cs.AR - 硬件体系结构
cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 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.NC - 神经元与认知 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.AR]Polystore++: Accelerated Polystore System for Heterogeneous Workloads
• [cs.CL]A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
• [cs.CL]An Analysis of Source-Side Grammatical Errors in NMT
• [cs.CL]BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
• [cs.CL]Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
• [cs.CL]Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor
• [cs.CL]Incorporating Context and External Knowledge for Pronoun Coreference Resolution
• [cs.CL]Outline Generation: Understanding the Inherent Content Structure of Documents
• [cs.CL]Personalizing Dialogue Agents via Meta-Learning
• [cs.CL]Training language GANs from Scratch
• [cs.CL]Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
• [cs.CL]mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
• [cs.CR]Devil in the Detail: Attack Scenarios in Industrial Applications
• [cs.CR]Tiresias: Predicting Security Events Through Deep Learning
• [cs.CV]A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
• [cs.CV]A Compressive Sensing Video dataset using Pixel-wise coded exposure
• [cs.CV]A Real-Time Tiny Detection Model for Stem End and Blossom End of Navel Orange
• [cs.CV]ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
• [cs.CV]Beyond Intra-modality Discrepancy: A Comprehensive Survey of Heterogeneous Person Re-identification
• [cs.CV]Brain-mediated Transfer Learning of Convolutional Neural Networks
• [cs.CV]DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
• [cs.CV]Deep Reason: A Strong Baseline for Real-World Visual Reasoning
• [cs.CV]Deep Trajectory for Recognition of Human Behaviours
• [cs.CV]From Here to There: Video Inbetweening Using Direct 3D Convolutions
• [cs.CV]Generative Flow via Invertible nxn Convolution
• [cs.CV]Guided Stereo Matching
• [cs.CV]Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning
• [cs.CV]Light-Weight RetinaNet for Object Detection
• [cs.CV]Mask-Guided Portrait Editing with Conditional GANs
• [cs.CV]Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
• [cs.CV]OVSNet : Towards One-Pass Real-Time Video Object Segmentation
• [cs.CV]PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
• [cs.CV]Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition
• [cs.CV]Rank3DGAN: Semantic mesh generation using relative attributes
• [cs.CV]Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
• [cs.CV]Saliency detection based on structural dissimilarity induced by image quality assessment model
• [cs.CV]Self-Critical Reasoning for Robust Visual Question Answering
• [cs.CV]Uncertainty Estimation in One-Stage Object Detection
• [cs.CY]Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
• [cs.DC]Atomic Cross-Chain Swaps with Improved Space and Time Complexity
• [cs.DC]Deploying AI Frameworks on Secure HPC Systems with Containers
• [cs.DC]Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
• [cs.DC]Performance-Feedback Autoscaling with Budget Constraints for Cloud-based Workloads of Workflows
• [cs.DS]A Practical Framework for Solving Center-Based Clustering with Outliers
• [cs.DS]Hardness of Distributed Optimization
• [cs.HC]From Search Engines to Search Services: An End-User Driven Approach
• [cs.IR]Controlling Risk of Web Question Answering
• [cs.IR]MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
• [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
• [cs.IT]Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
• [cs.IT]Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
• [cs.IT]Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
• [cs.IT]Minimax Rates of Estimating Approximate Differential Privacy
• [cs.IT]On Recurrent Neural Networks for Sequence-based Processing in Communications
• [cs.IT]On the Global Minimizers of Real Robust Phase Retrieval with Sparse Noise
• [cs.IT]On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors
• [cs.IT]Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications
• [cs.IT]Signature codes for weighted binary adder channel and multimedia fingerprinting
• [cs.IT]Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
• [cs.LG]Adaptive Symmetric Reward Noising for Reinforcement Learning
• [cs.LG]An Explicitly Relational Neural Network Architecture
• [cs.LG]Approximation Ratios of Graph Neural Networks for Combinatorial Problems
• [cs.LG]Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
• [cs.LG]Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
• [cs.LG]Continual Reinforcement Learning in 3D Non-stationary Environments
• [cs.LG]Copy this Sentence
• [cs.LG]Curriculum Loss: Robust Learning and Generalization against Label Corruption
• [cs.LG]Deep Model Predictive Control with Online Learning for Complex Physical Systems
• [cs.LG]Deep density ratio estimation for change point detection
• [cs.LG]Deep-gKnock: nonlinear group-feature selection with deep neural network
• [cs.LG]Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
• [cs.LG]Discrete Flows: Invertible Generative Models of Discrete Data
• [cs.LG]Distributional Policy Optimization: An Alternative Approach for Continuous Control
• [cs.LG]Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
• [cs.LG]Efficient Batch Black-box Optimization with Deterministic Regret Bounds
• [cs.LG]Embedded Meta-Learning: Toward more flexible deep-learning models
• [cs.LG]Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks
• [cs.LG]Exploration via Flow-Based Intrinsic Rewards
• [cs.LG]Federated Forest
• [cs.LG]Generative Grading: Neural Approximate Parsing for Automated Student Feedback
• [cs.LG]Generative adversarial network based on chaotic time series
• [cs.LG]Graph Representations for Higher-Order Logic and Theorem Proving
• [cs.LG]Gravity-Inspired Graph Autoencoders for Directed Link Prediction
• [cs.LG]HDI-Forest: Highest Density Interval Regression Forest
• [cs.LG]Interpreting a Recurrent Neural Network Model for ICU Mortality Using Learned Binary Masks
• [cs.LG]Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
• [cs.LG]Learning Cross-Domain Representation with Multi-Graph Neural Network
• [cs.LG]Learning Low-Rank Approximation for CNNs
• [cs.LG]Learning Surrogate Losses
• [cs.LG]Learning to learn by Self-Critique
• [cs.LG]Likelihood-Free Inference and Generation of Molecular Graphs
• [cs.LG]Loss Surface Modality of Feed-Forward Neural Network Architectures
• [cs.LG]Memorized Sparse Backpropagation
• [cs.LG]Momentum-Based Variance Reduction in Non-Convex SGD
• [cs.LG]Multi-Kernel Correntropy for Robust Learning
• [cs.LG]Neural Temporal-Difference Learning Converges to Global Optima
• [cs.LG]Neuro-Optimization: Learning Objective Functions Using Neural Networks
• [cs.LG]Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation
• [cs.LG]On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks
• [cs.LG]Optimizing Shallow Networks for Binary Classification
• [cs.LG]Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
• [cs.LG]Partially Encrypted Machine Learning using Functional Encryption
• [cs.LG]Perturbed Model Validation: A New Framework to Validate Model Relevance
• [cs.LG]Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering
• [cs.LG]Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective
• [cs.LG]Robust Attribution Regularization
• [cs.LG]SCRAM: Spatially Coherent Randomized Attention Maps
• [cs.LG]STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
• [cs.LG]Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
• [cs.LG]Statistical embedding for directed graphs
• [cs.LG]Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks
• [cs.LG]Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
• [cs.LG]The advantages of multiple classes for reducing overfitting from test set reuse
• [cs.LG]Training decision trees as replacement for convolution layers
• [cs.LG]What Can ResNet Learn Efficiently, Going Beyond Kernels?
• [cs.LG]X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations
• [cs.MA]Decentralized Informative Path Planning with Exploration-Exploitation Balance for Swarm Robotic Search
• [cs.MA]winPIBT: Expanded Prioritized Algorithm for Iterative Multi-agent Path Finding
• [cs.NE]Instruction-Level Design of Local Optimisers using Push GP
• [cs.RO]Designing an Inertia Actuator with a Fast Rotating Gyro inside an Egg-shaped Robot
• [cs.RO]Mechatronic Design of a Dribbling System for RoboCup Small Size Robot
• [cs.RO]Scene Induced Multi-Modal Trajectory Forecasting via Planning
• [cs.RO]Visual Model-predictive Localization for Computationally Efficient Autonomous Racing of a 72-gram Drone
• [cs.SD]Disentangled Feature for Weakly Supervised Multi-class Sound Event Detection
• [cs.SE]A Customised App to Attract Female Teenagers to Coding
• [cs.SI]An Integrated Model for User Innovation Knowledge Based on Super-network
• [cs.SI]Extended Scale-Free Networks
• [cs.SI]Multifaceted Privacy: How to Express Your Online Persona without Revealing Your Sensitive Attributes
• [cs.SI]Tempus Volat, Hora Fugit — A Survey of Dynamic Network Models in Discrete and Continuous Time
• [econ.EM]Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
• [econ.EM]Semi-Parametric Efficient Policy Learning with Continuous Actions
• [eess.IV]A Research and Strategy of Remote Sensing Image Denoising Algorithms
• [eess.IV]Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images
• [eess.IV]Tissue segmentation with deep 3D networks and spatial priors
• [math.CO]A concatenation construction for propelinear perfect codes from regular subgroups of GA(r,2)
• [math.PR]Revisiting Relations between Stochastic Ageing and Dependence for Exchangeable Lifetimes with an Extension for the IFRA/DFRA Property
• [math.PR]The Skipping Sampler: A new approach to sample from complex conditional densities
• [math.ST]Asymptotic Behaviour of Discretised Functionals of Long-Range Dependent Functional Data
• [math.ST]High-Dimensional Functional Factor Models
• [math.ST]Likelihood ratio tests for many groups in high dimensions
• [math.ST]Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
• [physics.soc-ph]Morphological organization of point-to-point transport in complex networks
• [q-bio.NC]Damped oscillations of the probability of random events followed by absolute refractory period
• [stat.AP]Inference of Dynamic Graph Changes for Functional Connectome
• [stat.AP]The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
• [stat.CO]A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
• [stat.CO]Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm
• [stat.CO]Estimating Convergence of Markov chains with L-Lag Couplings
• [stat.CO]Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study
• [stat.CO]Parallel Coordinate Order for High-Dimensional Data
• [stat.ME]Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
• [stat.ME]Optimal nonparametric change point detection and localization
• [stat.ML]Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation
• [stat.ML]Convergence Guarantees for Adaptive Bayesian Quadrature Methods
• [stat.ML]Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
• [stat.ML]OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits
• [stat.ML]Polynomial Cost of Adaptation for X -Armed Bandits
• [stat.ML]Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
• [stat.ML]Privacy Risks of Securing Machine Learning Models against Adversarial Examples
• [stat.ML]Sequential Gaussian Processes for Online Learning of Nonstationary Functions
• [stat.ML]Sliced Gromov-Wasserstein
• [stat.ML]forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction
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• [cs.AR]Polystore++: Accelerated Polystore System for Heterogeneous Workloads
Rekha Singhal, Nathan Zhang, Luigi Nardi, Muhammad Shahbaz, Kunle Olukotun
http://arxiv.org/abs/1905.10336v1
• [cs.CL]A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer
Fuli Luo, Peng Li, Jie Zhou, Pengcheng Yang, Baobao Chang, Zhifang Sui, Xu Sun
http://arxiv.org/abs/1905.10060v1
• [cs.CL]An Analysis of Source-Side Grammatical Errors in NMT
Antonios Anastasopoulos
http://arxiv.org/abs/1905.10024v1
• [cs.CL]BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, Kristina Toutanova
http://arxiv.org/abs/1905.10044v1
• [cs.CL]Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
Sungjin Lee, Igor Shalyminov
http://arxiv.org/abs/1905.10247v1
• [cs.CL]Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor
Malvina Nissim, Rik van Noord, Rob van der Goot
http://arxiv.org/abs/1905.09866v1
• [cs.CL]Incorporating Context and External Knowledge for Pronoun Coreference Resolution
Hongming Zhang, Yan Song, Yangqiu Song
http://arxiv.org/abs/1905.10238v1
• [cs.CL]Outline Generation: Understanding the Inherent Content Structure of Documents
Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng
http://arxiv.org/abs/1905.10039v1
• [cs.CL]Personalizing Dialogue Agents via Meta-Learning
Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung
http://arxiv.org/abs/1905.10033v1
• [cs.CL]Training language GANs from Scratch
Cyprien de Masson d’Autume, Mihaela Rosca, Jack Rae, Shakir Mohamed
http://arxiv.org/abs/1905.09922v1
• [cs.CL]Why Didn’t You Listen to Me? Comparing User Control of Human-in-the-Loop Topic Models
Varun Kumar, Alison Smith-Renner, Leah Findlater, Kevin Seppi, Jordan Boyd-Graber
http://arxiv.org/abs/1905.09864v1
• [cs.CL]mu-Forcing: Training Variational Recurrent Autoencoders for Text Generation
Dayiheng Liu, Xu Yang, Feng He, Yuanyuan Chen, Jiancheng Lv
http://arxiv.org/abs/1905.10072v1
• [cs.CR]Devil in the Detail: Attack Scenarios in Industrial Applications
Simon D. Duque Anton, Alexander Hafner, Hans Dieter Schotten
http://arxiv.org/abs/1905.10292v1
• [cs.CR]Tiresias: Predicting Security Events Through Deep Learning
Yun Shen, Enrico Mariconti, Pierre-Antoine Vervier, Gianluca Stringhini
http://arxiv.org/abs/1905.10328v1
• [cs.CV]A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
Junxing Hu, Ling Li, Yijun Lin, Fengge Wu, Junsuo Zhao
http://arxiv.org/abs/1905.10231v1
• [cs.CV]A Compressive Sensing Video dataset using Pixel-wise coded exposure
Sathyaprakash Narayanan, Yeshwanth Beti, Chetan Singh Thakur
http://arxiv.org/abs/1905.10054v1
• [cs.CV]A Real-Time Tiny Detection Model for Stem End and Blossom End of Navel Orange
Xiaoye Sun, Shaoyun Xu, Gongyan Li
http://arxiv.org/abs/1905.09994v1
• [cs.CV]ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
Xinxin Hu, Kailun Yang, Lei Fei, Kaiwei Wang
http://arxiv.org/abs/1905.10089v1
• [cs.CV]Beyond Intra-modality Discrepancy: A Comprehensive Survey of Heterogeneous Person Re-identification
Zheng Wang, Zhixiang Wang, Yang Wu, Jingdong Wang, Shin’ichi Satoh
http://arxiv.org/abs/1905.10048v1
• [cs.CV]Brain-mediated Transfer Learning of Convolutional Neural Networks
Satoshi Nishida, Yusuke Nakano, Antoine Blanc, Shinji Nishimoto
http://arxiv.org/abs/1905.10037v1
• [cs.CV]DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects
Edgar Tretschk, Ayush Tewari, Michael Zollhöfer, Vladislav Golyanik, Christian Theobalt
http://arxiv.org/abs/1905.10290v1
• [cs.CV]Deep Reason: A Strong Baseline for Real-World Visual Reasoning
Chenfei Wu, Yanzhao Zhou, Gen Li, Nan Duan, Duyu Tang, Xiaojie Wang
http://arxiv.org/abs/1905.10226v1
• [cs.CV]Deep Trajectory for Recognition of Human Behaviours
Tauseef Ali, Eissa Jaber Alreshidi
http://arxiv.org/abs/1905.10357v1
• [cs.CV]From Here to There: Video Inbetweening Using Direct 3D Convolutions
Yunpeng Li, Dominik Roblek, Marco Tagliasacchi
http://arxiv.org/abs/1905.10240v1
• [cs.CV]Generative Flow via Invertible nxn Convolution
Thanh-Dat Truong, Khoa Luu, Chi Nhan Duong, Ngan Le, Minh-Triet Tran
http://arxiv.org/abs/1905.10170v1
• [cs.CV]Guided Stereo Matching
Matteo Poggi, Davide Pallotti, Fabio Tosi, Stefano Mattoccia
http://arxiv.org/abs/1905.10107v1
• [cs.CV]Implicit Label Augmentation on Partially Annotated Clips via Temporally-Adaptive Features Learning
Yongxi Lu, Ziyao Tang, Tara Javidi
http://arxiv.org/abs/1905.10000v1
• [cs.CV]Light-Weight RetinaNet for Object Detection
Yixing Li, Fengbo Ren
http://arxiv.org/abs/1905.10011v1
• [cs.CV]Mask-Guided Portrait Editing with Conditional GANs
Shuyang Gu, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen, Lu Yuan
http://arxiv.org/abs/1905.10346v1
• [cs.CV]Multi-Scale Dual-Branch Fully Convolutional Network for Hand Parsing
Yang Lu, Xiaohui Liang, Frederick W. B. Li
http://arxiv.org/abs/1905.10100v1
• [cs.CV]OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Peng Sun, Peiwen Lin, Guangliang Cheng, Jianping Shi, Jiawan Zhang, Xi Li
http://arxiv.org/abs/1905.10064v1
• [cs.CV]PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Junyu Gao, Qi Wang, Xuelong Li
http://arxiv.org/abs/1905.10085v1
• [cs.CV]Pose-adaptive Hierarchical Attention Network for Facial Expression Recognition
Yuanyuan Liu, Jiyao Peng, Jiabei Zeng, Shiguang Shan
http://arxiv.org/abs/1905.10059v1
• [cs.CV]Rank3DGAN: Semantic mesh generation using relative attributes
Yassir Saquil, Qun-Ce Xu, Yong-Liang Yang, Peter Hall
http://arxiv.org/abs/1905.10257v1
• [cs.CV]Robust Semantic Segmentation in Adverse Weather Conditions by means of Sensor Data Fusion
Andreas Pfeuffer, Klaus Dietmayer
http://arxiv.org/abs/1905.10117v1
• [cs.CV]Saliency detection based on structural dissimilarity induced by image quality assessment model
Yang Li, Xuanqin Mou
http://arxiv.org/abs/1905.10150v1
• [cs.CV]Self-Critical Reasoning for Robust Visual Question Answering
Jialin Wu, Raymond J. Mooney
http://arxiv.org/abs/1905.09998v1
• [cs.CV]Uncertainty Estimation in One-Stage Object Detection
Florian Kraus, Klaus Dietmayer
http://arxiv.org/abs/1905.10296v1
• [cs.CY]Affirmative Action Policies for Top-k Candidates Selection, With an Application to the Design of Policies for University Admissions
Michael Mathioudakis, Carlos Castillo, Giorgio Barnabo, Sergio Celis
http://arxiv.org/abs/1905.09947v1
• [cs.DC]Atomic Cross-Chain Swaps with Improved Space and Time Complexity
Soichiro Imoto, Yuichi Sudo, Hirotsugu Kakugawa, Toshimitsu Masuzawa
http://arxiv.org/abs/1905.09985v1
• [cs.DC]Deploying AI Frameworks on Secure HPC Systems with Containers
David Brayford, Sofia Vallecorsa, Atanas Atanasov, Fabio Baruffa, Walter Riviera
http://arxiv.org/abs/1905.10090v1
• [cs.DC]Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang
http://arxiv.org/abs/1905.10083v1
• [cs.DC]Performance-Feedback Autoscaling with Budget Constraints for Cloud-based Workloads of Workflows
Alexey Ilyushkin, André Bauer, Alessandro V. Papadopoulos, Ewa Deelman, Alexandru Iosup
http://arxiv.org/abs/1905.10270v1
• [cs.DS]A Practical Framework for Solving Center-Based Clustering with Outliers
Hu Ding, Haikuo Yu
http://arxiv.org/abs/1905.10143v1
• [cs.DS]Hardness of Distributed Optimization
Nir Bachrach, Keren Censor-Hillel, Michal Dory, Yuval Efron, Dean Leitersdorf, Ami Paz
http://arxiv.org/abs/1905.10284v1
• [cs.HC]From Search Engines to Search Services: An End-User Driven Approach
Gabriela Bosetti, Sergio Firmenich, Alejandro Fernandez, Marco Winckler, Gustavo Rossi
http://arxiv.org/abs/1905.10215v1
• [cs.IR]Controlling Risk of Web Question Answering
ixin Su, Jiafeng Guo, Yixing Fan, Yanyan Lan, Xueqi Cheng
http://arxiv.org/abs/1905.10077v1
• [cs.IR]MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching
Jiafeng Guo, Yixing Fan, Xiang Ji, Xueqi Cheng
http://arxiv.org/abs/1905.10289v1
• [cs.IR]Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction
Qi Pi, Weijie Bian, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
http://arxiv.org/abs/1905.09248v3
• [cs.IT]Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
http://arxiv.org/abs/1905.10031v1
• [cs.IT]Contextual Bandit Learning for Machine Type Communications in the Null Space of Multi-Antenna Systems
Samad Ali, Hossein Asgharimoghaddam, Nandana Rajatheva, Walid Saad, Jussi Haapola
http://arxiv.org/abs/1905.09880v1
• [cs.IT]Do log factors matter? On optimal wavelet approximation and the foundations of compressed sensing
Ben Adcock, Simone Brugiapaglia, Matthew King-Roskamp
http://arxiv.org/abs/1905.10028v1
• [cs.IT]Minimax Rates of Estimating Approximate Differential Privacy
Xiyang Liu, Sewoong Oh
http://arxiv.org/abs/1905.10335v1
• [cs.IT]On Recurrent Neural Networks for Sequence-based Processing in Communications
Daniel Tandler, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink
http://arxiv.org/abs/1905.09983v1
• [cs.IT]On the Global Minimizers of Real Robust Phase Retrieval with Sparse Noise
Aleksandr Aravkin, James Burke, Daiwei He
http://arxiv.org/abs/1905.10358v1
• [cs.IT]On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors
Yuqing Ni, Kemi Ding, Yong Yang, Ling Shi
http://arxiv.org/abs/1905.10118v1
• [cs.IT]Secrecy Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Antenna Communications
Hong Shen, Wei Xu, Shulei Gong, Zhenyao He, Chunming Zhao
http://arxiv.org/abs/1905.10075v1
• [cs.IT]Signature codes for weighted binary adder channel and multimedia fingerprinting
Jinping Fan, Yujie Gu, Masahiro Hachimori, Ying Miao
http://arxiv.org/abs/1905.10180v1
• [cs.IT]Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
David G. Clark, Jesse A. Livezey, Kristofer E. Bouchard
http://arxiv.org/abs/1905.09944v1
• [cs.LG]Adaptive Symmetric Reward Noising for Reinforcement Learning
Refael Vivanti, Talya D. Sohlberg-Baris, Shlomo Cohen, Orna Cohen
http://arxiv.org/abs/1905.10144v1
• [cs.LG]An Explicitly Relational Neural Network Architecture
Murray Shanahan, Kyriacos Nikiforou, Antonia Creswell, Christos Kaplanis, David Barrett, Marta Garnelo
http://arxiv.org/abs/1905.10307v1
• [cs.LG]Approximation Ratios of Graph Neural Networks for Combinatorial Problems
Ryoma Sato, Makoto Yamada, Hisashi Kashima
http://arxiv.org/abs/1905.10261v1
• [cs.LG]Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar
Iddo Drori, Yamuna Krishnamurthy, Raoni Lourenco, Remi Rampin, Kyunghyun Cho, Claudio Silva, Juliana Freire
http://arxiv.org/abs/1905.10345v1
• [cs.LG]Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie
http://arxiv.org/abs/1905.10086v1
• [cs.LG]Continual Reinforcement Learning in 3D Non-stationary Environments
Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, Davide Maltoni
http://arxiv.org/abs/1905.10112v1
• [cs.LG]Copy this Sentence
Vasileios Lioutas, Andriy Drozdyuk
http://arxiv.org/abs/1905.09856v1
• [cs.LG]Curriculum Loss: Robust Learning and Generalization against Label Corruption
Yueming Lyu, Ivor W. Tsang
http://arxiv.org/abs/1905.10045v1
• [cs.LG]Deep Model Predictive Control with Online Learning for Complex Physical Systems
Katharina Bieker, Sebastian Peitz, Steven L. Brunton, J. Nathan Kutz, Michael Dellnitz
http://arxiv.org/abs/1905.10094v1
• [cs.LG]Deep density ratio estimation for change point detection
Haidar Khan, Lara Marcuse, Bülent Yener
http://arxiv.org/abs/1905.09876v1
• [cs.LG]Deep-gKnock: nonlinear group-feature selection with deep neural network
Guangyu Zhu, Tingting Zhao
http://arxiv.org/abs/1905.10013v1
• [cs.LG]Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
Gaël Letarte, Pascal Germain, Benjamin Guedj, François Laviolette
http://arxiv.org/abs/1905.10259v1
• [cs.LG]Discrete Flows: Invertible Generative Models of Discrete Data
Dustin Tran, Keyon Vafa, Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
http://arxiv.org/abs/1905.10347v1
• [cs.LG]Distributional Policy Optimization: An Alternative Approach for Continuous Control
Chen Tessler, Guy Tennenholtz, Shie Mannor
http://arxiv.org/abs/1905.09855v1
• [cs.LG]Doctor of Crosswise: Reducing Over-parametrization in Neural Networks
J. D. Curtó, H. C. Zarza
http://arxiv.org/abs/1905.10324v1
• [cs.LG]Efficient Batch Black-box Optimization with Deterministic Regret Bounds
Yueming Lyu, Yuan Yuan, Ivor W. Tsang
http://arxiv.org/abs/1905.10041v1
• [cs.LG]Embedded Meta-Learning: Toward more flexible deep-learning models
Andrew K. Lampinen, James L. McClelland
http://arxiv.org/abs/1905.09950v1
• [cs.LG]Explicitizing an Implicit Bias of the Frequency Principle in Two-layer Neural Networks
Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma
http://arxiv.org/abs/1905.10264v1
• [cs.LG]Exploration via Flow-Based Intrinsic Rewards
Hsuan-Kung Yang, Po-Han Chiang, Min-Fong Hong, Chun-Yi Lee
http://arxiv.org/abs/1905.10071v1
• [cs.LG]Federated Forest
Yang Liu, Yingting Liu, Zhijie Liu, Junbo Zhang, Chuishi Meng, Yu Zheng
http://arxiv.org/abs/1905.10053v1
• [cs.LG]Generative Grading: Neural Approximate Parsing for Automated Student Feedback
Ali Malik, Mike Wu, Vrinda Vasavada, Jinpeng Song, John Mitchell, Noah Goodman, Chris Piech
http://arxiv.org/abs/1905.09916v1
• [cs.LG]Generative adversarial network based on chaotic time series
Makoto Naruse, Takashi Matsubara, Nicolas Chauvet, Kazutaka Kanno, Tianyu Yang, Atsushi Uchida
http://arxiv.org/abs/1905.10163v1
• [cs.LG]Graph Representations for Higher-Order Logic and Theorem Proving
Aditya Paliwal, Sarah Loos, Markus Rabe, Kshitij Bansal, Christian Szegedy
http://arxiv.org/abs/1905.10006v1
• [cs.LG]Gravity-Inspired Graph Autoencoders for Directed Link Prediction
Guillaume Salha, Stratis Limnios, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis
http://arxiv.org/abs/1905.09570v2
• [cs.LG]HDI-Forest: Highest Density Interval Regression Forest
Lin Zhu, Jiaxin Lu, Yihong Chen
http://arxiv.org/abs/1905.10101v1
• [cs.LG]Interpreting a Recurrent Neural Network Model for ICU Mortality Using Learned Binary Masks
Long V. Ho, Melissa D. Aczon, David Ledbetter, Randall Wetzel
http://arxiv.org/abs/1905.09865v1
• [cs.LG]Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification
Xin Huang, Boli Chen, Lin Xiao, Liping Jing
http://arxiv.org/abs/1905.10070v1
• [cs.LG]Learning Cross-Domain Representation with Multi-Graph Neural Network
Yi Ouyang, Bin Guo, Xing Tang, Xiuqiang He, Jian Xiong, Zhiwen Yu
http://arxiv.org/abs/1905.10095v1
• [cs.LG]Learning Low-Rank Approximation for CNNs
Dongsoo Lee, Se Jung Kwon, Byeongwook Kim, Gu-Yeon Wei
http://arxiv.org/abs/1905.10145v1
• [cs.LG]Learning Surrogate Losses
Josif Grabocka, Randolf Scholz, Lars Schmidt-Thieme
http://arxiv.org/abs/1905.10108v1
• [cs.LG]Learning to learn by Self-Critique
Antreas Antoniou, Amos Storkey
http://arxiv.org/abs/1905.10295v1
• [cs.LG]Likelihood-Free Inference and Generation of Molecular Graphs
Sebastian Pölsterl, Christian Wachinger
http://arxiv.org/abs/1905.10310v1
• [cs.LG]Loss Surface Modality of Feed-Forward Neural Network Architectures
Anna Sergeevna Bosman, Andries Engelbrecht, Mardé Helbig
http://arxiv.org/abs/1905.10268v1
• [cs.LG]Memorized Sparse Backpropagation
Zhiyuan Zhang, Pengcheng Yang, Xuancheng Ren, Xu Sun
http://arxiv.org/abs/1905.10194v1
• [cs.LG]Momentum-Based Variance Reduction in Non-Convex SGD
Ashok Cutkosky, Francesco Orabona
http://arxiv.org/abs/1905.10018v1
• [cs.LG]Multi-Kernel Correntropy for Robust Learning
Badong Chen, Xin Wang, Zejian yuan, Pengju Ren, Jing Qin
http://arxiv.org/abs/1905.10115v1
• [cs.LG]Neural Temporal-Difference Learning Converges to Global Optima
Qi Cai, Zhuoran Yang, Jason D. Lee, Zhaoran Wang
http://arxiv.org/abs/1905.10027v1
• [cs.LG]Neuro-Optimization: Learning Objective Functions Using Neural Networks
Younghan Jeon, Minsik Lee, Jin Young Choi
http://arxiv.org/abs/1905.10079v1
• [cs.LG]Not All Features Are Equal: Feature Leveling Deep Neural Networks for Better Interpretation
Yingjing Lu, Runde Yang
http://arxiv.org/abs/1905.10009v1
• [cs.LG]On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks
Bing Yu, Junzhao Zhang, Zhanxing Zhu
http://arxiv.org/abs/1905.10157v1
• [cs.LG]Optimizing Shallow Networks for Binary Classification
Kalliopi Basioti, George V. Moustakides
http://arxiv.org/abs/1905.10161v1
• [cs.LG]Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
Sharan Vaswani, Aaron Mishkin, Issam Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
http://arxiv.org/abs/1905.09997v1
• [cs.LG]Partially Encrypted Machine Learning using Functional Encryption
Theo Ryffel, Edouard Dufour Sans, Romain Gay, Francis Bach, David Pointcheval
http://arxiv.org/abs/1905.10214v1
• [cs.LG]Perturbed Model Validation: A New Framework to Validate Model Relevance
Jie M. Zhang, Earl T. Barr, Benjamin Guedj, Mark Harman, John Shawe-Taylor
http://arxiv.org/abs/1905.10201v1
• [cs.LG]Power up! Robust Graph Convolutional Network against Evasion Attacks based on Graph Powering
Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi
http://arxiv.org/abs/1905.10029v1
• [cs.LG]Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective
Changjian Li, Krzysztof Czarnecki
http://arxiv.org/abs/1905.10016v1
• [cs.LG]Robust Attribution Regularization
Jiefeng Chen, Xi Wu, Vaibhav Rastogi, Yingyu Liang, Somesh Jha
http://arxiv.org/abs/1905.09957v1
• [cs.LG]SCRAM: Spatially Coherent Randomized Attention Maps
Dan A. Calian, Peter Roelants, Jacques Cali, Ben Carr, Krishna Dubba, John E. Reid, Dell Zhang
http://arxiv.org/abs/1905.10308v1
• [cs.LG]STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Lei Bai, Lina Yao, Salil. S Kanhere, Xianzhi Wang, Quan. Z Sheng
http://arxiv.org/abs/1905.10069v1
• [cs.LG]Semi-Supervised Classification on Non-Sparse Graphs Using Low-Rank Graph Convolutional Networks
Dominik Alfke, Martin Stoll
http://arxiv.org/abs/1905.10224v1
• [cs.LG]Statistical embedding for directed graphs
Thorben Funke, Tian Guo, Alen Lancic, Nino Antulov-Fantulin
http://arxiv.org/abs/1905.10227v1
• [cs.LG]Structured Compression by Unstructured Pruning for Sparse Quantized Neural Networks
Se Jung Kwon, Dongsoo Lee, Byeongwook Kim, Parichay Kapoor, Baeseong Park, Gu-Yeon Wei
http://arxiv.org/abs/1905.10138v1
• [cs.LG]Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Boris Muzellec, Marco Cuturi
http://arxiv.org/abs/1905.10099v1
• [cs.LG]The advantages of multiple classes for reducing overfitting from test set reuse
Vitaly Feldman, Roy Frostig, Moritz Hardt
http://arxiv.org/abs/1905.10360v1
• [cs.LG]Training decision trees as replacement for convolution layers
Wolfgang Fuhl, Gjergji Kasneci, Wolfgang Rosenstiel, Enkelejda Kasneci
http://arxiv.org/abs/1905.10073v1
• [cs.LG]What Can ResNet Learn Efficiently, Going Beyond Kernels?
Zeyuan Allen-Zhu, Yuanzhi Li
http://arxiv.org/abs/1905.10337v1
• [cs.LG]X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations
Alberto Marchisio, Beatrice Bussolino, Alessio Colucci, Muhammad Abdullah Hanif, Maurizio Martina, Guido Masera, Muhammad Shafique
http://arxiv.org/abs/1905.10142v1
• [cs.MA]Decentralized Informative Path Planning with Exploration-Exploitation Balance for Swarm Robotic Search
Payam Ghassemi, Souma Chowdhury
http://arxiv.org/abs/1905.09988v1
• [cs.MA]winPIBT: Expanded Prioritized Algorithm for Iterative Multi-agent Path Finding
Keisuke Okumura, Yasumasa Tamura, Xavier Défago
http://arxiv.org/abs/1905.10149v1
• [cs.NE]Instruction-Level Design of Local Optimisers using Push GP
Michael Lones
http://arxiv.org/abs/1905.10245v1
• [cs.RO]Designing an Inertia Actuator with a Fast Rotating Gyro inside an Egg-shaped Robot
Chun-Chi Wang, He-Zhi Liu, Rui-Yuan Lin, Li-Yang Lu, N. Michael Mayer
http://arxiv.org/abs/1905.10134v1
• [cs.RO]Mechatronic Design of a Dribbling System for RoboCup Small Size Robot
Zheyuan Huang, Yunkai Wang, Lingyun Chen, Jiacheng Li, Zexi Chen, Rong Xiong
http://arxiv.org/abs/1905.09934v1
• [cs.RO]Scene Induced Multi-Modal Trajectory Forecasting via Planning
Nachiket Deo, Mohan M. Trivedi
http://arxiv.org/abs/1905.09949v1
• [cs.RO]Visual Model-predictive Localization for Computationally Efficient Autonomous Racing of a 72-gram Drone
Shuo Li, Erik van der Horst, Philipp Duernay, Christophe De Wagter, Guido C. H. E. de Croon
http://arxiv.org/abs/1905.10110v1
• [cs.SD]Disentangled Feature for Weakly Supervised Multi-class Sound Event Detection
Liwei Lin, Xiangdong Wang, Hong Liu, Yueliang Qian
http://arxiv.org/abs/1905.10091v1
• [cs.SE]A Customised App to Attract Female Teenagers to Coding
Bernadette Spieler, Wolfgang Slany
http://arxiv.org/abs/1905.10065v1
• [cs.SI]An Integrated Model for User Innovation Knowledge Based on Super-network
Xiao Liao, Zhihong Li, Yunjiang Xi, Haibo Wang, Kenneth Zantow
http://arxiv.org/abs/1905.09923v1
• [cs.SI]Extended Scale-Free Networks
Arthur Charpentier, Emmanuel Flachaire
http://arxiv.org/abs/1905.10267v1
• [cs.SI]Multifaceted Privacy: How to Express Your Online Persona without Revealing Your Sensitive Attributes
Victor Zakhary, Ishani Gupta, Rey Tang, Amr El Abbadi
http://arxiv.org/abs/1905.09945v1
• [cs.SI]Tempus Volat, Hora Fugit — A Survey of Dynamic Network Models in Discrete and Continuous Time
Cornelius Fritz, Michael Lebacher, Göran Kauermann
http://arxiv.org/abs/1905.10351v1
• [econ.EM]Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis
http://arxiv.org/abs/1905.10176v1
• [econ.EM]Semi-Parametric Efficient Policy Learning with Continuous Actions
Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov
http://arxiv.org/abs/1905.10116v1
• [eess.IV]A Research and Strategy of Remote Sensing Image Denoising Algorithms
Ling Li, Junxing Hu, Fengge Wu, Junsuo Zhao
http://arxiv.org/abs/1905.10236v1
• [eess.IV]Functional Segmentation through Dynamic Mode Decomposition: Automatic Quantification of Kidney Function in DCE-MRI Images
Santosh Tirunagari, Norman Poh, Kevin Wells, Miroslaw Bober, Isky Gorden, David Windridge
http://arxiv.org/abs/1905.10218v1
• [eess.IV]Tissue segmentation with deep 3D networks and spatial priors
Lukas Hirsch, Yu Huang, Lucas C Parra
http://arxiv.org/abs/1905.10010v1
• [math.CO]A concatenation construction for propelinear perfect codes from regular subgroups of GA(r,2)
I. Yu. Mogilnykh, F. I. Solov’eva
http://arxiv.org/abs/1905.10005v1
• [math.PR]Revisiting Relations between Stochastic Ageing and Dependence for Exchangeable Lifetimes with an Extension for the IFRA/DFRA Property
Giovanna Nappo, Fabio L. Spizzichino
http://arxiv.org/abs/1905.10326v1
• [math.PR]The Skipping Sampler: A new approach to sample from complex conditional densities
John Moriarty, Jure Vogrinc, Alessandro Zocca
http://arxiv.org/abs/1905.09964v1
• [math.ST]Asymptotic Behaviour of Discretised Functionals of Long-Range Dependent Functional Data
Tareq Alodat, Andriy Olenko
http://arxiv.org/abs/1905.10030v1
• [math.ST]High-Dimensional Functional Factor Models
Gilles Nisol, Shahin Tavakoli, Marc Hallin
http://arxiv.org/abs/1905.10325v1
• [math.ST]Likelihood ratio tests for many groups in high dimensions
Holger Dette, Nina Dörnemann
http://arxiv.org/abs/1905.10354v1
• [math.ST]Nonparametric Bootstrap Inference for the Targeted Highly Adaptive LASSO Estimator
Weixin Cai, Mark van der Laan
http://arxiv.org/abs/1905.10299v1
• [physics.soc-ph]Morphological organization of point-to-point transport in complex networks
Min-Yeong Kang, Geoffroy Berthelot, Liubov Tupikina, Christos Nicolaides, Jean-Francois Colonna, Bernard Sapoval, Denis S. Grebenkov
http://arxiv.org/abs/1905.10333v1
• [q-bio.NC]Damped oscillations of the probability of random events followed by absolute refractory period
A. V. Paraskevov, A. S. Minkin
http://arxiv.org/abs/1905.10172v1
• [stat.AP]Inference of Dynamic Graph Changes for Functional Connectome
Dingjue Ji, Junwei Lu, Yiliang Zhang, Hongyu Zhao, Siyuan Gao
http://arxiv.org/abs/1905.09993v1
• [stat.AP]The experiment is just as important as the likelihood in understanding the prior: A cautionary note on robust cognitive modelling
Lauren Kennedy, Daniel Simpson, Andrew Gelman
http://arxiv.org/abs/1905.10341v1
• [stat.CO]A Single SMC Sampler on MPI that Outperforms a Single MCMC Sampler
Alessandro Varsi, Lykourgos Kekempanos, Jeyarajan Thiyagalingam, Simon Maskell
http://arxiv.org/abs/1905.10252v1
• [stat.CO]Divide-and-Conquer Information-Based Optimal Subdata Selection Algorithm
HaiYing Wang
http://arxiv.org/abs/1905.09948v1
• [stat.CO]Estimating Convergence of Markov chains with L-Lag Couplings
Niloy Biswas, Pierre E. Jacob
http://arxiv.org/abs/1905.09971v1
• [stat.CO]Monitoring dynamic networks: a simulation-based strategy for comparing monitoring methods and a comparative study
Lisha Yu, Inez M. Zwetsloot, Nathaniel T. Stevens, James D. Wilson, Kwok Leung Tsui
http://arxiv.org/abs/1905.10302v1
• [stat.CO]Parallel Coordinate Order for High-Dimensional Data
Shaima Tilouche, Vahid Partovi Nia, Samuel Bassetto
http://arxiv.org/abs/1905.10035v1
• [stat.ME]Adaptive Function-on-Scalar Regression with a Smoothing Elastic Net
Ardalan Mirshani, Matthew Reimherr
http://arxiv.org/abs/1905.09881v1
• [stat.ME]Optimal nonparametric change point detection and localization
Oscar Hernan Madrid Padilla, Yi Yu, Daren Wang, Alessandro Rinaldo
http://arxiv.org/abs/1905.10019v1
• [stat.ML]Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation
Rémi Flamary, Karim Lounici, André Ferrari
http://arxiv.org/abs/1905.10155v1
• [stat.ML]Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Motonobu Kanagawa, Philipp Hennig
http://arxiv.org/abs/1905.10271v1
• [stat.ML]Dirac Delta Regression: Conditional Density Estimation with Clinical Trials
Eric V. Strobl, Shyam Visweswaran
http://arxiv.org/abs/1905.10330v1
• [stat.ML]OSOM: A Simultaneously Optimal Algorithm for Multi-Armed and Linear Contextual Bandits
Niladri S. Chatterji, Vidya Muthukumar, Peter L. Bartlett
http://arxiv.org/abs/1905.10040v1
• [stat.ML]Polynomial Cost of Adaptation for X -Armed Bandits
Hédi Hadiji
http://arxiv.org/abs/1905.10221v1
• [stat.ML]Posterior Distribution for the Number of Clusters in Dirichlet Process Mixture Models
Chiao-Yu Yang, Nhat Ho, Michael I. Jordan
http://arxiv.org/abs/1905.09959v1
• [stat.ML]Privacy Risks of Securing Machine Learning Models against Adversarial Examples
Liwei Song, Reza Shokri, Prateek Mittal
http://arxiv.org/abs/1905.10291v1
• [stat.ML]Sequential Gaussian Processes for Online Learning of Nonstationary Functions
Michael Minyi Zhang, Bianca Dumitrascu, Sinead A. Williamson, Barbara E. Engelhardt
http://arxiv.org/abs/1905.10003v1
• [stat.ML]Sliced Gromov-Wasserstein
Titouan Vayer, Rémi Flamary, Romain Tavenard, Laetitia Chapel, Nicolas Courty
http://arxiv.org/abs/1905.10124v1
• [stat.ML]forgeNet: A graph deep neural network model using tree-based ensemble classifiers for feature extraction
Yunchuan Kong, Tianwei Yu
http://arxiv.org/abs/1905.09889v1