cond-mat.dis-nn - 无序系统与神经网络
cs.AI - 人工智能
cs.CG - 计算几何学
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.FL - 形式语言与自动机理论
cs.HC - 人机接口
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.NE - 神经与进化计算
cs.NI - 网络和互联网体系结构
cs.RO - 机器人学
cs.SE - 软件工程
cs.SI - 社交网络与信息网络
eess.SP - 信号处理
math.CO - 组合数学
math.OC - 优化与控制
math.PR - 概率
math.ST - 统计理论
physics.soc-ph - 物理学与社会
stat.AP - 应用统计
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [cond-mat.dis-nn]Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices
• [cs.AI]The Limits of Morality in Strategic Games
• [cs.AI]The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
• [cs.AI]When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?
• [cs.CG]Greedy Strategy Works for Clustering with Outliers and Coresets Construction
• [cs.CG]Spherical sampling methods for the calculation of metamer mismatch volumes
• [cs.CL]A Question-Entailment Approach to Question Answering
• [cs.CL]A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data
• [cs.CL]A review of sentiment computation methods with R packages
• [cs.CL]DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets
• [cs.CL]Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification
• [cs.CL]FANDA: A Novel Approach to Perform Follow-up Query Analysis
• [cs.CL]Product-Aware Answer Generation in E-Commerce Question-Answering
• [cs.CL]Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks
• [cs.CL]Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
• [cs.CL]TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
• [cs.CR]Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
• [cs.CV]3D Backbone Network for 3D Object Detection
• [cs.CV]A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation
• [cs.CV]A PCB Dataset for Defects Detection and Classification
• [cs.CV]Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
• [cs.CV]Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
• [cs.CV]CT synthesis from MR images for orthopedic applications in the lower arm using a conditional generative adversarial network
• [cs.CV]Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?
• [cs.CV]Correcting rural building annotations in OpenStreetMap using convolutional neural networks
• [cs.CV]DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
• [cs.CV]Deep Reasoning with Multi-scale Context for Salient Object Detection
• [cs.CV]Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
• [cs.CV]Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
• [cs.CV]Learning Disentangled Representations with Reference-Based Variational Autoencoders
• [cs.CV]MREAK : Morphological Retina Keypoint Descriptor
• [cs.CV]Object Detection based on Region Decomposition and Assembly
• [cs.CV]Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks
• [cs.CV]Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification
• [cs.CV]Semantic Matching by Weakly Supervised 2D Point Set Registration
• [cs.CV]Semi-Supervised Image-to-Image Translation
• [cs.CV]Semi-Supervised Semantic Matching
• [cs.CV]Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
• [cs.CV]Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
• [cs.CV]Unsupervised Image-to-Image Translation with Self-Attention Networks
• [cs.CV]Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
• [cs.CV]Visualizing Topographic Independent Component Analysis with Movies
• [cs.CV]Whole slide image registration for the study of tumor heterogeneity
• [cs.CY]Forecasting Transformative AI: An Expert Survey
• [cs.DC]A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
• [cs.DC]Asynchronous Decentralized Optimization in Directed Networks
• [cs.DC]Cloud BI: Future of Business Intelligence in the Cloud
• [cs.DC]DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching
• [cs.DC]Fundamental Limits of Approximate Gradient Coding
• [cs.DC]Mokka: RSM for open networks
• [cs.FL]A model for a Lindenmayer reconstruction algorithm
• [cs.HC]Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
• [cs.HC]Teaching robots to imitate a human with no on-teacher sensors. What are the key challenges?
• [cs.IR]Hybrid NER System for Multi-Source Offer Feeds
• [cs.IR]Neural IR Meets Graph Embedding: A Ranking Model for Product Search
• [cs.IR]Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)
• [cs.IR]Sequential Skip Prediction with Few-shot in Streamed Music Contents
• [cs.IT]A Systematic Construction of MDS Codes with Small Sub-packetization Level and Near Optimal Repair Bandwidth
• [cs.IT]Dispensing with Noise Forward in the "Weak" Relay-Eavesdropper Channel
• [cs.IT]End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
• [cs.IT]Explicit Polar Codes with Small Scaling Exponent
• [cs.IT]Homomorphic Sensing
• [cs.IT]Multi-Frequency Phase Synchronization
• [cs.IT]Real-Time Reconstruction of Counting Process through Queues
• [cs.IT]Recovery of Structured Signals From Corrupted Non-Linear Measurements
• [cs.IT]Using Erasure Feedback for Online Timely Updating with an Energy Harvesting Sensor
• [cs.IT]What Can Machine Learning Teach Us about Communications?
• [cs.LG]Adversarial Variational Inference and Learning in Markov Random Fields
• [cs.LG]Causal Reasoning from Meta-reinforcement Learning
• [cs.LG]Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
• [cs.LG]Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
• [cs.LG]Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
• [cs.LG]Context Prediction for Unsupervised Deep Learning on Point Clouds
• [cs.LG]Cooperative Online Learning: Keeping your Neighbors Updated
• [cs.LG]Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
• [cs.LG]Decoupled Greedy Learning of CNNs
• [cs.LG]Deep Generative Learning via Variational Gradient Flow
• [cs.LG]Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
• [cs.LG]Distillation Strategies for Proximal Policy Optimization
• [cs.LG]Fairness with Dynamics
• [cs.LG]Federated Reinforcement Learning
• [cs.LG]Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
• [cs.LG]Generating and Aligning from Data Geometries with Generative Adversarial Networks
• [cs.LG]Graph heat mixture model learning
• [cs.LG]Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
• [cs.LG]High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding
• [cs.LG]Hypergraph Convolution and Hypergraph Attention
• [cs.LG]ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
• [cs.LG]Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records
• [cs.LG]Large-Batch Training for LSTM and Beyond
• [cs.LG]Learning Interpretable Models with Causal Guarantees
• [cs.LG]Learning Neurosymbolic Generative Models via Program Synthesis
• [cs.LG]Learning Sublinear-Time Indexing for Nearest Neighbor Search
• [cs.LG]Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
• [cs.LG]Learning to compress and search visual data in large-scale systems
• [cs.LG]Location reference identification from tweets during emergencies: A deep learning approach
• [cs.LG]Loss Landscapes of Regularized Linear Autoencoders
• [cs.LG]Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review
• [cs.LG]Maximum Entropy Generators for Energy-Based Models
• [cs.LG]Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
• [cs.LG]Meta-Learning for Contextual Bandit Exploration
• [cs.LG]Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
• [cs.LG]On the Transformation of Latent Space in Autoencoders
• [cs.LG]Open-ended Learning in Symmetric Zero-sum Games
• [cs.LG]PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
• [cs.LG]Provable Smoothness Guarantees for Black-Box Variational Inference
• [cs.LG]Recovering Pairwise Interactions Using Neural Networks
• [cs.LG]Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory
• [cs.LG]Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems
• [cs.LG]Sitatapatra: Blocking the Transfer of Adversarial Samples
• [cs.LG]Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
• [cs.LG]Theoretically Principled Trade-off between Robustness and Accuracy
• [cs.LG]Traditional and Heavy-Tailed Self Regularization in Neural Network Models
• [cs.LG]Trajectory Normalized Gradients for Distributed Optimization
• [cs.LG]Width Provably Matters in Optimization for Deep Linear Neural Networks
• [cs.NE]QGAN: Quantized Generative Adversarial Networks
• [cs.NE]Really should we pruning after model be totally trained? Pruning based on a small amount of training
• [cs.NI]A stack-vector routing protocol for automatic tunneling
• [cs.NI]When Machine Learning Meets Big Data: A Wireless Communication Perspective
• [cs.RO]Decentralization of Multiagent Policies by Learning What to Communicate
• [cs.RO]Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots
• [cs.RO]Dynamic Locomotion For Passive-Ankle Biped Robots And Humanoids Using Whole-Body Locomotion Control
• [cs.RO]F1/10: An Open-Source Autonomous Cyber-Physical Platform
• [cs.RO]MPC for Humanoid Gait Generation: Stability and Feasibility
• [cs.RO]Mixed-Granularity Human-Swarm Interaction
• [cs.RO]Sequential path planning for a formation of mobile robots with split and merge
• [cs.RO]Vision-based Obstacle Removal System for Autonomous Ground Vehicles Using a Robotic Arm
• [cs.SE]Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction
• [cs.SI]Emotion Detection and Analysis on Social Media
• [eess.SP]Intersymbol and Intercarrier Interference in OFDM Transmissions through Highly Dispersive Channels
• [math.CO]On an open problem about a class of optimal ternary cyclic codes
• [math.OC]A Fully Stochastic Primal-Dual Algorithm
• [math.OC]A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
• [math.OC]Curvature-Exploiting Acceleration of Elastic Net Computations
• [math.OC]Model Function Based Conditional Gradient Method with Armijo-like Line Search
• [math.PR]Breaking Bivariate Records
• [math.PR]Stick-breaking processes, clumping, and Markov chain occupation laws
• [math.ST]Consistent nonparametric change point detection combining CUSUM and marked empirical processes
• [math.ST]Detecting Changes in Hidden Markov Models
• [math.ST]Optimal Nonparametric Inference under Quantization
• [math.ST]Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation
• [math.ST]Raking-ratio empirical process with auxiliary information learning
• [math.ST]Testing Equality of Autocovariance Operators for Functional Time Series
• [physics.soc-ph]Emergence of leader-follower hierarchy among players in an on-line experiment
• [physics.soc-ph]Reentrant phase transitions in threshold driven contagion on multiplex networks
• [stat.AP]Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors
• [stat.AP]Modelling the Demand and Uncertainty of Low Voltage Networks and the Effect of non-Domestic Consumers
• [stat.AP]Spatial Modeling of Trends in Crime over Time in Philadelphia
• [stat.ME]A new integrated likelihood for estimating population size in dependent dual-record system
• [stat.ME]Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models
• [stat.ME]New Exploratory Tools for Extremal Dependence: Chi Networks and Annual Extremal Networks
• [stat.ME]Seamless phase II/III clinical trials using early outcomes for treatment or subgroup selection: Methods and aspects of their implementation
• [stat.ML]A Review on Quantile Regression for Stochastic Computer Experiments
• [stat.ML]A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization
• [stat.ML]Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data
• [stat.ML]Deep Mean Functions for Meta-Learning in Gaussian Processes
• [stat.ML]General Supervision via Probabilistic Transformations
• [stat.ML]Large dimensional analysis of general margin based classification methods
• [stat.ML]Multi-fidelity Bayesian Optimization with Max-value Entropy Search
• [stat.ML]On Local Optimizers of Acquisition Functions in Bayesian Optimization
• [stat.ML]Overcomplete Independent Component Analysis via SDP
• [stat.ML]Pretending Fair Decisions via Stealthily Biased Sampling
• [stat.ML]Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
• [stat.ML]Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels
• [stat.ML]Three principles of data science: predictability, computability, and stability (PCS)
·····································
• [cond-mat.dis-nn]Convergent Dynamics for Solving the TAP Equations of Ising Models with Arbitrary Rotation Invariant Coupling Matrices
Burak Çakmak, Manfred Opper
http://arxiv.org/abs/1901.08583v1
• [cs.AI]The Limits of Morality in Strategic Games
Rui Cao, Pavel Naumov
http://arxiv.org/abs/1901.08467v1
• [cs.AI]The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition
Diego Perez-Liebana, Katja Hofmann, Sharada Prasanna Mohanty, Noburu Kuno, Andre Kramer, Sam Devlin, Raluca D. Gaina, Daniel Ionita
http://arxiv.org/abs/1901.08129v1
• [cs.AI]When is it right and good for an intelligent autonomous vehicle to take over control (and hand it back)?
Ajit Narayanan
http://arxiv.org/abs/1901.08221v1
• [cs.CG]Greedy Strategy Works for Clustering with Outliers and Coresets Construction
Hu Ding
http://arxiv.org/abs/1901.08219v1
• [cs.CG]Spherical sampling methods for the calculation of metamer mismatch volumes
Michal Mackiewicz, Hans Jakob Rivertz, Graham D. Finlayson
http://arxiv.org/abs/1901.08419v1
• [cs.CL]A Question-Entailment Approach to Question Answering
Asma Ben Abacha, Dina Demner-Fushman
http://arxiv.org/abs/1901.08079v1
• [cs.CL]A Tool for Spatio-Temporal Analysis of Social Anxiety with Twitter Data
Joohong Lee, Dongyoung Son, Yong Suk Choi
http://arxiv.org/abs/1901.08158v1
• [cs.CL]A review of sentiment computation methods with R packages
Maurizio Naldi
http://arxiv.org/abs/1901.08319v1
• [cs.CL]DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets
Canwen Xu, Jing Li, Xiangyang Luo, Jiaxin Pei, Chenliang Li, Donghong Ji
http://arxiv.org/abs/1901.07005v2
• [cs.CL]Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification
Xia Yuan, Liao xiaoli, Li Shilei, Shi Qinwen, Wu Jinfa, Li Ke
http://arxiv.org/abs/1901.08351v1
• [cs.CL]FANDA: A Novel Approach to Perform Follow-up Query Analysis
Qian Liu, Bei Chen, Jian-Guang Lou, Ge Jin, Dongmei Zhang
http://arxiv.org/abs/1901.08259v1
• [cs.CL]Product-Aware Answer Generation in E-Commerce Question-Answering
Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan
http://arxiv.org/abs/1901.07696v2
• [cs.CL]Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks
Paul Azunre, Craig Corcoran, Numa Dhamani, Jeffrey Gleason, Garrett Honke, David Sullivan, Rebecca Ruppel, Sandeep Verma, Jonathon Morgan
http://arxiv.org/abs/1901.08456v1
• [cs.CL]Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing
Joohong Lee, Sangwoo Seo, Yong Suk Choi
http://arxiv.org/abs/1901.08163v1
• [cs.CL]TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Thomas Wolf, Victor Sanh, Julien Chaumond, Clement Delangue
http://arxiv.org/abs/1901.08149v1
• [cs.CR]Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework
He Zhang, Xingrui Yu, Peng Ren, Chunbo Luo, Geyong Min
http://arxiv.org/abs/1901.07949v2
• [cs.CV]3D Backbone Network for 3D Object Detection
Xuesong Li, Jose E Guivant, Ngaiming Kwok, Yongzhi Xu
http://arxiv.org/abs/1901.08373v1
• [cs.CV]A Novel Self-Intersection Penalty Term for Statistical Body Shape Models and Its Applications in 3D Pose Estimation
Zaiqiang Wu, Wei Jiang, Hao Luo, Lin Cheng
http://arxiv.org/abs/1901.08274v1
• [cs.CV]A PCB Dataset for Defects Detection and Classification
Weibo Huang, Peng Wei
http://arxiv.org/abs/1901.08204v1
• [cs.CV]Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey
Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy
http://arxiv.org/abs/1901.08292v1
• [cs.CV]Application of Decision Rules for Handling Class Imbalance in Semantic Segmentation
Robin Chan, Matthias Rottmann, Fabian Hüger, Peter Schlicht, Hanno Gottschalk
http://arxiv.org/abs/1901.08394v1
• [cs.CV]CT synthesis from MR images for orthopedic applications in the lower arm using a conditional generative adversarial network
Frank Zijlstra, Koen Willemsen, Mateusz C. Florkow, Ralph J. B. Sakkers, Harrie H. Weinans, Bart C. H. van der Wal, Marijn van Stralen, Peter R. Seevinck
http://arxiv.org/abs/1901.08449v1
• [cs.CV]Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect?
Federico Fulgeri, Matteo Fabbri, Stefano Alletto, Simone Calderara, Rita Cucchiara
http://arxiv.org/abs/1901.08097v1
• [cs.CV]Correcting rural building annotations in OpenStreetMap using convolutional neural networks
John E. Vargas-Muñoz, Sylvain Lobry, Alexandre X. Falcão, Devis Tuia
http://arxiv.org/abs/1901.08190v1
• [cs.CV]DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
Rong Kang, Jieqi Shi, Xueming Li, Yang Liu, Xiao Liu
http://arxiv.org/abs/1901.07223v2
• [cs.CV]Deep Reasoning with Multi-scale Context for Salient Object Detection
Zun Li, Congyan Lang, Yunpeng Chen, Junhao Liew, Jiashi Feng
http://arxiv.org/abs/1901.08362v1
• [cs.CV]Domain Translation with Conditional GANs: from Depth to RGB Face-to-Face
Matteo Fabbri, Guido Borghi, Fabio Lanzi, Roberto Vezzani, Simone Calderara, Rita Cucchiara
http://arxiv.org/abs/1901.08101v1
• [cs.CV]Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Hailong Ma, Ruijun Xu, Jixiang Li, Qingyuan Li
http://arxiv.org/abs/1901.07261v2
• [cs.CV]Learning Disentangled Representations with Reference-Based Variational Autoencoders
Adria Ruiz, Oriol Martinez, Xavier Binefa, Jakob Verbeek
http://arxiv.org/abs/1901.08534v1
• [cs.CV]MREAK : Morphological Retina Keypoint Descriptor
Himanshu Vaghela, Manan Oza, Sudhir Bagul
http://arxiv.org/abs/1901.08213v1
• [cs.CV]Object Detection based on Region Decomposition and Assembly
Seung-Hwan Bae
http://arxiv.org/abs/1901.08225v1
• [cs.CV]Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks
Shilei Fu, Feng Xu, Ya-Qiu Jin
http://arxiv.org/abs/1901.08236v1
• [cs.CV]Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification
Cheng Xue, Qi Dou, Xueying Shi, Hao Chen, Pheng Ann Heng
http://arxiv.org/abs/1901.07759v2
• [cs.CV]Semantic Matching by Weakly Supervised 2D Point Set Registration
Zakaria Laskar, Hamed R. Tavakoli, Juho Kannala
http://arxiv.org/abs/1901.08341v1
• [cs.CV]Semi-Supervised Image-to-Image Translation
Manan Oza, Himanshu Vaghela, Sudhir Bagul
http://arxiv.org/abs/1901.08212v1
• [cs.CV]Semi-Supervised Semantic Matching
Zakaria Laskar, Juho Kannala
http://arxiv.org/abs/1901.08339v1
• [cs.CV]Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Alvaro Gomariz, Weiye Li, Ece Ozkan, Christine Tanner, Orcun Goksel
http://arxiv.org/abs/1901.08109v1
• [cs.CV]Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng
http://arxiv.org/abs/1901.08211v1
• [cs.CV]Unsupervised Image-to-Image Translation with Self-Attention Networks
Taewon Kang, Kwang Hee Lee
http://arxiv.org/abs/1901.08242v1
• [cs.CV]Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein, Hrvoje Bogunović, José Ignacio Orlando, Bianca S. Gerendas, Georg Langs, Ursula Schmidt-Erfurth
http://arxiv.org/abs/1901.08379v1
• [cs.CV]Visualizing Topographic Independent Component Analysis with Movies
Zhimin Chen, Darius Parvin, Maedbh King, Susan Hao
http://arxiv.org/abs/1901.08239v1
• [cs.CV]Whole slide image registration for the study of tumor heterogeneity
Leslie Solorzano, Gabriela M. Almeida, Bárbara Mesquita, Diana Martins, Carla Oliveira, Carolina Wählby
http://arxiv.org/abs/1901.08317v1
• [cs.CY]Forecasting Transformative AI: An Expert Survey
Ross Gruetzemacher, David Paradice, Kang Bok Lee
http://arxiv.org/abs/1901.08579v1
• [cs.DC]A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
Bing Lin, Fangning Zhu, Jianshan Zhang, Jiaqing Chen, Xing Chen, Neal N. Xiong, Jaime Lloret Mauri
http://arxiv.org/abs/1901.07216v2
• [cs.DC]Asynchronous Decentralized Optimization in Directed Networks
Jiaqi Zhang, Keyou You
http://arxiv.org/abs/1901.08215v1
• [cs.DC]Cloud BI: Future of Business Intelligence in the Cloud
Hussain Al-Aqrabi, Lu Liu, Richard Hill, Nick Antonopoulos
http://arxiv.org/abs/1901.08151v1
• [cs.DC]DistCache: Provable Load Balancing for Large-Scale Storage Systems with Distributed Caching
Zaoxing Liu, Zhihao Bai, Zhenming Liu, Xiaozhou Li, Changhoon Kim, Vladimir Braverman, Xin Jin, Ion Stoica
http://arxiv.org/abs/1901.08200v1
• [cs.DC]Fundamental Limits of Approximate Gradient Coding
Sinong Wang, Jiashang Liu, Ness Shroff
http://arxiv.org/abs/1901.08166v1
• [cs.DC]Mokka: RSM for open networks
Egor Zuev
http://arxiv.org/abs/1901.08435v1
• [cs.FL]A model for a Lindenmayer reconstruction algorithm
Diego Gabriel Krivochen, Beth Phillips
http://arxiv.org/abs/1901.08407v1
• [cs.HC]Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment
Jonathan Dodge, Q. Vera Liao, Yunfeng Zhang, Rachel K. E. Bellamy, Casey Dugan
http://arxiv.org/abs/1901.07694v1
• [cs.HC]Teaching robots to imitate a human with no on-teacher sensors. What are the key challenges?
Radoslav Skoviera, Karla Stepanova, Michael Tesar, Gabriela Sejnova, Jiri Sedlar, Michal Vavrecka, Robert Babuska, Josef Sivic
http://arxiv.org/abs/1901.08335v1
• [cs.IR]Hybrid NER System for Multi-Source Offer Feeds
Anusha Holla, Bharat Gaind, Vikas Reddy Katta, Abhishek Kundu, S Kamalesh
http://arxiv.org/abs/1901.08406v1
• [cs.IR]Neural IR Meets Graph Embedding: A Ranking Model for Product Search
Yuan Zhang, Dong Wang, Yan Zhang
http://arxiv.org/abs/1901.08286v1
• [cs.IR]Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report)
Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
http://arxiv.org/abs/1901.08422v1
• [cs.IR]Sequential Skip Prediction with Few-shot in Streamed Music Contents
Sungkyun Chang, Seungjin Lee, Kyogu Lee
http://arxiv.org/abs/1901.08203v1
• [cs.IT]A Systematic Construction of MDS Codes with Small Sub-packetization Level and Near Optimal Repair Bandwidth
Jie Li, Xiaohu Tang
http://arxiv.org/abs/1901.08254v1
• [cs.IT]Dispensing with Noise Forward in the "Weak" Relay-Eavesdropper Channel
Krishnamoorthy Iyer
http://arxiv.org/abs/1901.08363v1
• [cs.IT]End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
Boris Karanov, Domaniç Lavery, Polina Bayvel, Laurent Schmalen
http://arxiv.org/abs/1901.08570v1
• [cs.IT]Explicit Polar Codes with Small Scaling Exponent
Hanwen Yao, Arman Fazeli, Alexander Vardy
http://arxiv.org/abs/1901.08186v1
• [cs.IT]Homomorphic Sensing
Manolis C. Tsakiris, Liangzu Peng
http://arxiv.org/abs/1901.07852v2
• [cs.IT]Multi-Frequency Phase Synchronization
Tingran Gao, Zhizhen Zhao
http://arxiv.org/abs/1901.08235v1
• [cs.IT]Real-Time Reconstruction of Counting Process through Queues
Meng Wang, Wei Chen, Anthony Ephremides
http://arxiv.org/abs/1901.08197v1
• [cs.IT]Recovery of Structured Signals From Corrupted Non-Linear Measurements
Zhongxing Sun, Wei Cui, Yulong Liu
http://arxiv.org/abs/1901.08349v1
• [cs.IT]Using Erasure Feedback for Online Timely Updating with an Energy Harvesting Sensor
Ahmed Arafa, Jing Yang, Sennur Ulukus, H. Vincent Poor
http://arxiv.org/abs/1901.08577v1
• [cs.IT]What Can Machine Learning Teach Us about Communications?
Mengke Lian, Christian Häger, Henry D. Pfister
http://arxiv.org/abs/1901.07592v2
• [cs.LG]Adversarial Variational Inference and Learning in Markov Random Fields
Chongxuan Li, Chao Du, Kun Xu, Max Welling, Jun Zhu, Bo Zhang
http://arxiv.org/abs/1901.08400v1
• [cs.LG]Causal Reasoning from Meta-reinforcement Learning
Ishita Dasgupta, Jane Wang, Silvia Chiappa, Jovana Mitrovic, Pedro Ortega, David Raposo, Edward Hughes, Peter Battaglia, Matthew Botvinick, Zeb Kurth-Nelson
http://arxiv.org/abs/1901.08162v1
• [cs.LG]Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group
Mario Lezcano-Casado, David Martínez-Rubio
http://arxiv.org/abs/1901.08428v1
• [cs.LG]Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
http://arxiv.org/abs/1901.08460v1
• [cs.LG]Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar
http://arxiv.org/abs/1901.08255v1
• [cs.LG]Context Prediction for Unsupervised Deep Learning on Point Clouds
Jonathan Sauder, Bjarne Sievers
http://arxiv.org/abs/1901.08396v1
• [cs.LG]Cooperative Online Learning: Keeping your Neighbors Updated
Nicolò Cesa-Bianchi, Tommaso R. Cesari, Claire Monteleoni
http://arxiv.org/abs/1901.08082v1
• [cs.LG]Cross-Entropy Loss and Low-Rank Features Have Responsibility for Adversarial Examples
Kamil Nar, Orhan Ocal, S. Shankar Sastry, Kannan Ramchandran
http://arxiv.org/abs/1901.08360v1
• [cs.LG]Decoupled Greedy Learning of CNNs
Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
http://arxiv.org/abs/1901.08164v1
• [cs.LG]Deep Generative Learning via Variational Gradient Flow
Gao Yuan, Jiao Yuling, Wang Yang, Wang Yao, Yang Can, Zhang Shunkang
http://arxiv.org/abs/1901.08469v1
• [cs.LG]Deep Learning on Attributed Graphs: A Journey from Graphs to Their Embeddings and Back
Martin Simonovsky
http://arxiv.org/abs/1901.08296v1
• [cs.LG]Distillation Strategies for Proximal Policy Optimization
Sam Green, Craig M. Vineyard, Çetin Kaya Koç
http://arxiv.org/abs/1901.08128v1
• [cs.LG]Fairness with Dynamics
Min Wen, Osbert Bastani, Ufuk Topcu
http://arxiv.org/abs/1901.08568v1
• [cs.LG]Federated Reinforcement Learning
Hankz Hankui Zhuo, Wenfeng Feng, Qian Xu, Qiang Yang, Yufeng Lin
http://arxiv.org/abs/1901.08277v1
• [cs.LG]Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks
Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
http://arxiv.org/abs/1901.08584v1
• [cs.LG]Generating and Aligning from Data Geometries with Generative Adversarial Networks
Matthew Amodio, Smita Krishnaswamy
http://arxiv.org/abs/1901.08177v1
• [cs.LG]Graph heat mixture model learning
Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard
http://arxiv.org/abs/1901.08585v1
• [cs.LG]Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks
Charles H. Martin, Michael W. Mahoney
http://arxiv.org/abs/1901.08278v1
• [cs.LG]High Dimensional Robust Estimation of Sparse Models via Trimmed Hard Thresholding
Liu Liu, Tianyang Li, Constantine Caramanis
http://arxiv.org/abs/1901.08237v1
• [cs.LG]Hypergraph Convolution and Hypergraph Attention
Song Bai, Feihu Zhang, Philip H. S. Torr
http://arxiv.org/abs/1901.08150v1
• [cs.LG]ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU
William Caicedo-Torres, Jairo Gutierrez
http://arxiv.org/abs/1901.08201v1
• [cs.LG]Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records
Alvaro E. Ulloa Cerna, Marios Pattichis, David P. vanMaanen, Linyuan Jing, Aalpen A. Patel, Joshua V. Stough, Christopher M. Haggerty, Brandon K. Fornwalt
http://arxiv.org/abs/1901.08125v1
• [cs.LG]Large-Batch Training for LSTM and Beyond
Yang You, Jonathan Hseu, Chris Ying, James Demmel, Kurt Keutzer, Cho-Jui Hsieh
http://arxiv.org/abs/1901.08256v1
• [cs.LG]Learning Interpretable Models with Causal Guarantees
Carolyn Kim, Osbert Bastani
http://arxiv.org/abs/1901.08576v1
• [cs.LG]Learning Neurosymbolic Generative Models via Program Synthesis
Halley Young, Osbert Bastani, Mayur Naik
http://arxiv.org/abs/1901.08565v1
• [cs.LG]Learning Sublinear-Time Indexing for Nearest Neighbor Search
Yihe Dong, Piotr Indyk, Ilya Razenshteyn, Tal Wagner
http://arxiv.org/abs/1901.08544v1
• [cs.LG]Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, Zhenhui Li
http://arxiv.org/abs/1901.08518v1
• [cs.LG]Learning to compress and search visual data in large-scale systems
Sohrab Ferdowsi
http://arxiv.org/abs/1901.08437v1
• [cs.LG]Location reference identification from tweets during emergencies: A deep learning approach
Abhinav Kumar, Jyoti Prakash Singh
http://arxiv.org/abs/1901.08241v1
• [cs.LG]Loss Landscapes of Regularized Linear Autoencoders
Daniel Kunin, Jonathan M. Bloom, Aleksandrina Goeva, Cotton Seed
http://arxiv.org/abs/1901.08168v1
• [cs.LG]Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review
Shen Zhang, Shibo Zhang, Bingnan Wang, Thomas G. Habetler
http://arxiv.org/abs/1901.08247v1
• [cs.LG]Maximum Entropy Generators for Energy-Based Models
Rithesh Kumar, Anirudh Goyal, Aaron Courville, Yoshua Bengio
http://arxiv.org/abs/1901.08508v1
• [cs.LG]Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians
Vardan Papyan
http://arxiv.org/abs/1901.08244v1
• [cs.LG]Meta-Learning for Contextual Bandit Exploration
Amr Sharaf, Hal Daumé III
http://arxiv.org/abs/1901.08159v1
• [cs.LG]Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow
Hsuan-Kung Yang, Po-Han Chiang, Kuan-Wei Ho, Min-Fong Hong, Chun-Yi Lee
http://arxiv.org/abs/1901.08486v1
• [cs.LG]On the Transformation of Latent Space in Autoencoders
Jaehoon Cha, Kyeong Soo Kim, Sanghyuk Lee
http://arxiv.org/abs/1901.08479v1
• [cs.LG]Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi, Marta Garnelo, Yoram Bachrach, Wojciech M. Czarnecki, Julien Perolat, Max Jaderberg, Thore Graepel
http://arxiv.org/abs/1901.08106v1
• [cs.LG]PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits
Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
http://arxiv.org/abs/1901.08386v1
• [cs.LG]Provable Smoothness Guarantees for Black-Box Variational Inference
Justin Domke
http://arxiv.org/abs/1901.08431v1
• [cs.LG]Recovering Pairwise Interactions Using Neural Networks
Tianyu Cui, Pekka Marttinen, Samuel Kaski
http://arxiv.org/abs/1901.08361v1
• [cs.LG]Regret Minimisation in Multi-Armed Bandits Using Bounded Arm Memory
Arghya Roy Chaudhuri, Shivaram Kalyanakrishnan
http://arxiv.org/abs/1901.08387v1
• [cs.LG]Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems
Osbert Bastani
http://arxiv.org/abs/1901.08562v1
• [cs.LG]Sitatapatra: Blocking the Transfer of Adversarial Samples
Ilia Shumailov, Xitong Gao, Yiren Zhao, Robert Mullins, Ross Anderson, Cheng-Zhong Xu
http://arxiv.org/abs/1901.08121v1
• [cs.LG]Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
http://arxiv.org/abs/1901.08280v1
• [cs.LG]Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
http://arxiv.org/abs/1901.08573v1
• [cs.LG]Traditional and Heavy-Tailed Self Regularization in Neural Network Models
Charles H. Martin, Michael W. Mahoney
http://arxiv.org/abs/1901.08276v1
• [cs.LG]Trajectory Normalized Gradients for Distributed Optimization
Jianqiao Wangni, Ke Li, Jianbo Shi, Jitendra Malik
http://arxiv.org/abs/1901.08227v1
• [cs.LG]Width Provably Matters in Optimization for Deep Linear Neural Networks
Simon S. Du, Wei Hu
http://arxiv.org/abs/1901.08572v1
• [cs.NE]QGAN: Quantized Generative Adversarial Networks
Peiqi Wang, Dongsheng Wang, Yu Ji, Xinfeng Xie, Haoxuan Song, XuXin Liu, Yongqiang Lyu, Yuan Xie
http://arxiv.org/abs/1901.08263v1
• [cs.NE]Really should we pruning after model be totally trained? Pruning based on a small amount of training
Li Yue, Zhao Weibin, Shang Lin
http://arxiv.org/abs/1901.08455v1
• [cs.NI]A stack-vector routing protocol for automatic tunneling
Mohamed Lamine Lamali, Simon Lassourreuille, Stephan Kunne, Johanne Cohen
http://arxiv.org/abs/1901.08326v1
• [cs.NI]When Machine Learning Meets Big Data: A Wireless Communication Perspective
Yuanwei Liu, Suzhi Bi, Zhiyuan Shi, Lajos Hanzo
http://arxiv.org/abs/1901.08329v1
• [cs.RO]Decentralization of Multiagent Policies by Learning What to Communicate
James Paulos, Steven W. Chen, Daigo Shishika, Vijay Kumar
http://arxiv.org/abs/1901.08490v1
• [cs.RO]Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots
Guillaume Sartoretti, William Paivine, Yunfei Shi, Yue Wu, Howie Choset
http://arxiv.org/abs/1901.08537v1
• [cs.RO]Dynamic Locomotion For Passive-Ankle Biped Robots And Humanoids Using Whole-Body Locomotion Control
D. Kim, S. Jorgensen, J. Lee, J. Ahn, J. Luo, L. Sentis
http://arxiv.org/abs/1901.08100v1
• [cs.RO]F1/10: An Open-Source Autonomous Cyber-Physical Platform
Matthew O'Kelly, Varundev Sukhil, Houssam Abbas, Jack Harkins, Chris Kao, Yash Vardhan Pant, Rahul Mangharam, Dipshil Agarwal, Madhur Behl, Paolo Burgio, Marko Bertogna
http://arxiv.org/abs/1901.08567v1
• [cs.RO]MPC for Humanoid Gait Generation: Stability and Feasibility
Nicola Scianca, Daniele De Simone, Leonardo Lanari, Giuseppe Oriolo
http://arxiv.org/abs/1901.08505v1
• [cs.RO]Mixed-Granularity Human-Swarm Interaction
Jayam Patel, Yicong Xu, Carlo Pinciroli
http://arxiv.org/abs/1901.08522v1
• [cs.RO]Sequential path planning for a formation of mobile robots with split and merge
M. Estefanía Pereyra, R. Gastón Araguás, Miroslav Kulich
http://arxiv.org/abs/1901.08444v1
• [cs.RO]Vision-based Obstacle Removal System for Autonomous Ground Vehicles Using a Robotic Arm
Khashayar Asadi, Rahul Jain, Ziqian Qin, Mingda Sun, Mojtaba Noghabaei, Jeremy Cole, Kevin Han, Edgar Lobaton
http://arxiv.org/abs/1901.08180v1
• [cs.SE]Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction
Haonan Tong, Bin Liu, Shihai Wang, Qiuying Li
http://arxiv.org/abs/1901.08429v1
• [cs.SI]Emotion Detection and Analysis on Social Media
Bharat Gaind, Varun Syal, Sneha Padgalwar
http://arxiv.org/abs/1901.08458v1
• [eess.SP]Intersymbol and Intercarrier Interference in OFDM Transmissions through Highly Dispersive Channels
Wallace Alves Martins, Fernando Cruz-Roldán, Marc Moonen, Paulo Sergio Ramirez Diniz
http://arxiv.org/abs/1901.08142v1
• [math.CO]On an open problem about a class of optimal ternary cyclic codes
Dongchun Han, Haode Yan
http://arxiv.org/abs/1901.08230v1
• [math.OC]A Fully Stochastic Primal-Dual Algorithm
Adil Salim, Pascal Bianchi, Walid Hachem
http://arxiv.org/abs/1901.08170v1
• [math.OC]A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach
Aryan Mokhtari, Asuman Ozdaglar, Sarath Pattathil
http://arxiv.org/abs/1901.08511v1
• [math.OC]Curvature-Exploiting Acceleration of Elastic Net Computations
Vien V. Mai, Mikael Johansson
http://arxiv.org/abs/1901.08523v1
• [math.OC]Model Function Based Conditional Gradient Method with Armijo-like Line Search
Yura Malitsky, Peter Ochs
http://arxiv.org/abs/1901.08087v1
• [math.PR]Breaking Bivariate Records
James Allen Fill
http://arxiv.org/abs/1901.08232v1
• [math.PR]Stick-breaking processes, clumping, and Markov chain occupation laws
Zach Dietz, William Lippitt, Sunder Sethuraman
http://arxiv.org/abs/1901.08135v1
• [math.ST]Consistent nonparametric change point detection combining CUSUM and marked empirical processes
Maria Mohr, Natalie Neumeyer
http://arxiv.org/abs/1901.08491v1
• [math.ST]Detecting Changes in Hidden Markov Models
George V. Moustakides
http://arxiv.org/abs/1901.08434v1
• [math.ST]Optimal Nonparametric Inference under Quantization
Ruiqi Liu, Ganggang Xu, Zuofeng Shang
http://arxiv.org/abs/1901.08571v1
• [math.ST]Optimal Uncertainty Size in Distributionally Robust Inverse Covariance Estimation
Jose Blanchet, Nian Si
http://arxiv.org/abs/1901.07693v1
• [math.ST]Raking-ratio empirical process with auxiliary information learning
Mickael Albertus
http://arxiv.org/abs/1901.08519v1
• [math.ST]Testing Equality of Autocovariance Operators for Functional Time Series
Dimitrios Pilavakis, Efstathios Paparoditis, Theofanis Sapatinas
http://arxiv.org/abs/1901.08535v1
• [physics.soc-ph]Emergence of leader-follower hierarchy among players in an on-line experiment
Bálint J. Tóth, Gergely Palla, Enys Mones, Gergő Havadi, Nóra Páll, Péter Pollner, Tamás Vicsek
http://arxiv.org/abs/1901.08507v1
• [physics.soc-ph]Reentrant phase transitions in threshold driven contagion on multiplex networks
Samuel Unicomb, Gerardo Iñiguez, János Kertész, Márton Karsai
http://arxiv.org/abs/1901.08306v1
• [stat.AP]Asynchronous Multi-Sensor Change-Point Detection for Seismic Tremors
Liyan Xie, Yao Xie, George V. Moustakides
http://arxiv.org/abs/1901.08196v1
• [stat.AP]Modelling the Demand and Uncertainty of Low Voltage Networks and the Effect of non-Domestic Consumers
Georgios Giasemidis, Stephen Haben
http://arxiv.org/abs/1901.08497v1
• [stat.AP]Spatial Modeling of Trends in Crime over Time in Philadelphia
Cecilia Balocchi, Shane T. Jensen
http://arxiv.org/abs/1901.08117v1
• [stat.ME]A new integrated likelihood for estimating population size in dependent dual-record system
Kiranmoy Chatterjee, Diganta Mukherjee
http://arxiv.org/abs/1901.08107v1
• [stat.ME]Multi-Goal Prior Selection: A Way to Reconcile Bayesian and Classical Approaches for Random Effects Models
Masayo Y. Hirose, Partha Lahiri
http://arxiv.org/abs/1901.08245v1
• [stat.ME]New Exploratory Tools for Extremal Dependence: Chi Networks and Annual Extremal Networks
Whitney K. Huang, Daniel S. Cooley, Imme Ebert-Uphoff, Chen Chen, Snigdhansu Chatterjee
http://arxiv.org/abs/1901.08169v1
• [stat.ME]Seamless phase II/III clinical trials using early outcomes for treatment or subgroup selection: Methods and aspects of their implementation
Tim Friede, Nigel Stallard, Nicholas Parsons
http://arxiv.org/abs/1901.08365v1
• [stat.ML]A Review on Quantile Regression for Stochastic Computer Experiments
Léonard Torossian, Victor Picheny, Robert Faivre, Aurélien Garivier
http://arxiv.org/abs/1901.07874v2
• [stat.ML]A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization
Yan Wang, Xuelei Sherry Ni
http://arxiv.org/abs/1901.08433v1
• [stat.ML]Causal Mediation Analysis Leveraging Multiple Types of Summary Statistics Data
Yongjin Park, Abhishek Sarkar, Khoi Nguyen, Manolis Kellis
http://arxiv.org/abs/1901.08540v1
• [stat.ML]Deep Mean Functions for Meta-Learning in Gaussian Processes
Vincent Fortuin, Gunnar Rätsch
http://arxiv.org/abs/1901.08098v1
• [stat.ML]General Supervision via Probabilistic Transformations
Santiago Mazuelas, Aritz Perez
http://arxiv.org/abs/1901.08552v1
• [stat.ML]Large dimensional analysis of general margin based classification methods
Hanwen Huang
http://arxiv.org/abs/1901.08057v1
• [stat.ML]Multi-fidelity Bayesian Optimization with Max-value Entropy Search
Shion Takeno, Hitoshi Fukuoka, Yuhki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, Masayuki Karasuyama
http://arxiv.org/abs/1901.08275v1
• [stat.ML]On Local Optimizers of Acquisition Functions in Bayesian Optimization
Jungtaek Kim, Seungjin Choi
http://arxiv.org/abs/1901.08350v1
• [stat.ML]Overcomplete Independent Component Analysis via SDP
Anastasia Podosinnikova, Amelia Perry, Alexander Wein, Francis Bach, Alexandre d'Aspremont, David Sontag
http://arxiv.org/abs/1901.08334v1
• [stat.ML]Pretending Fair Decisions via Stealthily Biased Sampling
Kazuto Fukuchi, Satoshi Hara, Takanori Maehara
http://arxiv.org/abs/1901.08291v1
• [stat.ML]Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction
Bryan Lim, Stefan Zohren, Stephen Roberts
http://arxiv.org/abs/1901.08096v1
• [stat.ML]Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels
Matthew Willetts, Stephen J Roberts, Christopher C Holmes
http://arxiv.org/abs/1901.08560v1
• [stat.ML]Three principles of data science: predictability, computability, and stability (PCS)
Bin Yu, Karl Kumbier
http://arxiv.org/abs/1901.08152v1
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