astro-ph.IM - 仪器仪表和天体物理学方法
cs.AI - 人工智能
cs.CL - 计算与语言
cs.CV - 机器视觉与模式识别
cs.CY - 计算与社会
cs.DB - 数据库
cs.DC - 分布式、并行与集群计算
cs.IR - 信息检索
cs.IT - 信息论
cs.LG - 自动学习
cs.NE - 神经与进化计算
cs.RO - 机器人学
cs.SI - 社交网络与信息网络
math.FA - 泛函演算
math.ST - 统计理论
physics.soc-ph - 物理学与社会
q-fin.CP -计算金融学
quant-ph - 量子物理
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
• [astro-ph.IM]Laser Communication and Coordination Control of Spacecraft Swarms
• [cs.AI]Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
• [cs.AI]Evaluation Function Approximation for Scrabble
• [cs.AI]Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning
• [cs.CL]A BERT Baseline for the Natural Questions
• [cs.CL]Automatic Parallel Corpus Creation for Hindi-English News Translation Task
• [cs.CL]BioBERT: pre-trained biomedical language representation model for biomedical text mining
• [cs.CL]Emergent Linguistic Phenomena in Multi-Agent Communication Games
• [cs.CL]Misleading Metadata Detection on YouTube
• [cs.CV]Deep Multimodality Model for Multi-task Multi-view Learning
• [cs.CV]Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
• [cs.CV]Face morphing detection in the presence of printing/scanning and heterogeneous image sources
• [cs.CV]FaceForensics++: Learning to Detect Manipulated Facial Images
• [cs.CV]Improving Image Captioning by Leveraging Knowledge Graphs
• [cs.CV]Joint shape learning and segmentation for medical images using a minimalistic deep network
• [cs.CV]Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views
• [cs.CV]One-Class Convolutional Neural Network
• [cs.CV]Revisiting Self-Supervised Visual Representation Learning
• [cs.CV]Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
• [cs.CV]Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
• [cs.CV]Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components
• [cs.CY]Optimal Design of SWIPT-Aware Fog Computing Networks
• [cs.DB]HRDBMS: Combining the Best of Modern and Traditional Relational Databases
• [cs.DC]Ambitious Data Science Can Be Painless
• [cs.DC]Partitioned Paxos via the Network Data Plane
• [cs.IR]Topological and Semantic Graph-based Author Disambiguation on DBLP Data in Neo4j
• [cs.IT]Continuous Analog Channel Estimation Aided Beamforming for Massive MIMO Systems
• [cs.IT]Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
• [cs.IT]New Lower Bounds for Permutation Codes using Linear Block Codes
• [cs.IT]Robust Transceiver Design for MIMO Decode-and-Forward Full-Duplex Relay
• [cs.LG]Ask less - Scale Market Research without Annoying Your Customers
• [cs.LG]Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification
• [cs.LG]Bayesian surrogate learning in dynamic simulator-based regression problems
• [cs.LG]Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
• [cs.LG]Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
• [cs.LG]Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
• [cs.LG]Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
• [cs.LG]Diffusion Variational Autoencoders
• [cs.LG]Diversity-Sensitive Conditional Generative Adversarial Networks
• [cs.LG]Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
• [cs.LG]Improving Adversarial Robustness via Promoting Ensemble Diversity
• [cs.LG]Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization
• [cs.LG]On the Limitations of Representing Functions on Sets
• [cs.LG]Provably efficient RL with Rich Observations via Latent State Decoding
• [cs.LG]SecureBoost: A Lossless Federated Learning Framework
• [cs.LG]Self-Supervised Generalisation with Meta Auxiliary Learning
• [cs.LG]State-Regularized Recurrent Neural Networks
• [cs.LG]Subspace Robust Wasserstein distances
• [cs.LG]Towards a Deeper Understanding of Adversarial Losses
• [cs.LG]Unsupervised speech representation learning using WaveNet autoencoders
• [cs.LG]When Can Neural Networks Learn Connected Decision Regions?
• [cs.NE]A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction
• [cs.NE]A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data
• [cs.NE]Ablation Studies in Artificial Neural Networks
• [cs.RO]Effective Locomotion at Multiple Stride Frequencies Using Proprioceptive Feedback on a Legged Microrobot
• [cs.RO]Learning agile and dynamic motor skills for legged robots
• [cs.SI]Computational landscape of user behavior on social media
• [math.FA]Average sampling and average splines on combinatorial graphs
• [math.ST]Concentration of quadratic forms under a Bernstein moment assumption
• [math.ST]Erratum: Higher Order Elicitability and Osband's Principle
• [math.ST]Gaussian One-Armed Bandit and Optimization of Batch Data Processing
• [math.ST]Gibbs posterior convergence and the thermodynamic formalism
• [math.ST]Optimal Sparsity Testing in Linear regression Model
• [physics.soc-ph]A Generative Model for Exploring Structure Regularities in Attributed Networks
• [q-fin.CP]Pricing options and computing implied volatilities using neural networks
• [quant-ph]Quantum Terrorism: Collective Vulnerability of Global Quantum Systems
• [stat.AP]Spatial trend analysis of gridded temperature data at varying spatial scales
• [stat.CO]Local dimension reduction of summary statistics for likelihood-free inference
• [stat.ME]A Discrepancy-Based Design for A/B Testing Experiments
• [stat.ME]A Sequential Significance Test for Treatment by Covariate Interactions
• [stat.ME]An essay on copula modelling for discrete random vectors; or how to pour new wine into old bottles
• [stat.ME]Rescaling and other forms of unsupervised preprocessing introduce bias into cross-validation
• [stat.ML]Complexity of Linear Regions in Deep Networks
• [stat.ML]Empowering individual trait prediction using interactions
• [stat.ML]Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
• [stat.ML]Robust estimation of tree structured Gaussian Graphical Model
• [stat.ML]Spurious Vanishing Problem in Approximate Vanishing Ideal
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• [astro-ph.IM]Laser Communication and Coordination Control of Spacecraft Swarms
Himangshu Kalita, Leonard Vance, Vishnu Reddy, Jekan Thangavelautham
http://arxiv.org/abs/1901.08875v1
• [cs.AI]Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
Thomy Phan, Kyrill Schmid, Lenz Belzner, Thomas Gabor, Sebastian Feld, Claudia Linnhoff-Popien
http://arxiv.org/abs/1901.08761v1
• [cs.AI]Evaluation Function Approximation for Scrabble
Rishabh Agarwal
http://arxiv.org/abs/1901.08728v1
• [cs.AI]Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning
Quanshi Zhang, Lixin Fan, Bolei Zhou
http://arxiv.org/abs/1901.08813v1
• [cs.CL]A BERT Baseline for the Natural Questions
Chris Alberti, Kenton Lee, Michael Collins
http://arxiv.org/abs/1901.08634v1
• [cs.CL]Automatic Parallel Corpus Creation for Hindi-English News Translation Task
Aditya Kumar Pathak, Priyankit Acharya, Dilpreet Kaur, Rakesh Chandra Balabantaray
http://arxiv.org/abs/1901.08625v1
• [cs.CL]BioBERT: pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, Jaewoo Kang
http://arxiv.org/abs/1901.08746v1
• [cs.CL]Emergent Linguistic Phenomena in Multi-Agent Communication Games
Laura Graesser, Kyunghyun Cho, Douwe Kiela
http://arxiv.org/abs/1901.08706v1
• [cs.CL]Misleading Metadata Detection on YouTube
Priyank Palod, Ayush Patwari, Sudhanshu Bahety, Saurabh Bagchi, Pawan Goyal
http://arxiv.org/abs/1901.08759v1
• [cs.CV]Deep Multimodality Model for Multi-task Multi-view Learning
Lecheng Zheng, Yu Cheng, Jingrui He
http://arxiv.org/abs/1901.08723v1
• [cs.CV]Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
Priyanka Mandikal, R. Venkatesh Babu
http://arxiv.org/abs/1901.08906v1
• [cs.CV]Face morphing detection in the presence of printing/scanning and heterogeneous image sources
Matteo Ferrara, Annalisa Franco, Davide Maltoni
http://arxiv.org/abs/1901.08811v1
• [cs.CV]FaceForensics++: Learning to Detect Manipulated Facial Images
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner
http://arxiv.org/abs/1901.08971v1
• [cs.CV]Improving Image Captioning by Leveraging Knowledge Graphs
Yimin Zhou, Yiwei Sun, Vasant Honavar
http://arxiv.org/abs/1901.08942v1
• [cs.CV]Joint shape learning and segmentation for medical images using a minimalistic deep network
Balamurali Murugesan, Kaushik Sarveswaran, Sharath M Shankaranarayana, Keerthi Ram, Mohanasankar Sivaprakasam
http://arxiv.org/abs/1901.08824v1
• [cs.CV]Multiple Hypothesis Tracking Algorithm for Multi-Target Multi-Camera Tracking with Disjoint Views
Kwangjin Yoon, Young-min Song, Moongu Jeon
http://arxiv.org/abs/1901.08787v1
• [cs.CV]One-Class Convolutional Neural Network
Poojan Oza, Vishal M. Patel
http://arxiv.org/abs/1901.08688v1
• [cs.CV]Revisiting Self-Supervised Visual Representation Learning
Alexander Kolesnikov, Xiaohua Zhai, Lucas Beyer
http://arxiv.org/abs/1901.09005v1
• [cs.CV]Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection
Samet Akçay, Amir Atapour-Abarghouei, Toby P. Breckon
http://arxiv.org/abs/1901.08954v1
• [cs.CV]Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data
Nima Tajbakhsh, Yufei Hu, Junli Cao, Xingjian Yan, Yi Xiao, Yong Lu, Jianming Liang, Demetri Terzopoulos, Xiaowei Ding
http://arxiv.org/abs/1901.08707v1
• [cs.CV]Vision-based inspection system employing computer vision & neural networks for detection of fractures in manufactured components
Sarthak J Shetty
http://arxiv.org/abs/1901.08864v1
• [cs.CY]Optimal Design of SWIPT-Aware Fog Computing Networks
Jingxian Liu, Ke Xiong, Pingyi Fan, Zhangdui Zhong, Khaled Ben Letaief
http://arxiv.org/abs/1901.08997v1
• [cs.DB]HRDBMS: Combining the Best of Modern and Traditional Relational Databases
Jason Arnold, Boris Glavic, Ioan Raicu
http://arxiv.org/abs/1901.08666v1
• [cs.DC]Ambitious Data Science Can Be Painless
Hatef Monajemi, Riccardo Murri, Eric Jonas, Percy Liang, Victoria Stodden, David L. Donoho
http://arxiv.org/abs/1901.08705v1
• [cs.DC]Partitioned Paxos via the Network Data Plane
Huynh Tu Dang, Pietro Bressana, Han Wang, Ki Suh Lee, Noa Zilberman, Hakim Weatherspoon, Marco Canini, Fernando Pedone, Robert Soulé
http://arxiv.org/abs/1901.08806v1
• [cs.IR]Topological and Semantic Graph-based Author Disambiguation on DBLP Data in Neo4j
Valentina Franzoni, Michele Lepri, Alfredo Milani
http://arxiv.org/abs/1901.08977v1
• [cs.IT]Continuous Analog Channel Estimation Aided Beamforming for Massive MIMO Systems
Vishnu V. Ratnam, Andreas F. Molisch
http://arxiv.org/abs/1901.08763v1
• [cs.IT]Distributed Matrix-Vector Multiplication: A Convolutional Coding Approach
Anindya Bijoy Das, Aditya Ramamoorthy
http://arxiv.org/abs/1901.08716v1
• [cs.IT]New Lower Bounds for Permutation Codes using Linear Block Codes
Giacomo Micheli, Alessandro Neri
http://arxiv.org/abs/1901.08858v1
• [cs.IT]Robust Transceiver Design for MIMO Decode-and-Forward Full-Duplex Relay
Ali Kariminezhad, Aydin Sezgin
http://arxiv.org/abs/1901.08782v1
• [cs.LG]Ask less - Scale Market Research without Annoying Your Customers
Venkatesh Umaashankar, Girish Shanmugam S
http://arxiv.org/abs/1901.08744v1
• [cs.LG]Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification
Pawel Trajdos, Marcin Majak
http://arxiv.org/abs/1901.08827v1
• [cs.LG]Bayesian surrogate learning in dynamic simulator-based regression problems
Xi Chen, Mike Hobson
http://arxiv.org/abs/1901.08898v1
• [cs.LG]Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously
Julian Zimmert, Haipeng Luo, Chen-Yu Wei
http://arxiv.org/abs/1901.08779v1
• [cs.LG]Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
Sicong Liu, Anshumali Shrivastava, Junzhao Du, Lin Zhong
http://arxiv.org/abs/1901.08730v1
• [cs.LG]Communication-Efficient and Decentralized Multi-Task Boosting while Learning the Collaboration Graph
Valentina Zantedeschi, Aurélien Bellet, Marc Tommasi
http://arxiv.org/abs/1901.08460v2
• [cs.LG]Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Antonin Raffin, Ashley Hill, Kalifou René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat
http://arxiv.org/abs/1901.08651v1
• [cs.LG]Diffusion Variational Autoencoders
Luis A. Pérez Rey, Vlado Menkovski, Jacobus W. Portegies
http://arxiv.org/abs/1901.08991v1
• [cs.LG]Diversity-Sensitive Conditional Generative Adversarial Networks
Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee
http://arxiv.org/abs/1901.09024v1
• [cs.LG]Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington
http://arxiv.org/abs/1901.08987v1
• [cs.LG]Improving Adversarial Robustness via Promoting Ensemble Diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu
http://arxiv.org/abs/1901.08846v1
• [cs.LG]Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization
Pengqian Yu, Joon Sern Lee, Ilya Kulyatin, Zekun Shi, Sakyasingha Dasgupta
http://arxiv.org/abs/1901.08740v1
• [cs.LG]On the Limitations of Representing Functions on Sets
Edward Wagstaff, Fabian B. Fuchs, Martin Engelcke, Ingmar Posner, Michael Osborne
http://arxiv.org/abs/1901.09006v1
• [cs.LG]Provably efficient RL with Rich Observations via Latent State Decoding
Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
http://arxiv.org/abs/1901.09018v1
• [cs.LG]SecureBoost: A Lossless Federated Learning Framework
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, Qiang Yang
http://arxiv.org/abs/1901.08755v1
• [cs.LG]Self-Supervised Generalisation with Meta Auxiliary Learning
Shikun Liu, Andrew J. Davison, Edward Johns
http://arxiv.org/abs/1901.08933v1
• [cs.LG]State-Regularized Recurrent Neural Networks
Cheng Wang, Mathias Niepert
http://arxiv.org/abs/1901.08817v1
• [cs.LG]Subspace Robust Wasserstein distances
François-Pierre Paty, Marco Cuturi
http://arxiv.org/abs/1901.08949v1
• [cs.LG]Towards a Deeper Understanding of Adversarial Losses
Hao-Wen Dong, Yi-Hsuan Yang
http://arxiv.org/abs/1901.08753v1
• [cs.LG]Unsupervised speech representation learning using WaveNet autoencoders
Jan Chorowski, Ron J. Weiss, Samy Bengio, Aäron van den Oord
http://arxiv.org/abs/1901.08810v1
• [cs.LG]When Can Neural Networks Learn Connected Decision Regions?
Trung Le, Dinh Phung
http://arxiv.org/abs/1901.08710v1
• [cs.NE]A Neurally-Inspired Hierarchical Prediction Network for Spatiotemporal Sequence Learning and Prediction
Jielin Qiu, Ge Huang, Tai Sing Lee
http://arxiv.org/abs/1901.09002v1
• [cs.NE]A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data
Hassen Dhrif, Luis G. Sanchez Giraldo, Miroslav Kubat, Stefan Wuchty
http://arxiv.org/abs/1901.08619v1
• [cs.NE]Ablation Studies in Artificial Neural Networks
Richard Meyes, Melanie Lu, Constantin Waubert de Puiseau, Tobias Meisen
http://arxiv.org/abs/1901.08644v1
• [cs.RO]Effective Locomotion at Multiple Stride Frequencies Using Proprioceptive Feedback on a Legged Microrobot
Neel Doshi, Kaushik Jayaram, Samantha Castellanos, Scott Kuindersma, Robert J Wood
http://arxiv.org/abs/1901.08715v1
• [cs.RO]Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, Marco Hutter
http://arxiv.org/abs/1901.08652v1
• [cs.SI]Computational landscape of user behavior on social media
David Darmon, William Rand, Michelle Girvan
http://arxiv.org/abs/1901.08941v1
• [math.FA]Average sampling and average splines on combinatorial graphs
Isaac Z. Pesenson
http://arxiv.org/abs/1901.08726v1
• [math.ST]Concentration of quadratic forms under a Bernstein moment assumption
Pierre C Bellec
http://arxiv.org/abs/1901.08736v1
• [math.ST]Erratum: Higher Order Elicitability and Osband's Principle
Tobias Fissler, Johanna F. Ziegel
http://arxiv.org/abs/1901.08826v1
• [math.ST]Gaussian One-Armed Bandit and Optimization of Batch Data Processing
Alexander Kolnogorov
http://arxiv.org/abs/1901.08845v1
• [math.ST]Gibbs posterior convergence and the thermodynamic formalism
Kevin McGoff, Sayan Mukherjee, Andrew Nobel
http://arxiv.org/abs/1901.08641v1
• [math.ST]Optimal Sparsity Testing in Linear regression Model
Alexandra Carpentier, Nicolas Verzelen
http://arxiv.org/abs/1901.08802v1
• [physics.soc-ph]A Generative Model for Exploring Structure Regularities in Attributed Networks
Zhenhai Chang, Caiyan Jia, Xianjun Yin, Yimei Zheng
http://arxiv.org/abs/1901.08696v1
• [q-fin.CP]Pricing options and computing implied volatilities using neural networks
Shuaiqiang Liu, Cornelis W. Oosterlee, Sander M. Bohte
http://arxiv.org/abs/1901.08943v1
• [quant-ph]Quantum Terrorism: Collective Vulnerability of Global Quantum Systems
N. F. Johnson, F. J. Gomez-Ruiz, F. J. Rodriguez, L. Quiroga
http://arxiv.org/abs/1901.08873v1
• [stat.AP]Spatial trend analysis of gridded temperature data at varying spatial scales
Ola Haug, Thordis L Thorarinsdottir, Sigrunn H Sørbye, Christian L E Franzke
http://arxiv.org/abs/1901.08874v1
• [stat.CO]Local dimension reduction of summary statistics for likelihood-free inference
Jukka Sirén, Samuel Kaski
http://arxiv.org/abs/1901.08855v1
• [stat.ME]A Discrepancy-Based Design for A/B Testing Experiments
Yiou Li, Xiao Huang, Lulu Kang
http://arxiv.org/abs/1901.08984v1
• [stat.ME]A Sequential Significance Test for Treatment by Covariate Interactions
Min Qian, Bibhas Chakraborty, Raju Maiti
http://arxiv.org/abs/1901.08738v1
• [stat.ME]An essay on copula modelling for discrete random vectors; or how to pour new wine into old bottles
Gery Geenens
http://arxiv.org/abs/1901.08741v1
• [stat.ME]Rescaling and other forms of unsupervised preprocessing introduce bias into cross-validation
Amit Moscovich, Saharon Rosset
http://arxiv.org/abs/1901.08974v1
• [stat.ML]Complexity of Linear Regions in Deep Networks
Boris Hanin, David Rolnick
http://arxiv.org/abs/1901.09021v1
• [stat.ML]Empowering individual trait prediction using interactions
Damian Gola, Inke R. König
http://arxiv.org/abs/1901.08814v1
• [stat.ML]Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise
Andrei Kulunchakov, Julien Mairal
http://arxiv.org/abs/1901.08788v1
• [stat.ML]Robust estimation of tree structured Gaussian Graphical Model
Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis
http://arxiv.org/abs/1901.08770v1
• [stat.ML]Spurious Vanishing Problem in Approximate Vanishing Ideal
Hiroshi Kera, Yoshihiko Hasegawa
http://arxiv.org/abs/1901.08798v1
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