cond-mat.dis-nn - 无序系统与神经网络 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.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.LO - 计算逻辑 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.SI - 社交网络与信息网络 econ.GN - 一般经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.OC - 优化与控制 math.ST - 统计理论 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习
• [cond-mat.dis-nn]A Probability Density Theory for Spin-Glass Systems
• [cs.AI]Intelligent Roundabout Insertion using Deep Reinforcement Learning
• [cs.AI]Towards Intelligent Robotic Process Automation for BPMers
• [cs.CL]”Love is as Complex as Math”: Metaphor Generation System for Social Chatbot
• [cs.CL]On the comparability of Pre-trained Language Models
• [cs.CL]Question Type Classification Methods Comparison
• [cs.CL]Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
• [cs.CL]TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
• [cs.CL]Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
• [cs.CR]Improving PKI, BGP, and DNS Using Blockchain: A Systematic Review
• [cs.CV]A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
• [cs.CV]Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks
• [cs.CV]FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides
• [cs.CV]From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion
• [cs.CV]HandAugment: A Simple Data Augmentation for HANDS19 Challenge Task 1 — Depth-Based 3D Hand Pose Estimation
• [cs.CV]Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
• [cs.DC]A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs
• [cs.DC]AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing
• [cs.DC]Improving Grid Computing Performance by Optimally Reducing Checkpointing Effect
• [cs.DC]Peer-to-Peer Blockchain based Energy Trading
• [cs.IR]Characterizing Reading Time on Enterprise Emails
• [cs.IR]Modeling Information Need of Users in Search Sessions
• [cs.IT]Biometric and Physical Identifiers with Correlated Noise for Controllable Private Authentication
• [cs.IT]Convolution Idempotents with a given Zero-set
• [cs.IT]Efficient Information Reconciliation for Energy-Time Entanglement Quantum Key Distribution
• [cs.IT]Integer-Forcing Architectures for Uplink Cloud Radio Access Networks
• [cs.IT]Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networks
• [cs.LG]A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
• [cs.LG]Aleatoric and Epistemic Uncertainty with Random Forests
• [cs.LG]Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis
• [cs.LG]Automated Relational Meta-learning
• [cs.LG]Bayesian task embedding for few-shot Bayesian optimization
• [cs.LG]Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference
• [cs.LG]Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation
• [cs.LG]Large-scale Gender/Age Prediction of Tumblr Users
• [cs.LG]Learning Accurate Integer Transformer Machine-Translation Models
• [cs.LG]Making Sense of Reinforcement Learning and Probabilistic Inference
• [cs.LG]Memory-Loss is Fundamental for Stability and Distinguishes the Echo State Property Threshold in Reservoir Computing & Beyond
• [cs.LG]Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning
• [cs.LG]Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
• [cs.LG]The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling
• [cs.LG]Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
• [cs.LO]Bounds on the size of PC and URC formulas
• [cs.NE]A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization
• [cs.RO]Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
• [cs.SD]A Comparative Evaluation of Pitch Modification Techniques
• [cs.SD]Eigenresiduals for improved Parametric Speech Synthesis
• [cs.SD]Excitation-based Voice Quality Analysis and Modification
• [cs.SD]On the Mutual Information between Source and Filter Contributions for Voice Pathology Detection
• [cs.SI]Computing Accessibility Metrics for Argentina
• [cs.SI]Detecting Areas of Potential High Prevalence of Chagas in Argentina
• [cs.SI]Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
• [econ.GN]Judicial Favoritism of Politicians: Evidence from Small Claims Court
• [eess.IV]A Machine Learning Imaging Core using Separable FIR-IIR Filters
• [eess.IV]A Review on InSAR Phase Denoising
• [eess.IV]DeepFocus: a Few-Shot Microscope Slide Auto-Focus using a Sample Invariant CNN-based Sharpness Function
• [eess.IV]Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy
• [eess.IV]Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks
• [eess.SP]A Two-Stage Batch Algorithm for Nonlinear Static Parameter Estimation
• [eess.SP]Identifiability Conditions for Compressive Multichannel Blind Deconvolution
• [math.OC]A Proximal Linearization-based Decentralized Method for Nonconvex Problems with Nonlinear Constraints
• [math.OC]Stochastic Gradient Langevin Dynamics on a Distributed Network
• [math.ST]Scalability and robustness of spectral embedding: landmark diffusion is all you need
• [quant-ph]Meaning updating of density matrices
• [quant-ph]Operationally meaningful representations of physical systems in neural networks
• [stat.AP]Data Analysis of the Responses to Professor Abigail Thompson’s Statement on Mandatory Diversity Statements
• [stat.AP]Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
• [stat.AP]Predicting competitions by pairing conditional logistic regression and subjective Bayes: An Academy Awards case study
• [stat.ME]Local polynomial regression for pooled response data
• [stat.ME]Multivariate Temporal Point Process Regression
• [stat.ME]Pearson chi^2-divergence Approach to Gaussian Mixture Reduction and its Application to Gaussian-sum Filter and Smoother
• [stat.ML]Decomposable Probability-of-Success Metrics in Algorithmic Search
• [stat.ML]Explainable outlier detection through decision tree conditioning
• [stat.ML]Improve Unsupervised Domain Adaptation with Mixup Training
• [stat.ML]Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
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• [cond-mat.dis-nn]A Probability Density Theory for Spin-Glass Systems
Gavin S. Hartnett, Masoud Mohseni
http://arxiv.org/abs/2001.00927v1
• [cs.AI]Intelligent Roundabout Insertion using Deep Reinforcement Learning
Alessandro Paolo Capasso, Giulio Bacchiani, Daniele Molinari
http://arxiv.org/abs/2001.00786v1
• [cs.AI]Towards Intelligent Robotic Process Automation for BPMers
Simone Agostinelli, Andrea Marrella, Massimo Mecella
http://arxiv.org/abs/2001.00804v1
• [cs.CL]“Love is as Complex as Math”: Metaphor Generation System for Social Chatbot
Danning Zheng, Ruihua Song, Tianran Hu, Hao Fu, Jin Zhou
http://arxiv.org/abs/2001.00733v1
• [cs.CL]On the comparability of Pre-trained Language Models
Matthias Aßenmacher, Christian Heumann
http://arxiv.org/abs/2001.00781v1
• [cs.CL]Question Type Classification Methods Comparison
Tamirlan Seidakhmetov
http://arxiv.org/abs/2001.00571v1
• [cs.CL]Read Beyond the Lines: Understanding the Implied Textual Meaning via a Skim and Intensive Reading Model
Guoxiu He, Zhe Gao, Zhuoren Jiang, Yangyang Kang, Changlong Sun, Xiaozhong Liu, Wei Lu
http://arxiv.org/abs/2001.00572v1
• [cs.CL]TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Ziyi Yang, Chenguang Zhu, Robert Gmyr, Michael Zeng, Xuedong Huang
http://arxiv.org/abs/2001.00725v1
• [cs.CL]Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation
Goran Glavaš, Swapna Somasundaran
http://arxiv.org/abs/2001.00891v1
• [cs.CR]Improving PKI, BGP, and DNS Using Blockchain: A Systematic Review
Faizan Safdar Ali, Alptekin Kupcu
http://arxiv.org/abs/2001.00747v1
• [cs.CV]A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
Pei Xu, Shan Huang, Hongzhen Wang, Hao Song, Shen Huang, Qi Ju
http://arxiv.org/abs/2001.00722v1
• [cs.CV]Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks
Andreas Bühler, Niclas Vödisch, Mathias Bürki, Lukas Schaupp
http://arxiv.org/abs/2001.00762v1
• [cs.CV]FFusionCGAN: An end-to-end fusion method for few-focus images using conditional GAN in cytopathological digital slides
Xiebo Geng, Sibo Liua, Wei Han, Xu Li, Jiabo Ma, Jingya Yu, Xiuli Liu, Sahoqun Zeng, Li Chen, Shenghua Cheng
http://arxiv.org/abs/2001.00692v1
• [cs.CV]From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion
Jesse Scott, Christopher Funk, Bharadwaj Ravichandran, John H. Challis, Robert T. Collins, Yanxi Liu
http://arxiv.org/abs/2001.00657v1
• [cs.CV]HandAugment: A Simple Data Augmentation for HANDS19 Challenge Task 1 — Depth-Based 3D Hand Pose Estimation
Zhaohui Zhang, Shipeng Xie, Mingxiu Chen, Haichao Zhu
http://arxiv.org/abs/2001.00702v1
• [cs.CV]Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
Nachiket Deo, Mohan M. Trivedi
http://arxiv.org/abs/2001.00735v1
• [cs.DC]A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs
Jiajia Li, Mahesh Lakshminarasimhan, Xiaolong Wu, Ang Li, Catherine Olschanowsky, Kevin Barker
http://arxiv.org/abs/2001.00660v1
• [cs.DC]AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing
Vinu E. Venugopal, Martin Theobald, Samira Chaychi, Amal Tawakuli
http://arxiv.org/abs/2001.00164v2
• [cs.DC]Improving Grid Computing Performance by Optimally Reducing Checkpointing Effect
Garba Aliyu, Kana A. F. D., Abdullahi Mohammed, Idris Abdulmumin, Shehu Adamu, Fatsuma Jauro
http://arxiv.org/abs/2001.00884v1
• [cs.DC]Peer-to-Peer Blockchain based Energy Trading
Faizan Ali, Moayad Aloqaily, Omar Alfandi, Oznur Ozkasap
http://arxiv.org/abs/2001.00746v1
• [cs.IR]Characterizing Reading Time on Enterprise Emails
Xinyi Li, Chia-Jung Lee, Milad Shokouhi, Susan Dumais
http://arxiv.org/abs/2001.00802v1
• [cs.IR]Modeling Information Need of Users in Search Sessions
Kishaloy Halder, Heng-Tze Cheng, Ellie Ka In Chio, Georgios Roumpos, Tao Wu, Ritesh Agarwal
http://arxiv.org/abs/2001.00861v1
• [cs.IT]Biometric and Physical Identifiers with Correlated Noise for Controllable Private Authentication
Onur Günlü, Rafael F. Schaefer, H. Vincent Poor
http://arxiv.org/abs/2001.00847v1
• [cs.IT]Convolution Idempotents with a given Zero-set
Aditya Siripuram, Brad Osgood
http://arxiv.org/abs/2001.00739v1
• [cs.IT]Efficient Information Reconciliation for Energy-Time Entanglement Quantum Key Distribution
Siyi Yang, Murat Can Sarihan, Kai-Chi Chang, Chee Wei Wong, Lara Dolecek
http://arxiv.org/abs/2001.00611v1
• [cs.IT]Integer-Forcing Architectures for Uplink Cloud Radio Access Networks
Islam El Bakoury, Bobak Nazer
http://arxiv.org/abs/2001.00607v1
• [cs.IT]Novel Wake-up Scheme for Energy-Efficient Low-Latency Mobile Devices in 5G Networks
Soheil Rostami, Kari Heiska, Oleksandr Puchko, Kari Leppanen, Mikko Valkama
http://arxiv.org/abs/2001.00914v1
• [cs.LG]A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, Gunhee Kim
http://arxiv.org/abs/2001.00689v1
• [cs.LG]Aleatoric and Epistemic Uncertainty with Random Forests
Mohammad Hossein Shaker, Eyke Hüllermeier
http://arxiv.org/abs/2001.00893v1
• [cs.LG]Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysis
Roozbeh Yousefzadeh, Dianne P. O’Leary
http://arxiv.org/abs/2001.00682v1
• [cs.LG]Automated Relational Meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao, Yaliang Li, Bolin Ding, Ruirui Li, Zhenhui Li
http://arxiv.org/abs/2001.00745v1
• [cs.LG]Bayesian task embedding for few-shot Bayesian optimization
Steven Atkinson, Sayan Ghosh, Natarajan Chennimalai-Kumar, Genghis Khan, Liping Wang
http://arxiv.org/abs/2001.00637v1
• [cs.LG]Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference
Jianghao Shen, Yonggan Fu, Yue Wang, Pengfei Xu, Zhangyang Wang, Yingyan Lin
http://arxiv.org/abs/2001.00705v1
• [cs.LG]Joint Goal and Strategy Inference across Heterogeneous Demonstrators via Reward Network Distillation
Letian Chen, Rohan Paleja, Muyleng Ghuy, Matthew Gombolay
http://arxiv.org/abs/2001.00503v2
• [cs.LG]Large-scale Gender/Age Prediction of Tumblr Users
Yao Zhan, Changwei Hu, Yifan Hu, Tejaswi Kasturi, Shanmugam Ramasamy, Matt Gillingham, Keith Yamamoto
http://arxiv.org/abs/2001.00594v1
• [cs.LG]Learning Accurate Integer Transformer Machine-Translation Models
Ephrem Wu
http://arxiv.org/abs/2001.00926v1
• [cs.LG]Making Sense of Reinforcement Learning and Probabilistic Inference
Brendan O’Donoghue, Ian Osband, Catalin Ionescu
http://arxiv.org/abs/2001.00805v1
• [cs.LG]Memory-Loss is Fundamental for Stability and Distinguishes the Echo State Property Threshold in Reservoir Computing & Beyond
G Manjunath
http://arxiv.org/abs/2001.00766v1
• [cs.LG]Optimizing Wireless Systems Using Unsupervised and Reinforced-Unsupervised Deep Learning
Dong Liu, Chengjian Sun, Chenyang Yang, Lajos Hanzo
http://arxiv.org/abs/2001.00784v1
• [cs.LG]Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU
Patrick Kidger, Terry Lyons
http://arxiv.org/abs/2001.00706v1
• [cs.LG]The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling
Yaoshiang Ho, Samuel Wookey
http://arxiv.org/abs/2001.00570v1
• [cs.LG]Zero-Shot Reinforcement Learning with Deep Attention Convolutional Neural Networks
Sahika Genc, Sunil Mallya, Sravan Bodapati, Tao Sun, Yunzhe Tao
http://arxiv.org/abs/2001.00605v1
• [cs.LO]Bounds on the size of PC and URC formulas
Petr Kučera, Petr Savický
http://arxiv.org/abs/2001.00819v1
• [cs.NE]A Two stage Adaptive Knowledge Transfer Evolutionary Multi-tasking Based on Population Distribution for Multi/Many-Objective Optimization
Zhengping Liang, Weiqi Liang, Xiuju Xu, Zexuan Zhu
http://arxiv.org/abs/2001.00810v1
• [cs.RO]Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
Yipu Zhao, Patricio A. Vela
http://arxiv.org/abs/2001.00714v1
• [cs.SD]A Comparative Evaluation of Pitch Modification Techniques
Thomas Drugman, Thierry Dutoit
http://arxiv.org/abs/2001.00579v1
• [cs.SD]Eigenresiduals for improved Parametric Speech Synthesis
Thomas Drugman, Geoffrey Wilfart, Thierry Dutoit
http://arxiv.org/abs/2001.00581v1
• [cs.SD]Excitation-based Voice Quality Analysis and Modification
Thomas Drugman, Thierry Dutoit, Baris Bozkurt
http://arxiv.org/abs/2001.00582v1
• [cs.SD]On the Mutual Information between Source and Filter Contributions for Voice Pathology Detection
Thomas Drugman, Thomas Dubuisson, Thierry Dutoit
http://arxiv.org/abs/2001.00583v1
• [cs.SI]Computing Accessibility Metrics for Argentina
Carolina Lang, Tobias Carreira, German Cesar Dima, Lucila Berniell, Carlos Sarraute
http://arxiv.org/abs/2001.00596v1
• [cs.SI]Detecting Areas of Potential High Prevalence of Chagas in Argentina
Antonio Vazquez Brust, Tomas Olego, German Rosati, Carolina Lang, Guillermo Bozzoli, Diego Weinberg, Roberto Chuit, Martin A. Minnoni, Carlos Sarraute
http://arxiv.org/abs/2001.00604v1
• [cs.SI]Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
Kai Shu, Suhang Wang, Dongwon Lee, Huan Liu
http://arxiv.org/abs/2001.00623v1
• [econ.GN]Judicial Favoritism of Politicians: Evidence from Small Claims Court
Andre Assumpcao, Julio Trecenti
http://arxiv.org/abs/2001.00889v1
• [eess.IV]A Machine Learning Imaging Core using Separable FIR-IIR Filters
Masayoshi Asama, Leo F. Isikdogan, Sushma Rao, Bhavin V. Nayak, Gilad Michael
http://arxiv.org/abs/2001.00630v1
• [eess.IV]A Review on InSAR Phase Denoising
Gang Xu, Yandong Gao, Jinwei Li, Mengdao Xing
http://arxiv.org/abs/2001.00769v1
• [eess.IV]DeepFocus: a Few-Shot Microscope Slide Auto-Focus using a Sample Invariant CNN-based Sharpness Function
Adrian Shajkofci, Michael Liebling
http://arxiv.org/abs/2001.00667v1
• [eess.IV]Robust Self-Supervised Learning of Deterministic Errors in Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-ray Fluoroscopy
Jacky C. K. Chow, Steven K. Boyd, Derek D. Lichti, Janet L. Ronsky
http://arxiv.org/abs/2001.00686v1
• [eess.IV]Synthetic vascular structure generation for unsupervised pre-training in CTA segmentation tasks
Nil Stolt Ansó
http://arxiv.org/abs/2001.00666v1
• [eess.SP]A Two-Stage Batch Algorithm for Nonlinear Static Parameter Estimation
Kerry Sun, Demoz Gebre-Egziabher
http://arxiv.org/abs/2001.00672v1
• [eess.SP]Identifiability Conditions for Compressive Multichannel Blind Deconvolution
Satish Mulleti, Kiryung Lee, Yonina C. Eldar
http://arxiv.org/abs/2001.00613v1
• [math.OC]A Proximal Linearization-based Decentralized Method for Nonconvex Problems with Nonlinear Constraints
Yu Yang, Guoqiang Hu, Costas J. Spanos
http://arxiv.org/abs/2001.00767v1
• [math.OC]Stochastic Gradient Langevin Dynamics on a Distributed Network
Vyacheslav Kungurtsev
http://arxiv.org/abs/2001.00665v1
• [math.ST]Scalability and robustness of spectral embedding: landmark diffusion is all you need
Chao Shen, Hau-Tieng Wu
http://arxiv.org/abs/2001.00801v1
• [quant-ph]Meaning updating of density matrices
Bob Coecke, Konstantinos Meichanetzidis
http://arxiv.org/abs/2001.00862v1
• [quant-ph]Operationally meaningful representations of physical systems in neural networks
Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiene Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner
http://arxiv.org/abs/2001.00593v1
• [stat.AP]Data Analysis of the Responses to Professor Abigail Thompson’s Statement on Mandatory Diversity Statements
Joshua Paik, Igor Rivin
http://arxiv.org/abs/2001.00670v1
• [stat.AP]Hydrological time series forecasting using simple combinations: Big data testing and investigations on one-year ahead river flow predictability
Georgia Papacharalampous, Hristos Tyralis
http://arxiv.org/abs/2001.00811v1
• [stat.AP]Predicting competitions by pairing conditional logistic regression and subjective Bayes: An Academy Awards case study
Christopher T. Franck Christopher E. Wilson
http://arxiv.org/abs/2001.00878v1
• [stat.ME]Local polynomial regression for pooled response data
Dewei Wang, Xichen Mou, Xiang Li, Xianzheng Huang
http://arxiv.org/abs/2001.00915v1
• [stat.ME]Multivariate Temporal Point Process Regression
Xiwei Tang, Lexin Li
http://arxiv.org/abs/2001.00719v1
• [stat.ME]Pearson chi^2-divergence Approach to Gaussian Mixture Reduction and its Application to Gaussian-sum Filter and Smoother
Genshiro Kitagawa
http://arxiv.org/abs/2001.00727v1
• [stat.ML]Decomposable Probability-of-Success Metrics in Algorithmic Search
Tyler Sam, Jake Williams, Abel Tadesse, Huey Sun, George Montanez
http://arxiv.org/abs/2001.00742v1
• [stat.ML]Explainable outlier detection through decision tree conditioning
David Cortes
http://arxiv.org/abs/2001.00636v1
• [stat.ML]Improve Unsupervised Domain Adaptation with Mixup Training
Shen Yan, Huan Song, Nanxiang Li, Lincan Zou, Liu Ren
http://arxiv.org/abs/2001.00677v1
• [stat.ML]Wide Neural Networks with Bottlenecks are Deep Gaussian Processes
Devanshu Agrawal, Theodore Papamarkou, Jacob Hinkle
http://arxiv.org/abs/2001.00921v1