cs.AI - 人工智能

    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.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 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.SD - 声音处理 cs.SI - 社交网络与信息网络 cs.SY - 系统与控制 eess.IV - 图像与视频处理 eess.SP - 信号处理 math-ph - 数学物理 math.ST - 统计理论 q-bio.QM - 定量方法 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval
    • [cs.AI]AI Enabling Technologies: A Survey
    • [cs.AI]General Method for Prime-point Cyclic Convolution over the Real Field
    • [cs.AI]Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial Imagination
    • [cs.CL]Targeted Sentiment Analysis: A Data-Driven Categorization
    • [cs.CR]Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack
    • [cs.CR]Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection — An Analysis on CIC-AWS-2018 dataset
    • [cs.CR]Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain
    • [cs.CR]Practical Algebraic Attack on DAGS
    • [cs.CV]A Dual Path ModelWith Adaptive Attention For Vehicle Re-Identification
    • [cs.CV]Advancements in Image Classification using Convolutional Neural Network
    • [cs.CV]Cycle-IR: Deep Cyclic Image Retargeting
    • [cs.CV]D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
    • [cs.CV]Deep Closest Point: Learning Representations for Point Cloud Registration
    • [cs.CV]Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications
    • [cs.CV]DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
    • [cs.CV]Embedding Human Knowledge in Deep Neural Network via Attention Map
    • [cs.CV]Fast and Efficient Zero-Learning Image Fusion
    • [cs.CV]Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images
    • [cs.CV]Forecasting Pedestrian Trajectory with Machine-Annotated Training Data
    • [cs.CV]Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
    • [cs.CV]Grand Challenge of 106-Point Facial Landmark Localization
    • [cs.CV]Handheld Multi-Frame Super-Resolution
    • [cs.CV]Interactive Image Generation Using Scene Graphs
    • [cs.CV]Intra-frame Object Tracking by Deblatting
    • [cs.CV]Learning Interpretable Features via Adversarially Robust Optimization
    • [cs.CV]Learning Loss for Active Learning
    • [cs.CV]Learning Representations for Predicting Future Activities
    • [cs.CV]Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
    • [cs.CV]Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
    • [cs.CV]PPGNet: Learning Point-Pair Graph for Line Segment Detection
    • [cs.CV]ROSA: Robust Salient Object Detection against Adversarial Attacks
    • [cs.CV]S$^\mathbf{4}$L: Self-Supervised Semi-Supervised Learning
    • [cs.CV]Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
    • [cs.CV]TE141K: Artistic Text Benchmark for Text Effects Transfer
    • [cs.CV]Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection
    • [cs.CV]Weakly Labeling the Antarctic: The Penguin Colony Case
    • [cs.CV]What Do Single-view 3D Reconstruction Networks Learn?
    • [cs.CY]Designing technology, developing theory. Towards a symmetrical approach
    • [cs.CY]Entrofy Your Cohort: A Data Science Approach to Candidate Selection
    • [cs.DC]Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox
    • [cs.DC]parasweep: A template-based utility for generating, dispatching, and post-processing of parameter sweeps
    • [cs.DL]Interdisciplinary Relationships Between Biological and Physical Sciences
    • [cs.DS]Coresets for Minimum Enclosing Balls over Sliding Windows
    • [cs.DS]Linear Work Generation of R-MAT Graphs
    • [cs.DS]Variable Neighborhood Search for the Bin Packing Problem with Compatible Categories
    • [cs.IR]Compositional Coding for Collaborative Filtering
    • [cs.IR]Embarrassingly Shallow Autoencoders for Sparse Data
    • [cs.IT]Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency
    • [cs.IT]Explicit representation for a class of Type 2 constacyclic codes over the ring $\mathbb{F}_{2^m}[u]/\langle u^{2λ}\rangle$ with even length
    • [cs.IT]Fog-Aided Device to Device Networks with Opportunistic Content Delivery
    • [cs.IT]Fundamental Limits of Identification System With Secret Binding Under Noisy Enrollment
    • [cs.IT]Joint power and resource allocation of D2D communication with low-resolution ADC
    • [cs.IT]Learning Erdős-Rényi Random Graphs via Edge Detecting Queries
    • [cs.IT]On Coverage Probability in Uplink NOMA With Instantaneous Signal Power-Based User Ranking
    • [cs.IT]Online Trajectory Optimization for Rotary-Wing UAVs in Wireless Networks
    • [cs.IT]Stochastic Fading Channel Models with Multiple Dominant Specular Components for 5G and Beyond
    • [cs.LG]Adversarial Defense Framework for Graph Neural Network
    • [cs.LG]Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems
    • [cs.LG]AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
    • [cs.LG]Data-Efficient Mutual Information Neural Estimator
    • [cs.LG]Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
    • [cs.LG]Differentiable Approximation Bridges For Training Networks Containing Non-Differentiable Functions
    • [cs.LG]Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
    • [cs.LG]Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
    • [cs.LG]Learning Embeddings into Entropic Wasserstein Spaces
    • [cs.LG]Limits of Deepfake Detection: A Robust Estimation Viewpoint
    • [cs.LG]MAP Inference via L2-Sphere Linear Program Reformulation
    • [cs.LG]Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
    • [cs.LG]PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets
    • [cs.LG]Pretrain Soft Q-Learning with Imperfect Demonstrations
    • [cs.LG]Proportionally Fair Clustering
    • [cs.LG]Reconstruction of Privacy-Sensitive Data from Protected Templates
    • [cs.LG]Regression from Dependent Observations
    • [cs.LG]Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
    • [cs.LG]The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
    • [cs.LO]SMT-based Constraint Answer Set Solver EZSMT+
    • [cs.NE]A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem
    • [cs.NE]Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
    • [cs.NE]Classificação de espécies de peixe utilizando redes neurais convolucional
    • [cs.NE]Learning to Evolve
    • [cs.NE]Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models
    • [cs.NI]Path Design for Cellular-Connected UAV with Reinforcement Learning
    • [cs.NI]Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
    • [cs.RO]An Omnidirectional Aerial Manipulation Platform for Contact-Based Inspection
    • [cs.RO]Configuration-Space Flipper Planning for Rescue Robots
    • [cs.RO]Feature-Based Transfer Learning for Robotic Push Manipulation
    • [cs.RO]Model predictive approach to integrated path planning and tracking for autonomous vehicles
    • [cs.RO]Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
    • [cs.SD]Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech
    • [cs.SD]Universal Sound Separation
    • [cs.SI]Embedding vertex intrinsic relevance in network analysis: the case of Betweenness
    • [cs.SI]Fairness across Network Positions in Cyberbullying Detection Algorithms
    • [cs.SY]Prioritized Inverse Kinematics: Nonsmoothness, Trajectory Existence, Task Convergence, Stability
    • [cs.SY]Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
    • [eess.IV]QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field
    • [eess.SP]1D Convolutional Neural Networks and Applications: A Survey
    • [eess.SP]Collaborative Localization and Tracking with Minimal Infrastructure
    • [math-ph]Bounds on Lyapunov exponents
    • [math.ST]Double-calibration estimators accounting for under-coverage and nonresponse in socio-economic surveys
    • [math.ST]Non-Asymptotic Sequential Tests for Overlapping Hypotheses and application to near optimal arm identification in bandit models
    • [math.ST]On Semi-parametric Bernstein-von Mises Theorems for BART
    • [q-bio.QM]The Identification and Analysis of Indicators for Predicting Malarial Incidence in Zimbabwe
    • [stat.AP]Automatic multiscale approach for water networks partitioning into dynamic district metered areas
    • [stat.AP]Bias in the estimation of cumulative viremia in cohort studies of HIV-infected individuals
    • [stat.AP]Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson Regression
    • [stat.AP]The unfairness of the UEFA Euro 2020 qualifying
    • [stat.CO]Stein Point Markov Chain Monte Carlo
    • [stat.ME]Approximate Bayesian computation with the Wasserstein distance
    • [stat.ME]Conformal prediction for exponential families and generalized linear models
    • [stat.ML]A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
    • [stat.ML]A Novel Adaptive Kernel for the RBF Neural Networks
    • [stat.ML]Best-scored Random Forest Density Estimation
    • [stat.ML]Importance Weighted Hierarchical Variational Inference
    • [stat.ML]Two-stage Best-scored Random Forest for Large-scale Regression

    ·····································

    • [cs.AI]2-bit Model Compression of Deep Convolutional Neural Network on ASIC Engine for Image Retrieval
    Bin Yang, Lin Yang, Xiaochun Li, Wenhan Zhang, Hua Zhou, Yequn Zhang, Yongxiong Ren, Yinbo Shi
    http://arxiv.org/abs/1905.03362v1

    • [cs.AI]AI Enabling Technologies: A Survey
    Vijay Gadepally, Justin Goodwin, Jeremy Kepner, Albert Reuther, Hayley Reynolds, Siddharth Samsi, Jonathan Su, David Martinez
    http://arxiv.org/abs/1905.03592v1

    • [cs.AI]General Method for Prime-point Cyclic Convolution over the Real Field
    Qi Cai, Tsung-Ching Lin, Yuanxin Wu, Wenxian Yu, Trieu-Kien Truong
    http://arxiv.org/abs/1905.03398v1

    • [cs.AI]Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial Imagination
    Ruixue Liu, Baoyang Chen, Meng Chen, Youzheng Wu, Zhijie Qiu, Xiaodong He
    http://arxiv.org/abs/1905.03638v1

    • [cs.CL]Targeted Sentiment Analysis: A Data-Driven Categorization
    Jiaxin Pei, Aixin Sun, Chenliang Li
    http://arxiv.org/abs/1905.03423v1

    • [cs.CR]Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attack
    Di Zhao, Jiqiang Liu, Jialin Wang, Wenjia Niu, Endong Tong, Tong Chen, Gang Li
    http://arxiv.org/abs/1905.03454v1

    • [cs.CR]Evaluation of Machine Learning Classifiers for Zero-Day Intrusion Detection — An Analysis on CIC-AWS-2018 dataset
    Qianru Zhou, Dimitrios Pezaros
    http://arxiv.org/abs/1905.03685v1

    • [cs.CR]Mitigating Deep Learning Vulnerabilities from Adversarial Examples Attack in the Cybersecurity Domain
    Chris Einar San Agustin
    http://arxiv.org/abs/1905.03517v1

    • [cs.CR]Practical Algebraic Attack on DAGS
    Magali Bardet, Manon Bertin, Alain Couvreur, Ayoub Otmani
    http://arxiv.org/abs/1905.03635v1

    • [cs.CV]A Dual Path ModelWith Adaptive Attention For Vehicle Re-Identification
    Pirazh Khorramshahi, Amit Kumar, Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, Rama Chellappa
    http://arxiv.org/abs/1905.03397v1

    • [cs.CV]Advancements in Image Classification using Convolutional Neural Network
    Farhana Sultana, A. Sufian, Paramartha Dutta
    http://arxiv.org/abs/1905.03288v1

    • [cs.CV]Cycle-IR: Deep Cyclic Image Retargeting
    Weimin Tan, Bo Yan, Chumin Lin, Xuejing Niu
    http://arxiv.org/abs/1905.03556v1

    • [cs.CV]D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
    Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, Torsten Sattler
    http://arxiv.org/abs/1905.03561v1

    • [cs.CV]Deep Closest Point: Learning Representations for Point Cloud Registration
    Yue Wang, Justin M. Solomon
    http://arxiv.org/abs/1905.03304v1

    • [cs.CV]Deep Learning Acceleration Techniques for Real Time Mobile Vision Applications
    Gael Kamdem De Teyou
    http://arxiv.org/abs/1905.03418v1

    • [cs.CV]DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
    Erkun Yang, Tongliang Liu, Cheng Deng, Wei Liu, Dacheng Tao
    http://arxiv.org/abs/1905.03465v1

    • [cs.CV]Embedding Human Knowledge in Deep Neural Network via Attention Map
    Masahiro Mitsuhara, Hiroshi Fukui, Yusuke Sakashita, Takanori Ogata, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi
    http://arxiv.org/abs/1905.03540v1

    • [cs.CV]Fast and Efficient Zero-Learning Image Fusion
    Fayez Lahoud, Sabine Süsstrunk
    http://arxiv.org/abs/1905.03590v1

    • [cs.CV]Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images
    Wen-Shuai Hu, Heng-Chao Li, Lei Pan, Wei Li, Ran Tao, Qian Du
    http://arxiv.org/abs/1905.03577v1

    • [cs.CV]Forecasting Pedestrian Trajectory with Machine-Annotated Training Data
    Olly Styles, Arun Ross, Victor Sanchez
    http://arxiv.org/abs/1905.03681v1

    • [cs.CV]Frustratingly Easy Person Re-Identification: Generalizing Person Re-ID in Practice
    Jieru Jia, Qiuqi Ruan, Timothy M. Hospedales
    http://arxiv.org/abs/1905.03422v1

    • [cs.CV]Grand Challenge of 106-Point Facial Landmark Localization
    Yinglu Liu, Hao Shen, Yue Si, Xiaobo Wang, Xiangyu Zhu, Hailin Shi, Zhibin Hong, Hanqi Guo, Ziyuan Guo, Yanqin Chen, Bi Li, Teng Xi, Jun Yu, Haonian Xie, Guochen Xie, Mengyan Li, Qing Lu, Zengfu Wang, Shenqi Lai, Zhenhua Chai, Xiaoming Wei
    http://arxiv.org/abs/1905.03469v1

    • [cs.CV]Handheld Multi-Frame Super-Resolution
    Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, Peyman Milanfar
    http://arxiv.org/abs/1905.03277v1

    • [cs.CV]Interactive Image Generation Using Scene Graphs
    Gaurav Mittal, Shubham Agrawal, Anuva Agarwal, Sushant Mehta, Tanya Marwah
    http://arxiv.org/abs/1905.03743v1

    • [cs.CV]Intra-frame Object Tracking by Deblatting
    Jan Kotera, Denys Rozumnyi, Filip Šroubek, Jiří Matas
    http://arxiv.org/abs/1905.03633v1

    • [cs.CV]Learning Interpretable Features via Adversarially Robust Optimization
    Ashkan Khakzar, Shadi Albarqouni, Nassir Navab
    http://arxiv.org/abs/1905.03767v1

    • [cs.CV]Learning Loss for Active Learning
    Donggeun Yoo, In So Kweon
    http://arxiv.org/abs/1905.03677v1

    • [cs.CV]Learning Representations for Predicting Future Activities
    Mohammadreza Zolfaghari, Özgün Çiçek, Syed Mohsin Ali, Farzaneh Mahdisoltani, Can Zhang, Thomas Brox
    http://arxiv.org/abs/1905.03578v1

    • [cs.CV]Liver Lesion Segmentation with slice-wise 2D Tiramisu and Tversky loss function
    Karsten Roth, Tomasz Konopczyński, Jürgen Hesser
    http://arxiv.org/abs/1905.03639v1

    • [cs.CV]Multi-Person Pose Estimation with Enhanced Channel-wise and Spatial Information
    Kai Su, Dongdong Yu, Zhenqi Xu, Xin Geng, Changhu Wang
    http://arxiv.org/abs/1905.03466v1

    • [cs.CV]PPGNet: Learning Point-Pair Graph for Line Segment Detection
    Ziheng Zhang, Zhengxin Li, Ning Bi, Jia Zheng, Jinlei Wang, Kun Huang, Weixin Luo, Yanyu Xu, Shenghua Gao
    http://arxiv.org/abs/1905.03415v1

    • [cs.CV]ROSA: Robust Salient Object Detection against Adversarial Attacks
    Haofeng Li, Guanbin Li, Yizhou Yu
    http://arxiv.org/abs/1905.03434v1

    • [cs.CV]S$^\mathbf{4}$L: Self-Supervised Semi-Supervised Learning
    Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer
    http://arxiv.org/abs/1905.03670v1

    • [cs.CV]Seesaw-Net: Convolution Neural Network With Uneven Group Convolution
    Jintao Zhang
    http://arxiv.org/abs/1905.03672v1

    • [cs.CV]TE141K: Artistic Text Benchmark for Text Effects Transfer
    Shuai Yang, Wenjing Wang, Jiaying Liu
    http://arxiv.org/abs/1905.03646v1

    • [cs.CV]Two-Stage Convolutional Neural Network Architecture for Lung Nodule Detection
    Haichao Cao, Hong Liu, Enmin Song, Guangzhi Ma, Xiangyang Xu, Renchao Jin, Tengying Liu, Chih-Cheng Hung
    http://arxiv.org/abs/1905.03445v1

    • [cs.CV]Weakly Labeling the Antarctic: The Penguin Colony Case
    Hieu Le, Bento Gonçalves, Dimitris Samaras, Heather Lynch
    http://arxiv.org/abs/1905.03313v1

    • [cs.CV]What Do Single-view 3D Reconstruction Networks Learn?
    Maxim Tatarchenko, Stephan R. Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, Thomas Brox
    http://arxiv.org/abs/1905.03678v1

    • [cs.CY]Designing technology, developing theory. Towards a symmetrical approach
    Cornelius Schubert, Andreas Kolb
    http://arxiv.org/abs/1905.03580v1

    • [cs.CY]Entrofy Your Cohort: A Data Science Approach to Candidate Selection
    D. Huppenkothen, B. McFee, L. Norén
    http://arxiv.org/abs/1905.03314v1

    • [cs.DC]Arbitrarily large iterative tomographic reconstruction on multiple GPUs using the TIGRE toolbox
    Ander Biguri, Reuben Lindroos, Robert Bryll, Hossein Towsyfyan, Hans Deyhle, Richard Boardman, Mark Mavrogordato, Manjit Dosanjh, Steven Hancock, Thomas Blumensath
    http://arxiv.org/abs/1905.03748v1

    • [cs.DC]parasweep: A template-based utility for generating, dispatching, and post-processing of parameter sweeps
    Eviatar Bach
    http://arxiv.org/abs/1905.03448v1

    • [cs.DL]Interdisciplinary Relationships Between Biological and Physical Sciences
    Paulo E. P. Burke, Luciano da F. Costa
    http://arxiv.org/abs/1905.03298v1

    • [cs.DS]Coresets for Minimum Enclosing Balls over Sliding Windows
    Yanhao Wang, Yuchen Li, Kian-Lee Tan
    http://arxiv.org/abs/1905.03718v1

    • [cs.DS]Linear Work Generation of R-MAT Graphs
    Lorenz Hübschle-Schneider, Peter Sanders
    http://arxiv.org/abs/1905.03525v1

    • [cs.DS]Variable Neighborhood Search for the Bin Packing Problem with Compatible Categories
    Luiz F. O. Moura Santos, Hugo T. Y. Yoshizaki, Claudio B. Cunha
    http://arxiv.org/abs/1905.03427v1

    • [cs.IR]Compositional Coding for Collaborative Filtering
    Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi
    http://arxiv.org/abs/1905.03752v1

    • [cs.IR]Embarrassingly Shallow Autoencoders for Sparse Data
    Harald Steck
    http://arxiv.org/abs/1905.03375v1

    • [cs.IT]Deep Learning for TDD and FDD Massive MIMO: Mapping Channels in Space and Frequency
    Muhammad Alrabeiah, Ahmed Alkhateeb
    http://arxiv.org/abs/1905.03761v1

    • [cs.IT]Explicit representation for a class of Type 2 constacyclic codes over the ring $\mathbb{F}_{2^m}[u]/\langle u^{2λ}\rangle$ with even length**
    Yuan Cao, Yonglin Cao, Hai Q. Dinh, Songsak Sriboonchitta, Guidong Wang
    http://arxiv.org/abs/1905.03621v1

    • [cs.IT]Fog-Aided Device to Device Networks with Opportunistic Content Delivery
    Xiaoshi Song, Mengying Yuan, Chao Jia, Weimin Lei, Haijun Zhang
    http://arxiv.org/abs/1905.03527v1

    • [cs.IT]Fundamental Limits of Identification System With Secret Binding Under Noisy Enrollment
    Vamoua Yachongka, Hideki Yagi
    http://arxiv.org/abs/1905.03598v1

    • [cs.IT]Joint power and resource allocation of D2D communication with low-resolution ADC
    Muralikrishnan Srinivasan, Athira Subhash, Sheetal Kalyani
    http://arxiv.org/abs/1905.03443v1

    • [cs.IT]Learning Erdős-Rényi Random Graphs via Edge Detecting Queries
    Zihan Li, Matthias Fresacher, Jonathan Scarlett
    http://arxiv.org/abs/1905.03410v1

    • [cs.IT]On Coverage Probability in Uplink NOMA With Instantaneous Signal Power-Based User Ranking
    Mohammad Salehi, Ekram Hossain
    http://arxiv.org/abs/1905.03293v1

    • [cs.IT]Online Trajectory Optimization for Rotary-Wing UAVs in Wireless Networks
    Matthew Bliss, Nicolò Michelusi
    http://arxiv.org/abs/1905.01755v2

    • [cs.IT]Stochastic Fading Channel Models with Multiple Dominant Specular Components for 5G and Beyond
    Juan M. Romero-Jerez, F. Javier Lopez-Martinez, Juan P. Peña-Martin, Ali Abdi
    http://arxiv.org/abs/1905.03567v1

    • [cs.LG]Adversarial Defense Framework for Graph Neural Network
    Shen Wang, Zhengzhang Chen, Jingchao Ni, Xiao Yu, Zhichun Li, Haifeng Chen, Philip S. Yu
    http://arxiv.org/abs/1905.03679v1

    • [cs.LG]Adversarial Image Translation: Unrestricted Adversarial Examples in Face Recognition Systems
    Kazuya Kakizaki, Kosuke Yoshida
    http://arxiv.org/abs/1905.03421v1

    • [cs.LG]AutoAssist: A Framework to Accelerate Training of Deep Neural Networks
    Jiong Zhang, Hsiang-fu Yu, Inderjit S. Dhillon
    http://arxiv.org/abs/1905.03381v1

    • [cs.LG]Data-Efficient Mutual Information Neural Estimator
    Xiao Lin, Indranil Sur, Samuel A. Nastase, Ajay Divakaran, Uri Hasson, Mohamed R. Amer
    http://arxiv.org/abs/1905.03319v1

    • [cs.LG]Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation
    Colin Wei, Tengyu Ma
    http://arxiv.org/abs/1905.03684v1

    • [cs.LG]Differentiable Approximation Bridges For Training Networks Containing Non-Differentiable Functions
    Jason Ramapuram, Russ Webb
    http://arxiv.org/abs/1905.03658v1

    • [cs.LG]Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
    Yunhan Jia, Yantao Lu, Senem Velipasalar, Zhenyu Zhong, Tao Wei
    http://arxiv.org/abs/1905.03333v1

    • [cs.LG]Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
    Chirag Nagpal, Dennis Wei, Bhanukiran Vinzamuri, Monica Shekhar, Sara E. Berger, Subhro Das, Kush R. Varshney
    http://arxiv.org/abs/1905.03297v1

    • [cs.LG]Learning Embeddings into Entropic Wasserstein Spaces
    Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
    http://arxiv.org/abs/1905.03329v1

    • [cs.LG]Limits of Deepfake Detection: A Robust Estimation Viewpoint
    Sakshi Agarwal, Lav R. Varshney
    http://arxiv.org/abs/1905.03493v1

    • [cs.LG]MAP Inference via L2-Sphere Linear Program Reformulation
    Baoyuan Wu, Li Shen, Bernard Ghanem, Tong Zhang
    http://arxiv.org/abs/1905.03433v1

    • [cs.LG]Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models
    Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado
    http://arxiv.org/abs/1905.03406v1

    • [cs.LG]PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets
    Priyadarshini K, Siddhartha Chaudhuri, Subhasis Chaudhuri
    http://arxiv.org/abs/1905.03302v1

    • [cs.LG]Pretrain Soft Q-Learning with Imperfect Demonstrations
    Xiaoqin Zhang, Yunfei Li, Huimin Ma, Xiong Luo
    http://arxiv.org/abs/1905.03501v1

    • [cs.LG]Proportionally Fair Clustering
    Xingyu Chen, Brandon Fain, Charles Lyu, Kamesh Munagala
    http://arxiv.org/abs/1905.03674v1

    • [cs.LG]Reconstruction of Privacy-Sensitive Data from Protected Templates
    Shideh Rezaeifar, Behrooz Razeghi, Olga Taran, Taras Holotyak, Slava Voloshynovskiy
    http://arxiv.org/abs/1905.03282v1

    • [cs.LG]Regression from Dependent Observations
    Constantinos Daskalakis, Nishanth Dikkala, Ioannis Panageas
    http://arxiv.org/abs/1905.03353v1

    • [cs.LG]Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
    Baojian Zhou, Feng Chen, Yiming Ying
    http://arxiv.org/abs/1905.03652v1

    • [cs.LG]The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study
    Daniel S. Park, Jascha Sohl-Dickstein, Quoc V. Le, Samuel L. Smith
    http://arxiv.org/abs/1905.03776v1

    • [cs.LO]SMT-based Constraint Answer Set Solver EZSMT+
    Da Shen, Yuliya Lierler
    http://arxiv.org/abs/1905.03334v1

    • [cs.NE]A Reinforcement Learning Perspective on the Optimal Control of Mutation Probabilities for the (1+1) Evolutionary Algorithm: First Results on the OneMax Problem
    Luca Mossina, Emmanuel Rachelson, Daniel Delahaye
    http://arxiv.org/abs/1905.03726v1

    • [cs.NE]Automatic Design of Artificial Neural Networks for Gamma-Ray Detection
    Filipe Assunção, João Correia, Rúben Conceição, Mário Pimenta, Bernardo Tomé, Nuno Lourenço, Penousal Machado
    http://arxiv.org/abs/1905.03532v1

    • [cs.NE]Classificação de espécies de peixe utilizando redes neurais convolucional
    Andre G. C. Pacheco
    http://arxiv.org/abs/1905.03642v1

    • [cs.NE]Learning to Evolve
    Jan Schuchardt, Vladimir Golkov, Daniel Cremers
    http://arxiv.org/abs/1905.03389v1

    • [cs.NE]Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models
    Sungjae Cho, Jaeseo Lim, Chris Hickey, Jung Ae Park, Byoung-Tak Zhang
    http://arxiv.org/abs/1905.03617v1

    • [cs.NI]Path Design for Cellular-Connected UAV with Reinforcement Learning
    Yong Zeng, Xiaoli Xu
    http://arxiv.org/abs/1905.03440v1

    • [cs.NI]Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning
    Xinyu You, Xuanjie Li, Yuedong Xu, Hui Feng, Jin Zhao
    http://arxiv.org/abs/1905.03494v1

    • [cs.RO]An Omnidirectional Aerial Manipulation Platform for Contact-Based Inspection
    Karen Bodie, Maximilian Brunner, Michael Pantic, Stefan Walser, Patrick Pfändler, Ueli Angst, Roland Siegwart, Juan Nieto
    http://arxiv.org/abs/1905.03502v1

    • [cs.RO]Configuration-Space Flipper Planning for Rescue Robots
    Yijun Yuan, Letong Wang, Sören Schwertfeger
    http://arxiv.org/abs/1905.02984v2

    • [cs.RO]Feature-Based Transfer Learning for Robotic Push Manipulation
    Jochen Stüber, Marek Kopicki, Claudio Zito
    http://arxiv.org/abs/1905.03720v1

    • [cs.RO]Model predictive approach to integrated path planning and tracking for autonomous vehicles
    Chao Huang, Boyuan Li, Masako Kishida
    http://arxiv.org/abs/1905.03444v1

    • [cs.RO]Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies
    Shuo Feng, Yiheng Feng, Haowei Sun, Shao Bao, Aditi Misra, Yi Zhang, Henry X. Liu
    http://arxiv.org/abs/1905.03428v1

    • [cs.SD]Analysis of Deep Clustering as Preprocessing for Automatic Speech Recognition of Sparsely Overlapping Speech
    Tobias Menne, Ilya Sklyar, Ralf Schlüter, Hermann Ney
    http://arxiv.org/abs/1905.03500v1

    • [cs.SD]Universal Sound Separation
    Ilya Kavalerov, Scott Wisdom, Hakan Erdogan, Brian Patton, Kevin Wilson, Jonathan Le Roux, John R. Hershey
    http://arxiv.org/abs/1905.03330v1

    • [cs.SI]Embedding vertex intrinsic relevance in network analysis: the case of Betweenness
    Orazio Giustolisi, Luca Ridolfi, Antonietta Simone
    http://arxiv.org/abs/1905.03300v1

    • [cs.SI]Fairness across Network Positions in Cyberbullying Detection Algorithms
    Vivek Singh, Connor Hofenbitzer
    http://arxiv.org/abs/1905.03403v1

    • [cs.SY]Prioritized Inverse Kinematics: Nonsmoothness, Trajectory Existence, Task Convergence, Stability
    Sang-ik An, Dongheui Lee
    http://arxiv.org/abs/1905.03416v1

    • [cs.SY]Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology
    Shuo Feng, Yiheng Feng, Chunhui Yu, Yi Zhang, Henry X. Liu
    http://arxiv.org/abs/1905.03419v1

    • [eess.IV]QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with Increased Receptive Field
    Yicheng Chen, Angela Jakary, Christopher P. Hess, Janine M. Lupo
    http://arxiv.org/abs/1905.03356v1

    • [eess.SP]1D Convolutional Neural Networks and Applications: A Survey
    Serkan Kiranyaz, Onur Avci, Osama Abdeljaber, Turker Ince, Moncef Gabbouj, Daniel J. Inman
    http://arxiv.org/abs/1905.03554v1

    • [eess.SP]Collaborative Localization and Tracking with Minimal Infrastructure
    Yanjun Cao, David St-Onge, Andreas Zell, Giovanni Beltrame
    http://arxiv.org/abs/1905.03247v1

    • [math-ph]Bounds on Lyapunov exponents
    David Sutter, Omar Fawzi, Renato Renner
    http://arxiv.org/abs/1905.03270v1

    • [math.ST]Double-calibration estimators accounting for under-coverage and nonresponse in socio-economic surveys
    Maria Michela Dickson, Giuseppe Espa, Lorenzo Fattorini
    http://arxiv.org/abs/1905.03530v1

    • [math.ST]Non-Asymptotic Sequential Tests for Overlapping Hypotheses and application to near optimal arm identification in bandit models
    Aurélien Garivier, Emilie Kaufmann
    http://arxiv.org/abs/1905.03495v1

    • [math.ST]On Semi-parametric Bernstein-von Mises Theorems for BART
    Veronika Rockova
    http://arxiv.org/abs/1905.03735v1

    • [q-bio.QM]The Identification and Analysis of Indicators for Predicting Malarial Incidence in Zimbabwe
    Booma Sowkarthiga Balasubramani, Marco Nanni, Shin Imai, Isabel F. Cruz
    http://arxiv.org/abs/1905.03594v1

    • [stat.AP]Automatic multiscale approach for water networks partitioning into dynamic district metered areas
    Carlo Giudicianni, Manuel Herrera, Armando di Nardo, Kemi Adeyeye
    http://arxiv.org/abs/1905.03372v1

    • [stat.AP]Bias in the estimation of cumulative viremia in cohort studies of HIV-infected individuals
    Maia Lesosky, Tracy Glass, Brian Rambau, Nei-Yuan Hsiao, Elaine J Abrams, Landon Myer
    http://arxiv.org/abs/1905.03467v1

    • [stat.AP]Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson Regression
    Lorenz A. Gilch
    http://arxiv.org/abs/1905.03628v1

    • [stat.AP]The unfairness of the UEFA Euro 2020 qualifying
    László Csató
    http://arxiv.org/abs/1905.03325v1

    • [stat.CO]Stein Point Markov Chain Monte Carlo
    Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark Girolami, Lester Mackey, Chris. J. Oates
    http://arxiv.org/abs/1905.03673v1

    • [stat.ME]Approximate Bayesian computation with the Wasserstein distance
    Espen Bernton, Pierre E. Jacob, Mathieu Gerber, Christian P. Robert
    http://arxiv.org/abs/1905.03747v1

    • [stat.ME]Conformal prediction for exponential families and generalized linear models
    Daniel J. Eck, Forrest W. Crawford
    http://arxiv.org/abs/1905.03657v1

    • [stat.ML]A Bayesian Finite Mixture Model with Variable Selection for Data with Mixed-type Variables
    Shu Wang, Jonathan G. Yabes, Chung-Chou H. Chang
    http://arxiv.org/abs/1905.03680v1

    • [stat.ML]A Novel Adaptive Kernel for the RBF Neural Networks
    Shujaat Khan, Imran Naseem, Roberto Togneri, Mohammed Bennamoun
    http://arxiv.org/abs/1905.03546v1

    • [stat.ML]Best-scored Random Forest Density Estimation
    Hanyuan Hang, Hongwei Wen
    http://arxiv.org/abs/1905.03729v1

    • [stat.ML]Importance Weighted Hierarchical Variational Inference
    Artem Sobolev, Dmitry Vetrov
    http://arxiv.org/abs/1905.03290v1

    • [stat.ML]Two-stage Best-scored Random Forest for Large-scale Regression
    Hanyuan Hang, Yingyi Chen, Johan A. K. Suykens
    http://arxiv.org/abs/1905.03438v1