cs.AI - 人工智能
cs.CE - 计算工程、 金融和科学
cs.CL - 计算与语言
cs.CR - 加密与安全
cs.CV - 机器视觉与模式识别
cs.DC - 分布式、并行与集群计算
cs.DS - 数据结构与算法
cs.ET - 新兴技术
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.NA - 数值分析
cs.NE - 神经与进化计算
cs.SD - 声音处理
cs.SE - 软件工程
cs.SI - 社交网络与信息网络
cs.SY - 系统与控制
eess.SP - 信号处理
math.OC - 优化与控制
math.ST - 统计理论
physics.soc-ph - 物理学与社会
stat.AP - 应用统计
stat.ME - 统计方法论
stat.ML - (统计)机器学习
• [cs.AI]Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty
• [cs.AI]Forecasting Granular Audience Size for Online Advertising
• [cs.AI]Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss
• [cs.CE]Trajectory Design of Multiple Near Earth Asteroids Exploration Using Solar Sail Based on Deep Neural Network
• [cs.CL]DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension
• [cs.CL]Multi-Perspective Fusion Network for Commonsense Reading Comprehension
• [cs.CL]Multi-style Generative Reading Comprehension
• [cs.CL]Multi-turn Inference Matching Network for Natural Language Inference
• [cs.CL]Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents
• [cs.CR]Contamination Attacks and Mitigation in Multi-Party Machine Learning
• [cs.CR]Using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions
• [cs.CV]3D Object Detection Using Scale Invariant and Feature Reweighting Networks
• [cs.CV]All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks
• [cs.CV]Blind Motion Deblurring with Cycle Generative Adversarial Networks
• [cs.CV]Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
• [cs.CV]Deeper and Wider Siamese Networks for Real-Time Visual Tracking
• [cs.CV]Dynamics are Important for the Recognition of Equine Pain in Video
• [cs.CV]Ensembles of feedforward-designed convolutional neural networks
• [cs.CV]Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
• [cs.CV]FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
• [cs.CV]Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
• [cs.CV]GILT: Generating Images from Long Text
• [cs.CV]Interpretable BoW Networks for Adversarial Example Detection
• [cs.CV]Learning Independent Object Motion from Unlabelled Stereoscopic Videos
• [cs.CV]Morphological Networks for Image De-raining
• [cs.CV]Panoptic Feature Pyramid Networks
• [cs.CV]Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
• [cs.CV]Richer and Deeper Supervision Network for Salient Object Detection
• [cs.CV]Robust and High Performance Face Detector
• [cs.CV]Self-Supervised Learning from Web Data for Multimodal Retrieval
• [cs.CV]Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
• [cs.CV]Spherical CNNs on Unstructured Grids
• [cs.CV]Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
• [cs.CV]Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
• [cs.CV]Truncated nuclear norm regularization for low-rank tensor completion
• [cs.CV]Unpaired Pose Guided Human Image Generation
• [cs.CV]Unseen Object Segmentation in Videos via Transferable Representations
• [cs.DC]Age-of-Information for Computation-Intensive Messages in Mobile Edge Computing
• [cs.DC]CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
• [cs.DC]HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
• [cs.DC]Inversion-based Measurement of Data Consistency for Read/Write Registers
• [cs.DC]Lower bounds for maximal matchings and maximal independent sets
• [cs.DC]Superlight - A Permissionless, Light-client Only Blockchain with Self-Contained Proofs and BLS Signatures
• [cs.DS]Fair Algorithms for Clustering
• [cs.ET]SNRA: A Spintronic Neuromorphic Reconfigurable Array for In-Circuit Training and Evaluation of Deep Belief Networks
• [cs.IR]Using offline metrics and user behavior analysis to combine multiple systems for music recommendation
• [cs.IT]Age Optimal Information Gathering and Dissemination on Graphs
• [cs.IT]Covert Secret Key Generation with an Active Warden
• [cs.IT]Improved encoding and decoding for non-adaptive threshold group testing
• [cs.IT]Locally Repairable Convolutional Codes with Sliding Window Repair
• [cs.IT]Optimal Age over Erasure Channels
• [cs.IT]Optimal Multi-Quality Multicast for 360 Virtual Reality Video
• [cs.IT]Rate matching for polar codes based on binary domination
• [cs.IT]Service Rate Region of Content Access from Erasure Coded Storage
• [cs.IT]The Effect of Introducing Redundancy in a Probabilistic Forwarding Protocol
• [cs.LG]A New Perspective on Machine Learning: How to do Perfect Supervised Learning
• [cs.LG]Accelerating Goal-Directed Reinforcement Learning by Model Characterization
• [cs.LG]Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
• [cs.LG]Analogy-Based Preference Learning with Kernels
• [cs.LG]Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry
• [cs.LG]Audio Captcha Recognition Using RastaPLP Features by SVM
• [cs.LG]Comparing Sample-wise Learnability Across Deep Neural Network Models
• [cs.LG]Cost Sensitive Learning in the Presence of Symmetric Label Noise
• [cs.LG]Data Masking with Privacy Guarantees
• [cs.LG]Deep Neural Network Approximation Theory
• [cs.LG]Efficient Convolutional Neural Network Training with Direct Feedback Alignment
• [cs.LG]FIGR: Few-shot Image Generation with Reptile
• [cs.LG]FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
• [cs.LG]Fusion Strategies for Learning User Embeddings with Neural Networks
• [cs.LG]Geometrization of deep networks for the interpretability of deep learning systems
• [cs.LG]Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
• [cs.LG]Interpretable CNNs
• [cs.LG]Learning the optimal state-feedback via supervised imitation learning
• [cs.LG]Learning with Collaborative Neural Network Group by Reflection
• [cs.LG]Multi-Source Transfer Learning for Non-Stationary Environments
• [cs.LG]On the Dimensionality of Embeddings for Sparse Features and Data
• [cs.LG]On the effect of the activation function on the distribution of hidden nodes in a deep network
• [cs.LG]Optimal Differentially Private ADMM for Distributed Machine Learning
• [cs.LG]Recurrent Control Nets for Deep Reinforcement Learning
• [cs.LG]Risk-Aware Active Inverse Reinforcement Learning
• [cs.LG]Semi-parametric dynamic contextual pricing
• [cs.LG]Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
• [cs.LG]Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
• [cs.LG]Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
• [cs.LG]Visualising Basins of Attraction for the Cross-Entropy and the Squared Error Neural Network Loss Functions
• [cs.NA]Genetic Algorithm based Multi-Objective Optimization of Solidification in Die Casting using Deep Neural Network as Surrogate Model
• [cs.NE]Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
• [cs.NE]Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I)
• [cs.SD]Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation
• [cs.SE]Specification Patterns for Robotic Missions
• [cs.SI]Influence Minimization Under Budget and Matroid Constraints: Extended Version
• [cs.SI]K-Core Minimization: A Game Theoretic Approach
• [cs.SI]On neighbourhood degree sequences of complex networks
• [cs.SY]Analytically Exact Distributed Voltage Stability Index based on Power Flow Circles
• [eess.SP]Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Node Error
• [eess.SP]Compensating for Interference in Sliding Window Detection Processes using a Bayesian Paradigm
• [eess.SP]Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach
• [math.OC]Large-Scale Markov Decision Problems via the Linear Programming Dual
• [math.OC]Sum-of-square-of-rational-function based representations of positive semidefinite polynomial matrices
• [math.ST]A Scale-invariant Generalization of Renyi Entropy and Related Optimizations under Tsallis' Nonextensive Framework
• [math.ST]Monotone Least Squares and Isotonic Quantiles
• [math.ST]On Laplacian spectrum of dendrite trees
• [math.ST]On Tail Dependence Matrices - The Realization Problem for Parametric Families
• [math.ST]The semi-algebraic geometry of optimal designs for the Bradley-Terry model
• [physics.soc-ph]An alternative small-world network model approaching the Erdős-Rényi random graph
• [physics.soc-ph]Building connections: How scientists meet each other during a conference
• [physics.soc-ph]Coevolution spreading in complex networks
• [physics.soc-ph]Interplay of intra- and inter-dependence affects the robustness of network of networks
• [physics.soc-ph]Spectra of random networks with arbitrary degrees
• [stat.AP]Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems
• [stat.AP]Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation
• [stat.AP]Uncovering predictability in the evolution of the WTI oil futures curve
• [stat.ME]Bayes-raking: Bayesian Finite Population Inference with Known Margins
• [stat.ME]Bayesian Inference for Persistent Homology
• [stat.ME]Dynamic Tail Inference with Log-Laplace Volatility
• [stat.ME]What is the dimension of a stochastic process? Testing for the rank of a covariance operator
• [stat.ML]Comments on "Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?"
• [stat.ML]DPPNet: Approximating Determinantal Point Processes with Deep Networks
• [stat.ML]Graphical model inference: Sequential Monte Carlo meets deterministic approximations
• [stat.ML]Learning with Fenchel-Young Losses
• [stat.ML]Tree Tensor Networks for Generative Modeling
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• [cs.AI]Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty
Nikhil Bhargava, Brian Williams
http://arxiv.org/abs/1901.02307v1
• [cs.AI]Forecasting Granular Audience Size for Online Advertising
Ritwik Sinha, Dhruv Singal, Pranav Maneriker, Kushal Chawla, Yash Shrivastava, Deepak Pai, Atanu R Sinha
http://arxiv.org/abs/1901.02412v1
• [cs.AI]Towards a Decentralized, Autonomous Multiagent Framework for Mitigating Crop Loss
Roi Ceren, Shannon Quinn, Glen Raines
http://arxiv.org/abs/1901.02035v1
• [cs.CE]Trajectory Design of Multiple Near Earth Asteroids Exploration Using Solar Sail Based on Deep Neural Network
Yu Song, Shengping Gong
http://arxiv.org/abs/1901.02172v1
• [cs.CL]DEMN: Distilled-Exposition Enhanced Matching Network for Story Comprehension
Chunhua Liu, Haiou Zhang, Shan Jiang, Dong Yu
http://arxiv.org/abs/1901.02252v1
• [cs.CL]Multi-Perspective Fusion Network for Commonsense Reading Comprehension
Chunhua Liu, Yan Zhao, Qingyi Si, Haiou Zhang, Bohan Li, Dong Yu
http://arxiv.org/abs/1901.02257v1
• [cs.CL]Multi-style Generative Reading Comprehension
Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
http://arxiv.org/abs/1901.02262v1
• [cs.CL]Multi-turn Inference Matching Network for Natural Language Inference
Chunhua Liu, Shan Jiang, Hainan Yu, Dong Yu
http://arxiv.org/abs/1901.02222v1
• [cs.CL]Team EP at TAC 2018: Automating data extraction in systematic reviews of environmental agents
Artur Nowak, Paweł Kunstman
http://arxiv.org/abs/1901.02081v1
• [cs.CR]Contamination Attacks and Mitigation in Multi-Party Machine Learning
Jamie Hayes, Olga Ohrimenko
http://arxiv.org/abs/1901.02402v1
• [cs.CR]Using fuzzy bits and neural networks to partially invert few rounds of some cryptographic hash functions
Sergij V. Goncharov
http://arxiv.org/abs/1901.02438v1
• [cs.CV]3D Object Detection Using Scale Invariant and Feature Reweighting Networks
Xin Zhao, Zhe Liu, Ruolan Hu, Kaiqi Huang
http://arxiv.org/abs/1901.02237v1
• [cs.CV]All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks
Stephen Phillips, Kostas Daniilidis
http://arxiv.org/abs/1901.02078v1
• [cs.CV]Blind Motion Deblurring with Cycle Generative Adversarial Networks
Quan Yuan, Junxia Li, Lingwei Zhang, Zhefu Wu, Guangyu Liu
http://arxiv.org/abs/1901.01641v2
• [cs.CV]Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Nießner
http://arxiv.org/abs/1901.02070v1
• [cs.CV]Deeper and Wider Siamese Networks for Real-Time Visual Tracking
Zhipeng Zhang, Houwen Peng, Qiang Wang
http://arxiv.org/abs/1901.01660v2
• [cs.CV]Dynamics are Important for the Recognition of Equine Pain in Video
Sofia Broomé, Karina Bech Gleerup, Pia Haubro Andersen, Hedvig Kjellström
http://arxiv.org/abs/1901.02106v1
• [cs.CV]Ensembles of feedforward-designed convolutional neural networks
Yueru Chen, Yijing Yang, Wei Wang, C. -C. Jay Kuo
http://arxiv.org/abs/1901.02154v1
• [cs.CV]Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang
http://arxiv.org/abs/1901.02184v1
• [cs.CV]FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals
Umur Aybars Ciftci, Ilke Demir
http://arxiv.org/abs/1901.02212v1
• [cs.CV]Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets
Irtiza Hasan, Francesco Setti, Theodore Tsesmelis, Vasileios Belagiannis, Sikandar Amin, Alessio Del Bue, Marco Cristani, Fabio Galasso
http://arxiv.org/abs/1901.02000v1
• [cs.CV]GILT: Generating Images from Long Text
Ori Bar El, Ori Licht, Netanel Yosephian
http://arxiv.org/abs/1901.02404v1
• [cs.CV]Interpretable BoW Networks for Adversarial Example Detection
Krishna Kanth Nakka, Mathieu Salzmann
http://arxiv.org/abs/1901.02229v1
• [cs.CV]Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Zhe Cao, Abhishek Kar, Christian Haene, Jitendra Malik
http://arxiv.org/abs/1901.01971v2
• [cs.CV]Morphological Networks for Image De-raining
Ranjan Mondal, Pulak Purkait, Sanchayan Santra, Bhabatosh Chanda
http://arxiv.org/abs/1901.02411v1
• [cs.CV]Panoptic Feature Pyramid Networks
Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár
http://arxiv.org/abs/1901.02446v1
• [cs.CV]Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols
Yunxi Xiong, Yuankai Huo, Jiachen Wang, L. Taylor Davis, Maureen McHugo, Bennett A. Landman
http://arxiv.org/abs/1901.02040v1
• [cs.CV]Richer and Deeper Supervision Network for Salient Object Detection
Sen Jia, Neil D. B. Bruce
http://arxiv.org/abs/1901.02425v1
• [cs.CV]Robust and High Performance Face Detector
Yundong Zhang, Xiang Xu, Xiaotao Liu
http://arxiv.org/abs/1901.02350v1
• [cs.CV]Self-Supervised Learning from Web Data for Multimodal Retrieval
Raul Gomez, Lluis Gomez, Jaume Gibert, Dimosthenis Karatzas
http://arxiv.org/abs/1901.02004v1
• [cs.CV]Spatial-Winograd Pruning Enabling Sparse Winograd Convolution
Jiecao Yu, Jongsoo Park, Maxim Naumov
http://arxiv.org/abs/1901.02132v1
• [cs.CV]Spherical CNNs on Unstructured Grids
Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner
http://arxiv.org/abs/1901.02039v1
• [cs.CV]Stable Electromyographic Sequence Prediction During Movement Transitions using Temporal Convolutional Networks
Joseph L. Betthauser, John T. Krall, Rahul R. Kaliki, Matthew S. Fifer, Nitish V. Thakor
http://arxiv.org/abs/1901.02442v1
• [cs.CV]Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
Baoyuan Wu, Weidong Chen, Yanbo Fan, Yong Zhang, Jinlong Hou, Jie Liu, Junzhou Huang, Wei Liu, Tong Zhang
http://arxiv.org/abs/1901.01703v2
• [cs.CV]Truncated nuclear norm regularization for low-rank tensor completion
Shengke Xue, Wenyuan Qiu, Fan Liu, Xinyu Jin
http://arxiv.org/abs/1901.01997v1
• [cs.CV]Unpaired Pose Guided Human Image Generation
Xu Chen, Jie Song, Otmar Hilliges
http://arxiv.org/abs/1901.02284v1
• [cs.CV]Unseen Object Segmentation in Videos via Transferable Representations
Yi-Wen Chen, Yi-Hsuan Tsai, Chu-Ya Yang, Yen-Yu Lin, Ming-Hsuan Yang
http://arxiv.org/abs/1901.02444v1
• [cs.DC]Age-of-Information for Computation-Intensive Messages in Mobile Edge Computing
Qiaobin Kuang, Jie Gong, Xiang Chen, Xiao Ma
http://arxiv.org/abs/1901.01854v2
• [cs.DC]CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich, Luo Mai, Paolo Costa, Peter Pietzuch
http://arxiv.org/abs/1901.02244v1
• [cs.DC]HyPar: Towards Hybrid Parallelism for Deep Learning Accelerator Array
Linghao Song, Jiachen Mao, Youwei Zhuo, Xuehai Qian, Hai Li, Yiran Chen
http://arxiv.org/abs/1901.02067v1
• [cs.DC]Inversion-based Measurement of Data Consistency for Read/Write Registers
Yu Huang, Hengfeng Wei, Maosen Huang, Lingzhi Ouyang
http://arxiv.org/abs/1901.02192v1
• [cs.DC]Lower bounds for maximal matchings and maximal independent sets
Alkida Balliu, Sebastian Brandt, Juho Hirvonen, Dennis Olivetti, Mikaël Rabie, Jukka Suomela
http://arxiv.org/abs/1901.02441v1
• [cs.DC]Superlight - A Permissionless, Light-client Only Blockchain with Self-Contained Proofs and BLS Signatures
Roman Blum, Thomas Bocek
http://arxiv.org/abs/1901.02213v1
• [cs.DS]Fair Algorithms for Clustering
Suman K. Bera, Deeparnab Chakrabarty, Maryam Negahbani
http://arxiv.org/abs/1901.02393v1
• [cs.ET]SNRA: A Spintronic Neuromorphic Reconfigurable Array for In-Circuit Training and Evaluation of Deep Belief Networks
Ramtin Zand, Ronald F. DeMara
http://arxiv.org/abs/1901.02415v1
• [cs.IR]Using offline metrics and user behavior analysis to combine multiple systems for music recommendation
Andres Ferraro, Dmitry Bogdanov, Kyumin Choi, Xavier Serra
http://arxiv.org/abs/1901.02296v1
• [cs.IT]Age Optimal Information Gathering and Dissemination on Graphs
Vishrant Tripathi, Rajat Talak, Eytan Modiano
http://arxiv.org/abs/1901.02178v1
• [cs.IT]Covert Secret Key Generation with an Active Warden
Mehrdad Tahmasbi, Matthieu Bloch
http://arxiv.org/abs/1901.02044v1
• [cs.IT]Improved encoding and decoding for non-adaptive threshold group testing
Thach V. Bui, Minoru Kuribayashi, Mahdi Cheraghchi, Isao Echizen
http://arxiv.org/abs/1901.02283v1
• [cs.IT]Locally Repairable Convolutional Codes with Sliding Window Repair
Umberto Martínez-Peñas, Diego Napp
http://arxiv.org/abs/1901.02073v1
• [cs.IT]Optimal Age over Erasure Channels
Elie Najm, Emre Telatar, Rajai Nasser
http://arxiv.org/abs/1901.01573v2
• [cs.IT]Optimal Multi-Quality Multicast for 360 Virtual Reality Video
Kaixuan Long, Chencheng Ye, Ying Cui, Zhi Liu
http://arxiv.org/abs/1901.02203v1
• [cs.IT]Rate matching for polar codes based on binary domination
Min Jang, Seok-Ki Ahn, Hongsil Jeong, Kyung-Joong Kim, Seho Myung, Sang-Hyo Kim, Kyeongcheol Yang
http://arxiv.org/abs/1901.02287v1
• [cs.IT]Service Rate Region of Content Access from Erasure Coded Storage
Sarah Anderson, Ann Johnston, Gauri Joshi, Gretchen Matthews, Carolyn Mayer, Emina Soljanin
http://arxiv.org/abs/1901.02399v1
• [cs.IT]The Effect of Introducing Redundancy in a Probabilistic Forwarding Protocol
Vinay Kumar B. R., Roshan Anthony, Navin Kashyap
http://arxiv.org/abs/1901.02033v1
• [cs.LG]A New Perspective on Machine Learning: How to do Perfect Supervised Learning
Hui Jiang
http://arxiv.org/abs/1901.02046v1
• [cs.LG]Accelerating Goal-Directed Reinforcement Learning by Model Characterization
Shoubhik Debnath, Gaurav Sukhatme, Lantao Liu
http://arxiv.org/abs/1901.01977v1
• [cs.LG]Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
Randy Ardywibowo, Guang Zhao, Zhangyang Wang, Bobak Mortazavi, Shuai Huang, Xiaoning Qian
http://arxiv.org/abs/1901.02427v1
• [cs.LG]Analogy-Based Preference Learning with Kernels
Mohsen Ahmadi Fahandar, Eyke Hüllermeier
http://arxiv.org/abs/1901.02001v1
• [cs.LG]Artificial Intelligence and Machine Learning to Predict and Improve Efficiency in Manufacturing Industry
Ibtissam El Hassani, Choumicha El Mazgualdi, Tawfik Masrour
http://arxiv.org/abs/1901.02256v1
• [cs.LG]Audio Captcha Recognition Using RastaPLP Features by SVM
Ahmet Faruk Cakmak, Muhammet Balcilar
http://arxiv.org/abs/1901.02153v1
• [cs.LG]Comparing Sample-wise Learnability Across Deep Neural Network Models
Seung-Geon Lee, Jaedeok Kim, Hyun-Joo Jung, Yoonsuck Choe
http://arxiv.org/abs/1901.02347v1
• [cs.LG]Cost Sensitive Learning in the Presence of Symmetric Label Noise
Sandhya Tripathi, N. Hemachandra
http://arxiv.org/abs/1901.02271v1
• [cs.LG]Data Masking with Privacy Guarantees
Anh T. Pham, Shalini Ghosh, Vinod Yegneswaran
http://arxiv.org/abs/1901.02185v1
• [cs.LG]Deep Neural Network Approximation Theory
Philipp Grohs, Dmytro Perekrestenko, Dennis Elbrächter, Helmut Bölcskei
http://arxiv.org/abs/1901.02220v1
• [cs.LG]Efficient Convolutional Neural Network Training with Direct Feedback Alignment
Donghyeon Han, Hoi-jun Yoo
http://arxiv.org/abs/1901.01986v1
• [cs.LG]FIGR: Few-shot Image Generation with Reptile
Louis Clouâtre, Marc Demers
http://arxiv.org/abs/1901.02199v1
• [cs.LG]FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
Aditya Kusupati, Manish Singh, Kush Bhatia, Ashish Kumar, Prateek Jain, Manik Varma
http://arxiv.org/abs/1901.02358v1
• [cs.LG]Fusion Strategies for Learning User Embeddings with Neural Networks
Philipp Blandfort, Tushar Karayil, Federico Raue, Jörn Hees, Andreas Dengel
http://arxiv.org/abs/1901.02322v1
• [cs.LG]Geometrization of deep networks for the interpretability of deep learning systems
Xiao Dong, Ling Zhou
http://arxiv.org/abs/1901.02354v1
• [cs.LG]Guidelines and Benchmarks for Deployment of Deep Learning Models on Smartphones as Real-Time Apps
Abhishek Sehgal, Nasser Kehtarnavaz
http://arxiv.org/abs/1901.02144v1
• [cs.LG]Interpretable CNNs
Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu
http://arxiv.org/abs/1901.02413v1
• [cs.LG]Learning the optimal state-feedback via supervised imitation learning
Dharmesh Tailor, Dario Izzo
http://arxiv.org/abs/1901.02369v1
• [cs.LG]Learning with Collaborative Neural Network Group by Reflection
Zehua Cheng, Liyao Gao
http://arxiv.org/abs/1901.02433v1
• [cs.LG]Multi-Source Transfer Learning for Non-Stationary Environments
Honghui Du, Leandro L. Minku, Huiyu Zhou
http://arxiv.org/abs/1901.02052v1
• [cs.LG]On the Dimensionality of Embeddings for Sparse Features and Data
Maxim Naumov
http://arxiv.org/abs/1901.02103v1
• [cs.LG]On the effect of the activation function on the distribution of hidden nodes in a deep network
Philip M. Long, Hanie Sedghi
http://arxiv.org/abs/1901.02104v1
• [cs.LG]Optimal Differentially Private ADMM for Distributed Machine Learning
Jiahao Ding, Yanmin Gong, Miao Pan, Zhu Han
http://arxiv.org/abs/1901.02094v1
• [cs.LG]Recurrent Control Nets for Deep Reinforcement Learning
Vincent Liu, Ademi Adeniji, Nathaniel Lee, Jason Zhao, Mario Srouji
http://arxiv.org/abs/1901.01994v1
• [cs.LG]Risk-Aware Active Inverse Reinforcement Learning
Daniel S. Brown, Yuchen Cui, Scott Niekum
http://arxiv.org/abs/1901.02161v1
• [cs.LG]Semi-parametric dynamic contextual pricing
Virag Shah, Jose Blanchet, Ramesh Johari
http://arxiv.org/abs/1901.02045v1
• [cs.LG]Soft-Bayes: Prod for Mixtures of Experts with Log-Loss
Laurent Orseau, Tor Lattimore, Shane Legg
http://arxiv.org/abs/1901.02230v1
• [cs.LG]Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
Severine Affeldt, Lazhar Labiod, Mohamed Nadif
http://arxiv.org/abs/1901.02291v1
• [cs.LG]Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan, Lenz Belzner, Claudia Linnhoff-Popien
http://arxiv.org/abs/1901.02219v1
• [cs.LG]Visualising Basins of Attraction for the Cross-Entropy and the Squared Error Neural Network Loss Functions
Anna Sergeevna Bosman, Andries Engelbrecht, Mardé Helbig
http://arxiv.org/abs/1901.02302v1
• [cs.NA]Genetic Algorithm based Multi-Objective Optimization of Solidification in Die Casting using Deep Neural Network as Surrogate Model
Shantanu Shahane, Narayana Aluru, Placid Ferreira, Shiv G Kapoor, Surya Pratap Vanka
http://arxiv.org/abs/1901.02364v1
• [cs.NE]Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Ruijun Xu, Hailong Ma
http://arxiv.org/abs/1901.01074v2
• [cs.NE]Towards Self-constructive Artificial Intelligence: Algorithmic basis (Part I)
Fernando J. Corbacho
http://arxiv.org/abs/1901.01989v1
• [cs.SD]Sinusoidal wave generating network based on adversarial learning and its application: synthesizing frog sounds for data augmentation
Sangwook Park, David K. Han, Hanseok Ko
http://arxiv.org/abs/1901.02050v1
• [cs.SE]Specification Patterns for Robotic Missions
Claudio Menghi, Christos Tsigkanos, Patrizio Pelliccione, Carlo Ghezzi, Thorsten Berger
http://arxiv.org/abs/1901.02077v1
• [cs.SI]Influence Minimization Under Budget and Matroid Constraints: Extended Version
Sourav Medya, Arlei Silva, Ambuj Singh
http://arxiv.org/abs/1901.02156v1
• [cs.SI]K-Core Minimization: A Game Theoretic Approach
Sourav Medya, Tiyani Ma, Arlei Silva, Ambuj Singh
http://arxiv.org/abs/1901.02166v1
• [cs.SI]On neighbourhood degree sequences of complex networks
Keith M. Smith
http://arxiv.org/abs/1901.02353v1
• [cs.SY]Analytically Exact Distributed Voltage Stability Index based on Power Flow Circles
Kishan Prudhvi Guddanti, Amarsagar Reddy Ramapuram Matavalam, Yang Weng
http://arxiv.org/abs/1901.02303v1
• [eess.SP]Analysis of Distributed ADMM Algorithm for Consensus Optimization in Presence of Node Error
Layla Majzoobi, Farshad Lahouti, Vahid Shah-Mansouri
http://arxiv.org/abs/1901.02436v1
• [eess.SP]Compensating for Interference in Sliding Window Detection Processes using a Bayesian Paradigm
Graham V. Weinberg
http://arxiv.org/abs/1901.01296v1
• [eess.SP]Compressive-Sensing Data Reconstruction for Structural Health Monitoring: A Machine-Learning Approach
Yuequan Bao, Zhiyi Tang, Hui Li
http://arxiv.org/abs/1901.01995v1
• [math.OC]Large-Scale Markov Decision Problems via the Linear Programming Dual
Yasin Abbasi-Yadkori, Peter L. Bartlett, Xi Chen, Alan Malek
http://arxiv.org/abs/1901.01992v1
• [math.OC]Sum-of-square-of-rational-function based representations of positive semidefinite polynomial matrices
Thanh-Hieu Le, Nhat-Thien Pham
http://arxiv.org/abs/1901.02360v1
• [math.ST]A Scale-invariant Generalization of Renyi Entropy and Related Optimizations under Tsallis' Nonextensive Framework
Abhik Ghosh, Ayanendranath Basu
http://arxiv.org/abs/1901.01981v1
• [math.ST]Monotone Least Squares and Isotonic Quantiles
Alexandre Moesching, Lutz Duembgen
http://arxiv.org/abs/1901.02398v1
• [math.ST]On Laplacian spectrum of dendrite trees
Yuyang Xu, Jianfeng Yao
http://arxiv.org/abs/1901.02201v1
• [math.ST]On Tail Dependence Matrices - The Realization Problem for Parametric Families
Nariankadu D. Shyamalkumar, Siyang Tao
http://arxiv.org/abs/1901.02157v1
• [math.ST]The semi-algebraic geometry of optimal designs for the Bradley-Terry model
Thomas Kahle, Frank Röttger, Rainer Schwabe
http://arxiv.org/abs/1901.02375v1
• [physics.soc-ph]An alternative small-world network model approaching the Erdős-Rényi random graph
Benjamin F. Maier
http://arxiv.org/abs/1901.02381v1
• [physics.soc-ph]Building connections: How scientists meet each other during a conference
Mathieu Génois, Maria Zens, Clemens Lechner, Beatrice Rammstedt, Markus Strohmaier
http://arxiv.org/abs/1901.01182v2
• [physics.soc-ph]Coevolution spreading in complex networks
Wei Wang, Quan-Hui Liu, Junhao Liang, Yanqing Hu, Tao Zhou
http://arxiv.org/abs/1901.02125v1
• [physics.soc-ph]Interplay of intra- and inter-dependence affects the robustness of network of networks
Aradhana Singh, Sitabhra Sinha
http://arxiv.org/abs/1901.02329v1
• [physics.soc-ph]Spectra of random networks with arbitrary degrees
M. E. J. Newman, Xiao Zhang, Raj Rao Nadakuditi
http://arxiv.org/abs/1901.02029v1
• [stat.AP]Combining Unsupervised and Supervised Learning for Asset Class Failure Prediction in Power Systems
Ming Dong, L. S. Grumbach
http://arxiv.org/abs/1901.01985v1
• [stat.AP]Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation
Fan Li, Fan Li
http://arxiv.org/abs/1901.02152v1
• [stat.AP]Uncovering predictability in the evolution of the WTI oil futures curve
Fearghal Kearney, Han Lin Shang
http://arxiv.org/abs/1901.02248v1
• [stat.ME]Bayes-raking: Bayesian Finite Population Inference with Known Margins
Yajuan Si, Peigen Zhou
http://arxiv.org/abs/1901.02117v1
• [stat.ME]Bayesian Inference for Persistent Homology
Vasileios Maroulas, Farzana Nasrin, Christopher Oballe
http://arxiv.org/abs/1901.02034v1
• [stat.ME]Dynamic Tail Inference with Log-Laplace Volatility
Gordon V. Chavez
http://arxiv.org/abs/1901.02419v1
• [stat.ME]What is the dimension of a stochastic process? Testing for the rank of a covariance operator
Anirvan Chakraborty, Victor M. Panaretos
http://arxiv.org/abs/1901.02333v1
• [stat.ML]Comments on "Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?"
Talha Cihad Gulcu, Alper Gungor
http://arxiv.org/abs/1901.02182v1
• [stat.ML]DPPNet: Approximating Determinantal Point Processes with Deep Networks
Zelda Mariet, Yaniv Ovadia, Jasper Snoek
http://arxiv.org/abs/1901.02051v1
• [stat.ML]Graphical model inference: Sequential Monte Carlo meets deterministic approximations
Fredrik Lindsten, Jouni Helske, Matti Vihola
http://arxiv.org/abs/1901.02374v1
• [stat.ML]Learning with Fenchel-Young Losses
Mathieu Blondel, André F. T. Martins, Vlad Niculae
http://arxiv.org/abs/1901.02324v1
• [stat.ML]Tree Tensor Networks for Generative Modeling
Song Cheng, Lei Wang, Tao Xiang, Pan Zhang
http://arxiv.org/abs/1901.02217v1
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