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
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• [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