论
physics.soc-ph - 物理学与社会
q-bio.BM - 生物分子
stat.AP - 应用统计
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [astro-ph.IM]Towards Machine-assisted Meta-Studies: The Hubble Constant
• [cond-mat.stat-mech]Spectral content of a single non-Brownian trajectory
• [cs.AI]Causal Simulations for Uplift Modeling
• [cs.AI]Learning to Make Analogies by Contrasting Abstract Relational Structure
• [cs.AI]The Second Conversational Intelligence Challenge (ConvAI2)
• [cs.CL]A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
• [cs.CL]DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
• [cs.CL]Dating Documents using Graph Convolution Networks
• [cs.CL]Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding
• [cs.CL]How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
• [cs.CL]Human acceptability judgements for extractive sentence compression
• [cs.CL]Joint Entity Linking with Deep Reinforcement Learning
• [cs.CL]Multilingual NER Transfer for Low-resource Languages
• [cs.CL]Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
• [cs.CL]tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
• [cs.CV]A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids
• [cs.CV]BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
• [cs.CV]ColorNet: Investigating the importance of color spaces for image classification
• [cs.CV]Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
• [cs.CV]Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution
• [cs.CV]Deep Learning Solutions for TanDEM-X-based Forest Classification
• [cs.CV]Deep Triplet Quantization
• [cs.CV]Do we train on test data? Purging CIFAR of near-duplicates
• [cs.CV]End-to-end Lane Detection through Differentiable Least-Squares Fitting
• [cs.CV]Episodic Training for Domain Generalization
• [cs.CV]Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution
• [cs.CV]Generative Smoke Removal
• [cs.CV]Learnable Embedding Space for Efficient Neural Architecture Compression
• [cs.CV]Learning Differentiable Grammars for Continuous Data
• [cs.CV]Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images
• [cs.CV]Lift-the-Flap: Context Reasoning Using Object-Centered Graphs
• [cs.CV]Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
• [cs.CV]Rethinking Visual Relationships for High-level Image Understanding
• [cs.CV]SensitiveNets: Learning Agnostic Representations with Application to Face Recognition
• [cs.CV]Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
• [cs.CV]US-net for robust and efficient nuclei instance segmentation
• [cs.CY]Contactless Cardiac Arrest Detection Using Smart Devices
• [cs.CY]Race, Ethnicity and National Origin-based Discrimination in Social Media and Hate Crimes Across 100 U.S. Cities
• [cs.DC]Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures
• [cs.DC]Towards Collaborative Intelligence Friendly Architectures for Deep Learning
• [cs.DM]On two-fold packings of radius-1 balls in Hamming graphs
• [cs.DM]Some Enumeration Problems in the Duplication-Loss Model of Genome Rearrangement
• [cs.IR]CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
• [cs.IR]Learning Fast Matching Models from Weak Annotations
• [cs.IR]Sequential Evaluation and Generation Framework for Combinatorial Recommender System
• [cs.IT]An Analysis of State Evolution for Approximate Message Passing with Side Information
• [cs.IT]Cache-aided Interference Management Using Hypercube Combinatorial Cache Designs
• [cs.IT]High-performance quantization for spectral super-resolution
• [cs.IT]On dual codes in the Doob schemes
• [cs.IT]Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks
• [cs.IT]Privacy Against Brute-Force Inference Attacks
• [cs.IT]Private Secure Coded Computation
• [cs.IT]Probability of Error for Detecting a Change in a Parameter, Total Variation of the Posterior Distribution, and Bayesian Fisher Information
• [cs.IT]Sensing-Throughput Tradeoff for Superior Selective Reporting-based Spectrum Sensing in Energy Harvesting HCRNs
• [cs.IT]The Relation Between Bayesian Fisher Information and Shannon Information for Detecting a Change in a Parameter
• [cs.LG]Agnostic Federated Learning
• [cs.LG]An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
• [cs.LG]Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network
• [cs.LG]Compressing GANs using Knowledge Distillation
• [cs.LG]Compressing Gradient Optimizers via Count-Sketches
• [cs.LG]DANTE: Deep AlterNations for Training nEural networks
• [cs.LG]Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
• [cs.LG]Dynamic fairness - Breaking vicious cycles in automatic decision making
• [cs.LG]Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge
• [cs.LG]Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
• [cs.LG]Gaussian Conditional Random Fields for Classification
• [cs.LG]Generalized Sliced Wasserstein Distances
• [cs.LG]Learning Action Representations for Reinforcement Learning
• [cs.LG]Natural and Adversarial Error Detection using Invariance to Image Transformations
• [cs.LG]Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints
• [cs.LG]Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
• [cs.LG]Optimal Adversarial Attack on Autoregressive Models
• [cs.LG]Passed & Spurious: analysing descent algorithms and local minima in spiked matrix-tensor model
• [cs.LG]Policy Consolidation for Continual Reinforcement Learning
• [cs.LG]Privacy Preserving Off-Policy Evaluation
• [cs.LG]TF-Replicator: Distributed Machine Learning for Researchers
• [cs.LG]The Hanabi Challenge: A New Frontier for AI Research
• [cs.LG]Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
• [cs.LG]Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
• [cs.LG]Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
• [cs.LG]Your Gameplay Says it All: Modelling Motivation in Tom Clancy's The Division
• [cs.NE]Fast Re-Optimization via Structural Diversity
• [cs.NE]Parallel Black-Box Complexity with Tail Bounds
• [cs.PL]OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles
• [cs.RO]Active Estimation of 3D Lines in Spherical Coordinates
• [cs.RO]Characterizing Input Methods for Human-to-robot Demonstrations
• [cs.RO]Comparison and Experimental Validation of Predictive Models for Soft, Fiber-Reinforced Actuators
• [cs.RO]Flexible collaborative transportation by a team of rotorcraft
• [cs.RO]Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization
• [cs.RO]Intelligent architectures for robotics: The merging of cognition and emotion
• [cs.RO]Thermal Recovery of Multi-Limbed Robots with Electric Actuators
• [cs.SI]Adaptive Influence Maximization under General Feedback Models
• [cs.SI]Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms
• [cs.SI]Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)
• [econ.GN]Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound
• [eess.IV]SCATGAN for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks
• [eess.IV]Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
• [math.CO]On -resilient
-functions
• [math.PR]Limit theorems for cloning algorithms
• [math.PR]Phase Transition in the Recovery of Rank One Matrices Corrupted by Gaussian Noise
• [math.ST]Bayesian optimality of testing procedures for survival data
• [math.ST]Challenges with EM in application to weakly identifiable mixture models
• [math.ST]Estimation and Clustering in Popularity Adjusted Stochastic Block Model
• [math.ST]Multi-Armed Bandit Problem and Batch UCB Rule
• [math.ST]Non-Markovian Monte Carlo on Directed Graphs
• [math.ST]On the monotonicity of copula-based conditional distributions
• [physics.soc-ph]A surface-depth theory of the emergence of complex networks
• [physics.soc-ph]Predictability of missing links in complex networks
• [q-bio.BM]ProteinNet: a standardized data set for machine learning of protein structure
• [stat.AP]A copula-based measure for quantifying asymmetry in dependence and associations
• [stat.AP]StaTIX - Statistical Type Inference on Linked Data
• [stat.ME]Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
• [stat.ME]D-optimal Design for Network A/B Testing
• [stat.ME]Intuitive principle-based priors for attributing variance in additive model structures
• [stat.ME]Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics
• [stat.ML]Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
• [stat.ML]Combinatorial Bayesian Optimization using Graph Representations
• [stat.ML]Crime Linkage Detection by Spatial-Temporal-Textual Point Processes
• [stat.ML]Multi-level Monte Carlo Variational Inference
• [stat.ML]Signal propagation in continuous approximations of binary neural networks
• [stat.ML]Tree-Sliced Approximation of Wasserstein Distances
• [stat.ML]Understanding MCMC Dynamics as Flows on the Wasserstein Space
·····································
• [astro-ph.IM]Towards Machine-assisted Meta-Studies: The Hubble Constant
Tom Crossland, Pontus Stenetorp, Sebastian Riedel, Daisuke Kawata, Thomas D. Kitching, Rupert A. C. Croft
http://arxiv.org/abs/1902.00027v1
• [cond-mat.stat-mech]Spectral content of a single non-Brownian trajectory
D. Krapf, N. Lukat, E. Marinari, R. Metzler, G. Oshanin, C. Selhuber-Unkel, A. Squarcini, L. Stadler, M. Weiss, X. Xu
http://arxiv.org/abs/1902.00481v1
• [cs.AI]Causal Simulations for Uplift Modeling
Jeroen Berrevoets, Wouter Verbeke
http://arxiv.org/abs/1902.00287v1
• [cs.AI]Learning to Make Analogies by Contrasting Abstract Relational Structure
Felix Hill, Adam Santoro, David G. T. Barrett, Ari S. Morcos, Timothy Lillicrap
http://arxiv.org/abs/1902.00120v1
• [cs.AI]The Second Conversational Intelligence Challenge (ConvAI2)
Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W Black, Alexander Rudnicky, Jason Williams, Joelle Pineau, Mikhail Burtsev, Jason Weston
http://arxiv.org/abs/1902.00098v1
• [cs.CL]A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings
Wei Yang, Wei Lu, Vincent W. Zheng
http://arxiv.org/abs/1902.00184v1
• [cs.CL]DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi, Claire Cardie
http://arxiv.org/abs/1902.00164v1
• [cs.CL]Dating Documents using Graph Convolution Networks
Shikhar Vashishth, Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar
http://arxiv.org/abs/1902.00175v1
• [cs.CL]Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding
A. Mishra, H. Mishra, S. Rathee
http://arxiv.org/abs/1902.00496v1
• [cs.CL]How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions
Goran Glavas, Robert Litschko, Sebastian Ruder, Ivan Vulic
http://arxiv.org/abs/1902.00508v1
• [cs.CL]Human acceptability judgements for extractive sentence compression
Abram Handler, Brian Dillon, Brendan O'Connor
http://arxiv.org/abs/1902.00489v1
• [cs.CL]Joint Entity Linking with Deep Reinforcement Learning
Zheng Fang, Yanan Cao, Dongjie Zhang, Qian Li, Zhenyu Zhang, Yanbing Liu
http://arxiv.org/abs/1902.00330v1
• [cs.CL]Multilingual NER Transfer for Low-resource Languages
Afshin Rahimi, Yuan Li, Trevor Cohn
http://arxiv.org/abs/1902.00193v1
• [cs.CL]Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models
Dinghan Shen, Asli Celikyilmaz, Yizhe Zhang, Liqun Chen, Xin Wang, Jianfeng Gao, Lawrence Carin
http://arxiv.org/abs/1902.00154v1
• [cs.CL]tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
Blaž Škrlj, Matej Martinc, Jan Kralj, Nada Lavrač, Senja Pollak
http://arxiv.org/abs/1902.00438v1
• [cs.CV]A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids
Qiuyu Zhu, Ruixin Zhang
http://arxiv.org/abs/1902.00220v1
• [cs.CV]BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection
Hedi Ben-younes, Rémi Cadene, Nicolas Thome, Matthieu Cord
http://arxiv.org/abs/1902.00038v1
• [cs.CV]ColorNet: Investigating the importance of color spaces for image classification
Shreyank N Gowda, Chun Yuan
http://arxiv.org/abs/1902.00267v1
• [cs.CV]Dataset Culling: Towards Efficient Training Of Distillation-Based Domain Specific Models
Kentaro Yoshioka, Edward Lee, Simon Wong, Mark Horowitz
http://arxiv.org/abs/1902.00173v1
• [cs.CV]Deep Hyperspectral Prior: Denoising, Inpainting, Super-Resolution
Oleksii Sidorov, Jon Yngve Hardeberg
http://arxiv.org/abs/1902.00301v1
• [cs.CV]Deep Learning Solutions for TanDEM-X-based Forest Classification
Antonio Mazza, Francescopaolo Sica
http://arxiv.org/abs/1902.00274v1
• [cs.CV]Deep Triplet Quantization
Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, Jingdong Wang
http://arxiv.org/abs/1902.00153v1
• [cs.CV]Do we train on test data? Purging CIFAR of near-duplicates
Björn Barz, Joachim Denzler
http://arxiv.org/abs/1902.00423v1
• [cs.CV]End-to-end Lane Detection through Differentiable Least-Squares Fitting
Bert De Brabandere, Wouter Van Gansbeke, Davy Neven, Marc Proesmans, Luc Van Gool
http://arxiv.org/abs/1902.00293v1
• [cs.CV]Episodic Training for Domain Generalization
Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, Timothy M. Hospedales
http://arxiv.org/abs/1902.00113v1
• [cs.CV]Fast and Optimal Laplacian Solver for Gradient-Domain Image Editing using Green Function Convolution
Dominique Beaini, Sofiane Achiche, Fabrice Nonez, Olivier Brochu Dufour, Cédric Leblond-Ménard, Mahdis Asaadi, Maxime Raison
http://arxiv.org/abs/1902.00176v1
• [cs.CV]Generative Smoke Removal
Oleksii Sidorov, Congcong Wang, Faouzi Alaya Cheikh
http://arxiv.org/abs/1902.00311v1
• [cs.CV]Learnable Embedding Space for Efficient Neural Architecture Compression
Shengcao Cao, Xiaofang Wang, Kris M. Kitani
http://arxiv.org/abs/1902.00383v1
• [cs.CV]Learning Differentiable Grammars for Continuous Data
AJ Piergiovanni, Anelia Angelova, Michael S. Ryoo
http://arxiv.org/abs/1902.00505v1
• [cs.CV]Learning Metric Graphs for Neuron Segmentation In Electron Microscopy Images
Kyle Luther, H. Sebastian Seung
http://arxiv.org/abs/1902.00100v1
• [cs.CV]Lift-the-Flap: Context Reasoning Using Object-Centered Graphs
Mengmi Zhang, Jiashi Feng, Karla Montejo, Joseph Kwon, Joo Hwee Lim, Gabriel Kreiman
http://arxiv.org/abs/1902.00163v1
• [cs.CV]Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation
Christoph Angermann, Markus Haltmeier, Ruth Steiger, Sergiy Pereverzyev Jr, Elke Gizewski
http://arxiv.org/abs/1902.00347v1
• [cs.CV]Rethinking Visual Relationships for High-level Image Understanding
Yuanzhi Liang, Yalong Bai, Wei Zhang, Xueming Qian, Li Zhu, Tao Mei
http://arxiv.org/abs/1902.00313v1
• [cs.CV]SensitiveNets: Learning Agnostic Representations with Application to Face Recognition
Aythami Morales, Julian Fierrez, Ruben Vera-Rodriguez
http://arxiv.org/abs/1902.00334v1
• [cs.CV]Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus
Dongfang Yang, Linhui Li, Keith Redmill, Ümit Özgüner
http://arxiv.org/abs/1902.00487v1
• [cs.CV]US-net for robust and efficient nuclei instance segmentation
Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro, Qianni Zhang
http://arxiv.org/abs/1902.00125v1
• [cs.CY]Contactless Cardiac Arrest Detection Using Smart Devices
Justin Chan, Thomas Rea, Shyamnath Gollakota, Jacob E. Sunshine
http://arxiv.org/abs/1902.00062v1
• [cs.CY]Race, Ethnicity and National Origin-based Discrimination in Social Media and Hate Crimes Across 100 U.S. Cities
Kunal Relia, Zhengyi Li, Stephanie H. Cook, Rumi Chunara
http://arxiv.org/abs/1902.00119v1
• [cs.DC]Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures
Artem Lutov, Mourad Khayati, Philippe Cudré-Mauroux
http://arxiv.org/abs/1902.00475v1
• [cs.DC]Towards Collaborative Intelligence Friendly Architectures for Deep Learning
Amir Erfan Eshratifar, Amirhossein Esmaili, Massoud Pedram
http://arxiv.org/abs/1902.00147v1
• [cs.DM]On two-fold packings of radius-1 balls in Hamming graphs
Denis S. Krotov, Vladimir N. Potapov
http://arxiv.org/abs/1902.00023v1
• [cs.DM]Some Enumeration Problems in the Duplication-Loss Model of Genome Rearrangement
Mladen Kovačević, Sanja Brdar, Vladimir Crnojević
http://arxiv.org/abs/1902.00230v1
• [cs.IR]CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
Shikhar Vashishth, Prince Jain, Partha Talukdar
http://arxiv.org/abs/1902.00172v1
• [cs.IR]Learning Fast Matching Models from Weak Annotations
Xue Li, Zhipeng Luo, Hao Sun, Jianjin Zhang, Weihao Han, Xianqi Chu, Liangjie Zhang, Qi Zhang
http://arxiv.org/abs/1901.10710v2
• [cs.IR]Sequential Evaluation and Generation Framework for Combinatorial Recommender System
Fan Wang, Xiaomin Fan, Lihang Liu, Yaxue Chen, Jiucheng Tao, Zhiming Peng, Cihang Jin, Hao Tian
http://arxiv.org/abs/1902.00245v1
• [cs.IT]An Analysis of State Evolution for Approximate Message Passing with Side Information
Hangjin Liu, Cynthia Rush, Dror Baron
http://arxiv.org/abs/1902.00150v1
• [cs.IT]Cache-aided Interference Management Using Hypercube Combinatorial Cache Designs
Xiang Zhang, Mingyue Ji
http://arxiv.org/abs/1902.00135v1
• [cs.IT]High-performance quantization for spectral super-resolution
C. Sinan Güntürk, Weilin Li
http://arxiv.org/abs/1902.00131v1
• [cs.IT]On dual codes in the Doob schemes
Denis S. Krotov
http://arxiv.org/abs/1902.00020v1
• [cs.IT]Opportunistic Ambient Backscatter Communication in RF-Powered Cognitive Radio Networks
Rajalekshmi Kishore, Sanjeev Gurugopinath, Paschalis C. Sofotasios, Sami Muhaidat, Naofal Al-Dhahir
http://arxiv.org/abs/1902.00332v1
• [cs.IT]Privacy Against Brute-Force Inference Attacks
Seyed Ali Osia, Borzoo Rassouli, Hamed Haddadi, Hamid R. Rabiee, Deniz Gündüz
http://arxiv.org/abs/1902.00329v1
• [cs.IT]Private Secure Coded Computation
Minchul Kim, Jungwoo Lee
http://arxiv.org/abs/1902.00167v1
• [cs.IT]Probability of Error for Detecting a Change in a Parameter, Total Variation of the Posterior Distribution, and Bayesian Fisher Information
Eric Clarkson
http://arxiv.org/abs/1902.00099v1
• [cs.IT]Sensing-Throughput Tradeoff for Superior Selective Reporting-based Spectrum Sensing in Energy Harvesting HCRNs
Rajalekshmi Kishore, Sanjeev Gurugopinath, Sami Muhaidat, Paschalis C. Sofotasios, Octavia A. Dobre, Naofal Al-Dhahir
http://arxiv.org/abs/1902.00373v1
• [cs.IT]The Relation Between Bayesian Fisher Information and Shannon Information for Detecting a Change in a Parameter
Eric Clarkson
http://arxiv.org/abs/1902.00103v1
• [cs.LG]Agnostic Federated Learning
Mehryar Mohri, Gary Sivek, Ananda Theertha Suresh
http://arxiv.org/abs/1902.00146v1
• [cs.LG]An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
Tor Lattimore, Csaba Szepesvari
http://arxiv.org/abs/1902.00470v1
• [cs.LG]Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network
Ruihuan Du, Zhong Yu, Harikesh Nair, Bo Cui, Ruyang Shou
http://arxiv.org/abs/1902.00215v1
• [cs.LG]Compressing GANs using Knowledge Distillation
Angeline Aguinaldo, Ping-Yeh Chiang, Alex Gain, Ameya Patil, Kolten Pearson, Soheil Feizi
http://arxiv.org/abs/1902.00159v1
• [cs.LG]Compressing Gradient Optimizers via Count-Sketches
Ryan Spring, Anastasios Kyrillidis, Vijai Mohan, Anshumali Shrivastava
http://arxiv.org/abs/1902.00179v1
• [cs.LG]DANTE: Deep AlterNations for Training nEural networks
Sneha Kudugunta, Vaibhav B Sinha, Adepu Ravi Sankar, Surya Teja Chavali, Purushottam Kar, Vineeth N Balasubramanian
http://arxiv.org/abs/1902.00491v1
• [cs.LG]Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasia Koloskova, Sebastian U. Stich, Martin Jaggi
http://arxiv.org/abs/1902.00340v1
• [cs.LG]Dynamic fairness - Breaking vicious cycles in automatic decision making
Benjamin Paaßen, Astrid Bunge, Carolin Hainke, Leon Sindelar, Matthias Vogelsang
http://arxiv.org/abs/1902.00375v1
• [cs.LG]Efficient Hybrid Network Architectures for Extremely Quantized Neural Networks Enabling Intelligence at the Edge
Indranil Chakraborty, Deboleena Roy, Aayush Ankit, Kaushik Roy
http://arxiv.org/abs/1902.00460v1
• [cs.LG]Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel
http://arxiv.org/abs/1902.00275v1
• [cs.LG]Gaussian Conditional Random Fields for Classification
Andrija Petrović, Mladen Nikolić, Miloš Jovanović, Boris Delibašić
http://arxiv.org/abs/1902.00045v1
• [cs.LG]Generalized Sliced Wasserstein Distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde
http://arxiv.org/abs/1902.00434v1
• [cs.LG]Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas, Scott Jordan, Philip S. Thomas
http://arxiv.org/abs/1902.00183v1
• [cs.LG]Natural and Adversarial Error Detection using Invariance to Image Transformations
Yuval Bahat, Michal Irani, Gregory Shakhnarovich
http://arxiv.org/abs/1902.00236v1
• [cs.LG]Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints
Dimche Kostadinov, Behrooz Razdehi, Slava Voloshynovskiy
http://arxiv.org/abs/1902.00016v1
• [cs.LG]Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation
Yogesh Balaji, Rama Chellappa, Soheil Feizi
http://arxiv.org/abs/1902.00415v1
• [cs.LG]Optimal Adversarial Attack on Autoregressive Models
Yiding Chen, Xiaojin Zhu
http://arxiv.org/abs/1902.00202v1
• [cs.LG]Passed & Spurious: analysing descent algorithms and local minima in spiked matrix-tensor model
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
http://arxiv.org/abs/1902.00139v1
• [cs.LG]Policy Consolidation for Continual Reinforcement Learning
Christos Kaplanis, Murray Shanahan, Claudia Clopath
http://arxiv.org/abs/1902.00255v1
• [cs.LG]Privacy Preserving Off-Policy Evaluation
Tengyang Xie, Philip S. Thomas, Gerome Miklau
http://arxiv.org/abs/1902.00174v1
• [cs.LG]TF-Replicator: Distributed Machine Learning for Researchers
Peter Buchlovsky, David Budden, Dominik Grewe, Chris Jones, John Aslanides, Frederic Besse, Andy Brock, Aidan Clark, Sergio Gómez Colmenarejo, Aedan Pope, Fabio Viola, Dan Belov
http://arxiv.org/abs/1902.00465v1
• [cs.LG]The Hanabi Challenge: A New Frontier for AI Research
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling
http://arxiv.org/abs/1902.00506v1
• [cs.LG]Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders
Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar
http://arxiv.org/abs/1902.00450v1
• [cs.LG]Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning
Kyungjae Lee, Sungyub Kim, Sungbin Lim, Sungjoon Choi, Songhwai Oh
http://arxiv.org/abs/1902.00137v1
• [cs.LG]Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation
Sahil Singla, Eric Wallace, Shi Feng, Soheil Feizi
http://arxiv.org/abs/1902.00407v1
• [cs.LG]Your Gameplay Says it All: Modelling Motivation in Tom Clancy's The Division
David Melhart, Ahmad Azadvar, Alessandro Canossa, Antonios Liapis, Georgios N. Yannakakis
http://arxiv.org/abs/1902.00040v1
• [cs.NE]Fast Re-Optimization via Structural Diversity
Benjamin Doerr, Carola Doerr, Frank Neumann
http://arxiv.org/abs/1902.00304v1
• [cs.NE]Parallel Black-Box Complexity with Tail Bounds
Per Kristian Lehre, Dirk Sudholt
http://arxiv.org/abs/1902.00107v1
• [cs.PL]OODIDA: On-board/Off-board Distributed Data Analytics for Connected Vehicles
Gregor Ulm, Emil Gustavsson, Mats Jirstrand
http://arxiv.org/abs/1902.00319v1
• [cs.RO]Active Estimation of 3D Lines in Spherical Coordinates
André Mateus, Omar Tahri, Pedro Miraldo
http://arxiv.org/abs/1902.00473v1
• [cs.RO]Characterizing Input Methods for Human-to-robot Demonstrations
Pragathi Praveena, Guru Subramani, Bilge Mutlu, Michael Gleicher
http://arxiv.org/abs/1902.00084v1
• [cs.RO]Comparison and Experimental Validation of Predictive Models for Soft, Fiber-Reinforced Actuators
Audrey Sedal, Alan Wineman, R Brent Gillespie, C David Remy
http://arxiv.org/abs/1902.00054v1
• [cs.RO]Flexible collaborative transportation by a team of rotorcraft
Hector Garcia de Marina, Ewoud Smeur
http://arxiv.org/abs/1902.00279v1
• [cs.RO]Geometric interpretation of the general POE model for a serial-link robot via conversion into D-H parameterization
Liao Wu, Ross Crawford, Jonathan Roberts
http://arxiv.org/abs/1902.00198v1
• [cs.RO]Intelligent architectures for robotics: The merging of cognition and emotion
Luiz Pessoa
http://arxiv.org/abs/1902.00363v1
• [cs.RO]Thermal Recovery of Multi-Limbed Robots with Electric Actuators
Steven Jens Jorgensen, James Holley, Frank Mathis, Luis Sentis
http://arxiv.org/abs/1902.00187v1
• [cs.SI]Adaptive Influence Maximization under General Feedback Models
Guangmo Tong
http://arxiv.org/abs/1902.00192v1
• [cs.SI]Perils and Challenges of Social Media and Election Manipulation Analysis: The 2018 US Midterms
Ashok Deb, Luca Luceri, Adam Badawy, Emilio Ferrara
http://arxiv.org/abs/1902.00043v1
• [cs.SI]Social Network Based Substance Abuse Prevention via Network Modification (A Preliminary Study)
Aida Rahmattalabi, Anamika Barman Adhikari, Phebe Vayanos, Milind Tambe, Eric Rice, Robin Baker
http://arxiv.org/abs/1902.00171v1
• [econ.GN]Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound
Morteza Taiebat, Samuel Stolper, Ming Xu
http://arxiv.org/abs/1902.00382v1
• [eess.IV]SCATGAN for Reconstruction of Ultrasound Scatterers Using Generative Adversarial Networks
Andrawes Al Bahou, Christine Tanner, Orcun Goksel
http://arxiv.org/abs/1902.00469v1
• [eess.IV]Scalable Learning-Based Sampling Optimization for Compressive Dynamic MRI
Thomas Sanchez, Baran Gözcü, Ruud B. van Heeswijk, Efe Ilıcak, Tolga Çukur, and Volkan Cevher
http://arxiv.org/abs/1902.00386v1
• [math.CO]On -resilient
-functions
Denis S. Krotov
http://arxiv.org/abs/1902.00022v1
• [math.PR]Limit theorems for cloning algorithms
Letizia Angeli, Stefan Grosskinsky, Adam M. Johansen
http://arxiv.org/abs/1902.00509v1
• [math.PR]Phase Transition in the Recovery of Rank One Matrices Corrupted by Gaussian Noise
Enrico Au-Yeung, Greg Zanotti
http://arxiv.org/abs/1902.00104v1
• [math.ST]Bayesian optimality of testing procedures for survival data
Andrea Arfé, Brian Alexander, Lorenzo Trippa
http://arxiv.org/abs/1902.00161v1
• [math.ST]Challenges with EM in application to weakly identifiable mixture models
Raaz Dwivedi, Nhat Ho, Koulik Khamaru, Martin J. Wainwright, Michael I. Jordan, Bin Yu
http://arxiv.org/abs/1902.00194v1
• [math.ST]Estimation and Clustering in Popularity Adjusted Stochastic Block Model
Majid Noroozi, Ramchandra Rimal, Marianna Pensky
http://arxiv.org/abs/1902.00431v1
• [math.ST]Multi-Armed Bandit Problem and Batch UCB Rule
Alexander Kolnogorov, Sergey Garbar
http://arxiv.org/abs/1902.00214v1
• [math.ST]Non-Markovian Monte Carlo on Directed Graphs
Chul-Ho Lee, Min Kang, Do Young Eun
http://arxiv.org/abs/1902.00180v1
• [math.ST]On the monotonicity of copula-based conditional distributions
Bouchra R. Nasri, Bruno N. Remillard
http://arxiv.org/abs/1902.00050v1
• [physics.soc-ph]A surface-depth theory of the emergence of complex networks
Keith M. Smith
http://arxiv.org/abs/1902.00336v1
• [physics.soc-ph]Predictability of missing links in complex networks
Guillermo García-Pérez, Roya Aliakbarisani, Abdorasoul Ghasemi, M. Ángeles Serrano
http://arxiv.org/abs/1902.00035v1
• [q-bio.BM]ProteinNet: a standardized data set for machine learning of protein structure
Mohammed AlQuraishi
http://arxiv.org/abs/1902.00249v1
• [stat.AP]A copula-based measure for quantifying asymmetry in dependence and associations
Robert R. Junker, Florian Griessenberger, Wolfgang Trutschnig
http://arxiv.org/abs/1902.00203v1
• [stat.AP]StaTIX - Statistical Type Inference on Linked Data
Artem Lutov, Soheil Roshankish, Mourad Khayati, Philippe Cudré-Mauroux
http://arxiv.org/abs/1902.00490v1
• [stat.ME]Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits
Martin J. Zhang, James Zou, David Tse
http://arxiv.org/abs/1902.00197v1
• [stat.ME]D-optimal Design for Network A/B Testing
Victoria Pokhiko, Qiong Zhang, Lulu Kang, D'arcy P. Mays
http://arxiv.org/abs/1902.00482v1
• [stat.ME]Intuitive principle-based priors for attributing variance in additive model structures
Geir-Arne Fuglstad, Ingeborg Gullikstad Hem, Alexander Knight, Håvard Rue, Andrea Riebler
http://arxiv.org/abs/1902.00242v1
• [stat.ME]Using tours to visually investigate properties of new projection pursuit indexes with application to problems in physics
Ursula Laa, Dianne Cook
http://arxiv.org/abs/1902.00181v1
• [stat.ML]Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling
Chuan Lu, Xueyu Zhu
http://arxiv.org/abs/1902.00148v1
• [stat.ML]Combinatorial Bayesian Optimization using Graph Representations
Changyong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling
http://arxiv.org/abs/1902.00448v1
• [stat.ML]Crime Linkage Detection by Spatial-Temporal-Textual Point Processes
Shixiang Zhu, Yao Xie
http://arxiv.org/abs/1902.00440v1
• [stat.ML]Multi-level Monte Carlo Variational Inference
Masahiro Fujisawa, Issei Sato
http://arxiv.org/abs/1902.00468v1
• [stat.ML]Signal propagation in continuous approximations of binary neural networks
George Stamatescu, Federica Gerace, Carlo Lucibello, Ian Fuss, Langford B. White
http://arxiv.org/abs/1902.00177v1
• [stat.ML]Tree-Sliced Approximation of Wasserstein Distances
Tam Le, Makoto Yamada, Kenji Fukumizu, Marco Cuturi
http://arxiv.org/abs/1902.00342v1
• [stat.ML]Understanding MCMC Dynamics as Flows on the Wasserstein Space
Chang Liu, Jingwei Zhuo, Jun Zhu
http://arxiv.org/abs/1902.00282v1
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