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.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.GT - 计算机科学与博弈论 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MM - 多媒体 cs.NE - 神经与进化计算 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 hep-ph - 高能物理现象学 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.comp-ph - 计算物理学 q-bio.NC - 神经元与认知 q-fin.CP -计算金融学 q-fin.GN - 通用财务 q-fin.ST - 统计金融学 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

    • [cs.AI]STRIPS Action Discovery
    • [cs.AI]Tackling Air Traffic Conflicts as a Weighted CSP : Experiments with the Lumberjack Method
    • [cs.AI]The Tensor Brain: Semantic Decoding for Perception and Memory
    • [cs.CL]ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
    • [cs.CL]Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
    • [cs.CL]Do We Need Word Order Information for Cross-lingual Sequence Labeling
    • [cs.CL]Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
    • [cs.CL]Harnessing Code Switching to Transcend the Linguistic Barrier
    • [cs.CL]Introducing the diagrammatic mode
    • [cs.CL]Learning Robust and Multilingual Speech Representations
    • [cs.CL]Lost in Embedding Space: Explaining Cross-Lingual Task Performance with Eigenvalue Divergence
    • [cs.CL]LowResourceEval-2019: a shared task on morphological analysis for low-resource languages
    • [cs.CL]Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
    • [cs.CV]2018 Robotic Scene Segmentation Challenge
    • [cs.CV]A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme
    • [cs.CV]A Deeper Look into Hybrid Images
    • [cs.CV]Adversarial Incremental Learning
    • [cs.CV]Automatic marker-free registration of tree point-cloud data based on rotating projection
    • [cs.CV]ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
    • [cs.CV]Fast Video Object Segmentation using the Global Context Module
    • [cs.CV]Image Embedded Segmentation: Combining Supervised and Unsupervised Objectives through Generative Adversarial Networks
    • [cs.CV]Multiple Object
    890
    Tracking by Flowing and Fusing
    • [cs.CV]Semantic Adversarial Perturbations using Learnt Representations
    • [cs.CV]The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
    • [cs.CV]The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Induction
    • [cs.CV]Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
    • [cs.CV]Weakly Supervised Instance Segmentation by Deep Multi-Task Community Learning
    • [cs.CV]Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm
    • [cs.DB]Proceedings of Symposium on Data Mining Applications 2014
    • [cs.DC]SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications
    • [cs.DC]Shared-Memory Parallel Maximal Clique Enumeration from Static and Dynamic Graphs
    • [cs.GT]Fictitious Play Outperforms Counterfactual Regret Minimization
    • [cs.HC]Developing an Augmented Reality Tourism App through User-Centred Design (Extended Version)
    • [cs.IR]Correcting for Selection Bias in Learning-to-rank Systems
    • [cs.IR]Graph Convolution Machine for Context-aware Recommender System
    • [cs.IR]Learning to Structure Long-term Dependence for Sequential Recommendation
    • [cs.IT]A Spatiotemporal Framework for Information Freshness in IoT Uplink Networks
    • [cs.IT]A generalization of the $α$-divergences based on comparable and distinct weighted means
    • [cs.IT]Asymptotic regime analysis of NOMA uplink networks under QoS delay Constraints
    • [cs.IT]Computability of the Zero-Error capacity with Kolmogorov Oracle
    • [cs.IT]Exploiting Randomly-located Blockages for Large-Scale Deployment of Intelligent Surfaces
    • [cs.IT]Multichannel ALOHA with Exploration Phase
    • [cs.IT]Numerically Stable Binary Gradient Coding
    • [cs.IT]On the Age of Information in Internet of Things Systems with Correlated Devices
    • [cs.IT]Optimal Beamforming for MISO Communications via Intelligent Reflecting Surfaces
    • [cs.IT]Peregrine: Network Localization and Navigation with Scalable Inference and Efficient Operation
    • [cs.IT]Polynomial Time Algorithms for Constructing Optimal Binary AIFV-$2$ Codes
    • [cs.IT]SCAN List Decoding of Polar Codes
    • [cs.IT]Towards Power-Efficient Aerial Communications via Dynamic Multi-UAV Cooperation
    • [cs.IT]Who Should Google Scholar Update More Often?
    • [cs.LG]A Graph-Based Approach for Active Learning in Regression
    • [cs.LG]A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks
    • [cs.LG]A tutorial on ensembles and deep learning fusion with MNIST as guiding thread: A complex heterogeneous fusion scheme reaching 10 digits error
    • [cs.LG]AVATAR — Machine Learning Pipeline Evaluation Using Surrogate Model
    • [cs.LG]Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain
    • [cs.LG]Adversarial Training for Aspect-Based Sentiment Analysis with BERT
    • [cs.LG]An Implicit Attention Mechanism for Deep Learning Pedestrian Re-identification Frameworks
    • [cs.LG]Analysing Affective Behavior in the First ABAW 2020 Competition
    • [cs.LG]Bayesian Reasoning with Deep-Learned Knowledge
    • [cs.LG]Better Multi-class Probability Estimates for Small Data Sets
    • [cs.LG]Black-Box Saliency Map Generation Using Bayesian Optimisation
    • [cs.LG]Constructing Deep Neural Networks with a Priori Knowledge of Wireless Tasks
    • [cs.LG]Ensemble Grammar Induction For Detecting Anomalies in Time Series
    • [cs.LG]FOCUS: Dealing with Label Quality Disparity in Federated Learning
    • [cs.LG]Fase-AL — Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning
    • [cs.LG]GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
    • [cs.LG]HAMLET — A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection
    • [cs.LG]How Does BN Increase Collapsed Neural Network Filters?
    • [cs.LG]Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks
    • [cs.LG]Learning Discrete Distributions by Dequantization
    • [cs.LG]Multi-Marginal Optimal Transport Defines a Generalized Metric
    • [cs.LG]Multi-Participant Multi-Class Vertical Federated Learning
    • [cs.LG]Non-Determinism in TensorFlow ResNets
    • [cs.LG]On Constraint Definability in Tractable Probabilistic Models
    • [cs.LG]Safe Predictors for Enforcing Input-Output Specifications
    • [cs.LG]Scalable Psychological Momentum Forecasting in Esports
    • [cs.LG]Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
    • [cs.LG]Towards a Kernel based Physical Interpretation of Model Uncertainty
    • [cs.LG]Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding
    • [cs.LG]Which way? Direction-Aware Attributed Graph Embedding
    • [cs.LG]stream-learn — open-source Python library for difficult data stream batch analysis
    • [cs.MM]NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder
    • [cs.NE]A Study of Fitness Landscapes for Neuroevolution
    • [cs.RO]Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs
    • [cs.RO]Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces
    • [cs.RO]Model-free vision-based shaping of deformable plastic materials
    • [cs.RO]Universally Safe Swerve Manoeuvres for Autonomous Driving
    • [cs.SD]Continuous speech separation: dataset and analysis
    • [cs.SE]Brand Intelligence Analytics
    • [cs.SI]D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks
    • [cs.SI]Echo Chambers Exist! (But They’re Full of Opposing Views)
    • [cs.SI]Exploiting Uncertainty in Popularity Prediction of Information Diffusion Cascades Using Self-exciting Point Processes
    • [cs.SI]Going beyond accuracy: estimating homophily in social networks using predictions
    • [cs.SI]How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
    • [econ.EM]Blocked Clusterwise Regression
    • [eess.AS]Conditioning Autoencoder Latent Spaces for Real-Time Timbre Interpolation and Synthesis
    • [eess.IV]Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval
    • [eess.IV]Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
    • [eess.SP]An IoT based Active Building Surveillance System using Raspberry Pi and NodeMCU
    • [eess.SP]Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
    • [eess.SP]EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
    • [eess.SP]Grassmannian Optimization for Online Tensor Completion and Tracking in the t-SVD Algebra
    • [eess.SP]REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
    • [hep-ph]Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
    • [math.OC]Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout
    • [math.PR]Almost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices
    • [math.ST]Asymptotics of Cross-Validation
    • [math.ST]Empirical tail copulas for functional data
    • [math.ST]Finite-time Analysis of Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards
    • [math.ST]Reproducing kernels based schemes for nonparametric regression
    • [physics.comp-ph]Hamiltonian Neural Networks for solving differential equations
    • [q-bio.NC]Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data
    • [q-fin.CP]Deep combinatorial optimisation for optimal stopping time problems and stochastic impulse control. Application to swing options pricing and fixed transaction costs options hedging
    • [q-fin.GN]Nonparametric sign prediction of high-dimensional correlation matrix coefficients
    • [q-fin.ST]The Impact of Oil and Gold Prices Shock on Tehran Stock Exchange: A Copula Approach
    • [quant-ph]Analysis of Y00 Protocol under Quantum Generalization of a Fast Correlation Attack: Toward Information-Theoretic Security
    • [quant-ph]Hierarchical decoding to reduce hardware requirements for quantum computing
    • [quant-ph]Kelly Betting with Quantum Payoff: a continuous variable approach
    • [stat.AP]Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database
    • [stat.AP]Uncovering life-course patterns with causal discovery and survival analysis
    • [stat.CO]Real-time Linear Operator Construction and State Estimation with Kalman Filter
    • [stat.ME]A Comparison of Prior Elicitation Aggregation using the Classical Method and SHELF
    • [stat.ME]Assessing the Calibration of Subdistribution Hazard Models in Discrete Time
    • [stat.ME]Supervised Functional PCA with Covariate Dependent Mean and Covariance Structure
    • [stat.ML]A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net
    • [stat.ML]A scale-dependent notion of effective dimension
    • [stat.ML]An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
    • [stat.ML]Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm
    • [stat.ML]NCVis: Noise Contrastive Approach for Scalable Visualization
    • [stat.ML]TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions
    • [stat.ML]Transport Gaussian Processes for Regression

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

    • [cs.AI]STRIPS Action Discovery
    Alejandro Suárez-Hernández, Javier Segovia-Aguas, Carme Torras, Guillem Alenyà
    http://arxiv.org/abs/2001.11457v1

    • [cs.AI]Tackling Air Traffic Conflicts as a Weighted CSP : Experiments with the Lumberjack Method
    Thomas Chaboud, Cédric Pralet, Nicolas Schmidt
    http://arxiv.org/abs/2001.11390v1

    • [cs.AI]The Tensor Brain: Semantic Decoding for Perception and Memory
    Volker Tresp, Sahand Sharifzadeh, Dario Konopatzki, Yunpu Ma
    http://arxiv.org/abs/2001.11027v1

    • [cs.CL]ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
    Zuohui Fu, Yikun Xian, Shijie Geng, Yingqiang Ge, Yuting Wang, Xin Dong, Guang Wang, Gerard de Melo
    http://arxiv.org/abs/2001.11121v1

    • [cs.CL]Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks
    Lena Schmidt, Julie Weeds, Julian P. T. Higgins
    http://arxiv.org/abs/2001.11268v1

    • [cs.CL]Do We Need Word Order Information for Cross-lingual Sequence Labeling
    Zihan Liu, Pascale Fung
    http://arxiv.org/abs/2001.11164v1

    • [cs.CL]Don’t Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
    Subendhu Rongali, Luca Soldaini, Emilio Monti, Wael Hamza
    http://arxiv.org/abs/2001.11458v1

    • [cs.CL]Harnessing Code Switching to Transcend the Linguistic Barrier
    Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Jaime G. Carbonell
    http://arxiv.org/abs/2001.11258v1

    • [cs.CL]Introducing the diagrammatic mode
    Tuomo Hiippala, John A. Bateman
    http://arxiv.org/abs/2001.11224v1

    • [cs.CL]Learning Robust and Multilingual Speech Representations
    Kazuya Kawakami, Luyu Wang, Chris Dyer, Phil Blunsom, Aaron van den Oord
    http://arxiv.org/abs/2001.11128v1

    • [cs.CL]Lost in Embedding Space: Explaining Cross-Lingual Task Performance with Eigenvalue Divergence
    Haim Dubossarsky, Ivan Vulić, Roi Reichart, Anna Korhonen
    http://arxiv.org/abs/2001.11136v1

    • [cs.CL]LowResourceEval-2019: a shared task on morphological analysis for low-resource languages
    Elena Klyachko, Alexey Sorokin, Natalia Krizhanovskaya, Andrew Krizhanovsky, Galina Ryazanskaya
    http://arxiv.org/abs/2001.11285v1

    • [cs.CL]Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages
    Edoardo M. Ponti, Ivan Vulić, Ryan Cotterell, Marinela Parovic, Roi Reichart, Anna Korhonen
    http://arxiv.org/abs/2001.11453v1

    • [cs.CV]2018 Robotic Scene Segmentation Challenge
    Max Allan, Satoshi Kondo, Sebastian Bodenstedt, Stefan Leger, Rahim Kadkhodamohammadi, Imanol Luengo, Felix Fuentes, Evangello Flouty, Ahmed Mohammed, Marius Pedersen, Avinash Kori, Varghese Alex, Ganapathy Krishnamurthi, David Rauber, Robert Mendel, Christoph Palm, Sophia Bano, Guinther Saibro, Chi-Sheng Shih, Hsun-An Chiang, Juntang Zhuang, Junlin Yang, Vladimir Iglovikov, Anton Dobrenkii, Madhu Reddiboina, Anubhav Reddy, Xingtong Liu, Cong Gao, Mathias Unberath, Mahdi Azizian, Danail Stoyanov, Lena Maier-Hein, Stefanie Speidel
    http://arxiv.org/abs/2001.11190v1

    • [cs.CV]A CNN With Multi-scale Convolution for Hyperspectral Image Classification using Target-Pixel-Orientation scheme
    Jayasree Saha, Yuvraj Khanna, Jayanta Mukherjee
    http://arxiv.org/abs/2001.11198v1

    • [cs.CV]A Deeper Look into Hybrid Images
    Jimut Bahan Pal
    http://arxiv.org/abs/2001.11302v1

    • [cs.CV]Adversarial Incremental Learning
    Ankur Singh
    http://arxiv.org/abs/2001.11152v1

    • [cs.CV]Automatic marker-free registration of tree point-cloud data based on rotating projection
    Xiuxian Xu, Pei Wang, Xiaozheng Gan, Yaxin Li, Li Zhang, Qing Zhang, Mei Zhou, Yinghui Zhao, Xinwei Li
    http://arxiv.org/abs/2001.11192v1

    • [cs.CV]ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
    Lichao Mou, Yuansheng Hua, Pu Jin, Xiao Xiang Zhu
    http://arxiv.org/abs/2001.11394v1

    • [cs.CV]Fast Video Object Segmentation using the Global Context Module
    Yu Li, Zhuoran Shen, Ying Shan
    http://arxiv.org/abs/2001.11243v1

    • [cs.CV]Image Embedded Segmentation: Combining Supervised and Unsupervised Objectives through Generative Adversarial Networks
    C. T. Sari, G. N. Gunesli, C. Sokmensuer, C. Gunduz-Demir
    http://arxiv.org/abs/2001.11202v1

    • [cs.CV]Multiple Object
    890
    Tracking by Flowing and Fusing
    Jimuyang Zhang, Sanping Zhou, Xin Chang, Fangbin Wan, Jinjun Wang, Yang Wu, Dong Huang
    http://arxiv.org/abs/2001.11180v1

    • [cs.CV]Semantic Adversarial Perturbations using Learnt Representations
    Isaac Dunn, Tom Melham, Daniel Kroening
    http://arxiv.org/abs/2001.11055v1

    • [cs.CV]The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
    Yuli Zhang, Yeyang He, Shaowen Zhu, Xinhan Di
    http://arxiv.org/abs/2001.11194v1

    • [cs.CV]The Ladder Algorithm: Finding Repetitive Structures in Medical Images by Induction
    Rhydian Windsor, Amir Jamaludin
    http://arxiv.org/abs/2001.11284v1

    • [cs.CV]Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform
    Jongmin Yu, Duyong Kim, Younkwon Lee, Moongu Jeon
    http://arxiv.org/abs/2001.11175v1

    • [cs.CV]Weakly Supervised Instance Segmentation by Deep Multi-Task Community Learning
    Seohyun Kim, Jaedong Hwang, Jeany Son, Bohyung Han
    http://arxiv.org/abs/2001.11207v1

    • [cs.CV]Weakly Supervised Segmentation of Cracks on Solar Cells using Normalized Lp Norm
    Martin Mayr, Mathis Hoffmann, Andreas Maier, Vincent Christlein
    http://arxiv.org/abs/2001.11248v1

    • [cs.DB]Proceedings of Symposium on Data Mining Applications 2014
    Basit Qureshi, Yasir Javed
    http://arxiv.org/abs/2001.11324v1

    • [cs.DC]SLO-ML: A Language for Service Level Objective Modelling in Multi-cloud Applications
    Abdessalam Elhabbash, Assylbek Jumagaliyev, Gordon S. Blair, Yehia Elkhatib
    http://arxiv.org/abs/2001.11093v1

    • [cs.DC]Shared-Memory Parallel Maximal Clique Enumeration from Static and Dynamic Graphs
    Apurba Das, Seyed-Vahid Sanei-Mehri, Srikanta Tirthapura
    http://arxiv.org/abs/2001.11433v1

    • [cs.GT]Fictitious Play Outperforms Counterfactual Regret Minimization
    Sam Ganzfried
    http://arxiv.org/abs/2001.11165v1

    • [cs.HC]Developing an Augmented Reality Tourism App through User-Centred Design (Extended Version)
    Meredydd Williams, Kelvin K. K. Yao, Jason R. C. Nurse
    http://arxiv.org/abs/2001.11131v1

    • [cs.IR]Correcting for Selection Bias in Learning-to-rank Systems
    Zohreh Ovaisi, Ragib Ahsan, Yifan Zhang, Kathryn Vasilaky, Elena Zheleva
    http://arxiv.org/abs/2001.11358v1

    • [cs.IR]Graph Convolution Machine for Context-aware Recommender System
    Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie, Yongdong Zhang
    http://arxiv.org/abs/2001.11402v1

    • [cs.IR]Learning to Structure Long-term Dependence for Sequential Recommendation
    Renqin Cai, Qinglei Wang, Chong Wang, Xiaobing Liu
    http://arxiv.org/abs/2001.11369v1

    • [cs.IT]A Spatiotemporal Framework for Information Freshness in IoT Uplink Networks
    Mustafa Emara, Hesham ElSawy, Gerhard Bauch
    http://arxiv.org/abs/2001.11333v1

    • [cs.IT]A generalization of the $α$-divergences based on comparable and distinct weighted means
    Frank Nielsen
    http://arxiv.org/abs/2001.09660v2

    • [cs.IT]Asymptotic regime analysis of NOMA uplink networks under QoS delay Constraints
    Mouktar Bello
    http://arxiv.org/abs/2001.11423v1

    • [cs.IT]Computability of the Zero-Error capacity with Kolmogorov Oracle
    Holger Boche, Christian Deppe
    http://arxiv.org/abs/2001.11442v1

    • [cs.IT]Exploiting Randomly-located Blockages for Large-Scale Deployment of Intelligent Surfaces
    Mustafa A. Kishk, Mohamed-Slim Alouini
    http://arxiv.org/abs/2001.10766v2

    • [cs.IT]Multichannel ALOHA with Exploration Phase
    Jinho Choi
    http://arxiv.org/abs/2001.11115v1

    • [cs.IT]Numerically Stable Binary Gradient Coding
    Neophytos Charalambides, Hessam Mahdavifar, Alfred O. Hero III
    http://arxiv.org/abs/2001.11449v1

    • [cs.IT]On the Age of Information in Internet of Things Systems with Correlated Devices
    Bo Zhou, Walid Saad
    http://arxiv.org/abs/2001.11162v1

    • [cs.IT]Optimal Beamforming for MISO Communications via Intelligent Reflecting Surfaces
    Xianghao Yu, Dongfang Xu, Robert Schober
    http://arxiv.org/abs/2001.11429v1

    • [cs.IT]Peregrine: Network Localization and Navigation with Scalable Inference and Efficient Operation
    Bryan Teague, Zhenyu Liu, Florian Meyer, Andrea Conti, Moe Z. Win
    http://arxiv.org/abs/2001.11494v1

    • [cs.IT]Polynomial Time Algorithms for Constructing Optimal Binary AIFV-$2$ Codes
    Mordecai Golin, Elfarouk Harb
    http://arxiv.org/abs/2001.11170v1

    • [cs.IT]SCAN List Decoding of Polar Codes
    Charles Pillet, Carlo Condo, Valerio Bioglio
    http://arxiv.org/abs/2001.11283v1

    • [cs.IT]Towards Power-Efficient Aerial Communications via Dynamic Multi-UAV Cooperation
    Lin Xiang, Lei Lei, Symeon Chatzinotas, Björn Ottersten, Robert Schober
    http://arxiv.org/abs/2001.11255v1

    • [cs.IT]Who Should Google Scholar Update More Often?
    Melih Bastopcu, Sennur Ulukus
    http://arxiv.org/abs/2001.11500v1

    • [cs.LG]A Graph-Based Approach for Active Learning in Regression
    Hongjing Zhang, S. S. Ravi, Ian Davidson
    http://arxiv.org/abs/2001.11143v1

    • [cs.LG]A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks
    Phan-Minh Nguyen, Huy Tuan Pham
    http://arxiv.org/abs/2001.11443v1

    • [cs.LG]A tutorial on ensembles and deep learning fusion with MNIST as guiding thread: A complex heterogeneous fusion scheme reaching 10 digits error
    S. Tabik, R. F. Alvear-Sandoval, M. M. Ruiz, J. L. Sancho-Gómez, A. R. Figueiras-Vidal, F. Herrera
    http://arxiv.org/abs/2001.11486v1

    • [cs.LG]AVATAR — Machine Learning Pipeline Evaluation Using Surrogate Model
    Tien-Dung Nguyen, Tomasz Maszczyk, Katarzyna Musial, Marc-Andre Zöller, Bogdan Gabrys
    http://arxiv.org/abs/2001.11158v1

    • [cs.LG]Adversarial Attacks on Convolutional Neural Networks in Facial Recognition Domain
    Yigit Alparslan, Jeremy Keim-Shenk, Shweta Khade, Rachel Greenstadt
    http://arxiv.org/abs/2001.11137v1

    • [cs.LG]Adversarial Training for Aspect-Based Sentiment Analysis with BERT
    Akbar Karimi, Leonardo Rossi, Andrea Prati, Katharina Full
    http://arxiv.org/abs/2001.11316v1

    • [cs.LG]An Implicit Attention Mechanism for Deep Learning Pedestrian Re-identification Frameworks
    Ehsan Yaghoubi, Diana Borza, Pendar Alirezazadeh, Aruna Kumar, Hugo Proença
    http://arxiv.org/abs/2001.11267v1

    • [cs.LG]Analysing Affective Behavior in the First ABAW 2020 Competition
    Dimitrios Kollias, Attila Schulc, Elnar Hajiyev, Stefanos Zafeiriou
    http://arxiv.org/abs/2001.11409v1

    • [cs.LG]Bayesian Reasoning with Deep-Learned Knowledge
    Jakob Knollmüller, Torsten Enßlin
    http://arxiv.org/abs/2001.11031v1

    • [cs.LG]Better Multi-class Probability Estimates for Small Data Sets
    Tuomo Alasalmi, Jaakko Suutala, Heli Koskimäki, Juha Röning
    http://arxiv.org/abs/2001.11242v1

    • [cs.LG]Black-Box Saliency Map Generation Using Bayesian Optimisation
    Mamuku Mokuwe, Michael Burke, Anna Sergeevna Bosman
    http://arxiv.org/abs/2001.11366v1

    • [cs.LG]Constructing Deep Neural Networks with a Priori Knowledge of Wireless Tasks
    Jia Guo, Chenyang Yang
    http://arxiv.org/abs/2001.11355v1

    • [cs.LG]Ensemble Grammar Induction For Detecting Anomalies in Time Series
    Yifeng Gao, Jessica Lin, Constantin Brif
    http://arxiv.org/abs/2001.11102v1

    • [cs.LG]FOCUS: Dealing with Label Quality Disparity in Federated Learning
    Yiqiang Chen, Xiaodong Yang, Xin Qin, Han Yu, Biao Chen, Zhiqi Shen
    http://arxiv.org/abs/2001.11359v1

    • [cs.LG]Fase-AL — Adaptation of Fast Adaptive Stacking of Ensembles for Supporting Active Learning
    Agustín Alejandro Ortiz-Díaz, Fabiano Baldo, Laura María Palomino Mariño, Alberto Verdecia Cabrera
    http://arxiv.org/abs/2001.11466v1

    • [cs.LG]GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values
    Shangtong Zhang, Bo Liu, Shimon Whiteson
    http://arxiv.org/abs/2001.11113v1

    • [cs.LG]HAMLET — A Learning Curve-Enabled Multi-Armed Bandit for Algorithm Selection
    Mischa Schmidt, Julia Gastinger, Sébastien Nicolas, Anett Schülke
    http://arxiv.org/abs/2001.11261v1

    • [cs.LG]How Does BN Increase Collapsed Neural Network Filters?
    Sheng Zhou, Xinjiang Wang, Wenjie Li, Litong Feng, Ping Luo
    http://arxiv.org/abs/2001.11216v1

    • [cs.LG]Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks
    Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
    http://arxiv.org/abs/2001.11279v1

    • [cs.LG]Learning Discrete Distributions by Dequantization
    Emiel Hoogeboom, Taco S. Cohen, Jakub M. Tomczak
    http://arxiv.org/abs/2001.11235v1

    • [cs.LG]Multi-Marginal Optimal Transport Defines a Generalized Metric
    Liang Mi, José Bento
    http://arxiv.org/abs/2001.11114v1

    • [cs.LG]Multi-Participant Multi-Class Vertical Federated Learning
    Siwei Feng, Han Yu
    http://arxiv.org/abs/2001.11154v1

    • [cs.LG]Non-Determinism in TensorFlow ResNets
    Miguel Morin, Matthew Willetts
    http://arxiv.org/abs/2001.11396v1

    • [cs.LG]On Constraint Definability in Tractable Probabilistic Models
    Ioannis Papantonis, Vaishak Belle
    http://arxiv.org/abs/2001.11349v1

    • [cs.LG]Safe Predictors for Enforcing Input-Output Specifications
    Stephen Mell, Olivia Brown, Justin Goodwin, Sung-Hyun Son
    http://arxiv.org/abs/2001.11062v1

    • [cs.LG]Scalable Psychological Momentum Forecasting in Esports
    Alfonso White, Daniela M. Romano
    http://arxiv.org/abs/2001.11274v1

    • [cs.LG]Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
    Szilárd Aradi
    http://arxiv.org/abs/2001.11231v1

    • [cs.LG]Towards a Kernel based Physical Interpretation of Model Uncertainty
    Rishabh Singh, Jose C. Principe
    http://arxiv.org/abs/2001.11495v1

    • [cs.LG]Urban2Vec: Incorporating Street View Imagery and POIs for Multi-Modal Urban Neighborhood Embedding
    Zhecheng Wang, Haoyuan Li, Ram Rajagopal
    http://arxiv.org/abs/2001.11101v1

    • [cs.LG]Which way? Direction-Aware Attributed Graph Embedding
    Zekarias T. Kefato, Nasrullah Sheikh, Alberto Montresor
    http://arxiv.org/abs/2001.11297v1

    • [cs.LG]stream-learn — open-source Python library for difficult data stream batch analysis
    Paweł Ksieniewicz, Paweł Zyblewski
    http://arxiv.org/abs/2001.11077v1

    • [cs.MM]NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder
    Helard Martinez, M. C. Farias, A. Hines
    http://arxiv.org/abs/2001.11406v1

    • [cs.NE]A Study of Fitness Landscapes for Neuroevolution
    Nuno M. Rodrigues, Sara Silva, Leonardo Vanneschi
    http://arxiv.org/abs/2001.11272v1

    • [cs.RO]Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs
    Max Basescu, Joseph Moore
    http://arxiv.org/abs/2001.11478v1

    • [cs.RO]Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces
    Dale McConachie, Thomas Power, Peter Mitrano, Dmitry Berenson
    http://arxiv.org/abs/2001.11051v1

    • [cs.RO]Model-free vision-based shaping of deformable plastic materials
    Andrea Cherubini, Valerio Ortenzi, Akansel Cosgun, Robert Lee, Peter Corke
    http://arxiv.org/abs/2001.11196v1

    • [cs.RO]Universally Safe Swerve Manoeuvres for Autonomous Driving
    Ryan De Iaco, Stephen L. Smith, Krzysztof Czarnecki
    http://arxiv.org/abs/2001.11159v1

    • [cs.SD]Continuous speech separation: dataset and analysis
    Zhuo Chen, Takuya Yoshioka, Liang Lu, Tianyan Zhou, Zhong Meng, Yi Luo, Jian Wu, Jinyu Li
    http://arxiv.org/abs/2001.11482v1

    • [cs.SE]Brand Intelligence Analytics
    A. Fronzetti Colladon, F. Grippa
    http://arxiv.org/abs/2001.11479v1

    • [cs.SI]D2M: Dynamic Defense and Modeling of Adversarial Movement in Networks
    Scott Freitas, Andrew Wicker, Duen Horng Chau, Joshua Neil
    http://arxiv.org/abs/2001.11108v1

    • [cs.SI]Echo Chambers Exist! (But They’re Full of Opposing Views)
    Jonathan Bright, Nahema Marchal, Bharath Ganesh, Stevan Rudinac
    http://arxiv.org/abs/2001.11461v1

    • [cs.SI]Exploiting Uncertainty in Popularity Prediction of Information Diffusion Cascades Using Self-exciting Point Processes
    Quyu Kong, Marian-Andrei Rizoiu, Lexing Xie
    http://arxiv.org/abs/2001.11132v1

    • [cs.SI]Going beyond accuracy: estimating homophily in social networks using predictions
    George Berry, Antonio Sirianni, Ingmar Weber, Jisun An, Michael Macy
    http://arxiv.org/abs/2001.11171v1

    • [cs.SI]How Much and When Do We Need Higher-order Information in Hypergraphs? A Case Study on Hyperedge Prediction
    Se-eun Yoon, Hyungseok Song, Kijung Shin, Yung Yi
    http://arxiv.org/abs/2001.11181v1

    • [econ.EM]Blocked Clusterwise Regression
    Max Cytrynbaum
    http://arxiv.org/abs/2001.11130v1

    • [eess.AS]Conditioning Autoencoder Latent Spaces for Real-Time Timbre Interpolation and Synthesis
    Joseph T Colonel, Sam Keene
    http://arxiv.org/abs/2001.11296v1

    • [eess.IV]Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval
    Abin Jose, Erik Stefan Ottlik, Christian Rohlfing, Jens-Rainer Ohm
    http://arxiv.org/abs/2001.11400v1

    • [eess.IV]Semi-Automatic Generation of Tight Binary Masks and Non-Convex Isosurfaces for Quantitative Analysis of 3D Biological Samples
    Sourabh Bhide, Ralf Mikut, Maria Leptin, Johannes Stegmaier
    http://arxiv.org/abs/2001.11469v1

    • [eess.SP]An IoT based Active Building Surveillance System using Raspberry Pi and NodeMCU
    Sruthy. S, S. Yamuna, Sudhish N. George
    http://arxiv.org/abs/2001.11340v1

    • [eess.SP]Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems
    Ahmet M. Elbir, A Papazafeiropoulos, P. Kourtessis, S. Chatzinotas
    http://arxiv.org/abs/2001.11085v1

    • [eess.SP]EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications
    Xiaotong Gu, Zehong Cao, Alireza Jolfaei, Peng Xu, Dongrui Wu, Tzyy-Ping Jung, Chin-Teng Lin
    http://arxiv.org/abs/2001.11337v1

    • [eess.SP]Grassmannian Optimization for Online Tensor Completion and Tracking in the t-SVD Algebra
    Kyle Gilman, Laura Balzano
    http://arxiv.org/abs/2001.11419v1

    • [eess.SP]REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild
    Rahul Duggal, Scott Freitas, Cao Xiao, Duen Horng Chau, Jimeng Sun
    http://arxiv.org/abs/2001.11363v1

    • [hep-ph]Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN)
    Yasir Alanazi, N. Sato, Tianbo Liu, W. Melnitchouk, Michelle P. Kuchera, Evan Pritchard, Michael Robertson, Ryan Strauss, Luisa Velasco, Yaohang Li
    http://arxiv.org/abs/2001.11103v1

    • [math.OC]Experimental Validation of a Real-Time Optimal Controller for Coordination of CAVs in a Multi-Lane Roundabout
    Behdad Chalaki, Logan E. Beaver, Andreas A. Malikopoulos
    http://arxiv.org/abs/2001.11176v1

    • [math.PR]Almost sure convergence of the largest and smallest eigenvalues of high-dimensional sample correlation matrices
    Johannes Heiny, Thomas Mikosch
    http://arxiv.org/abs/2001.11459v1

    • [math.ST]Asymptotics of Cross-Validation
    Morgane Austern, Wenda Zhou
    http://arxiv.org/abs/2001.11111v1

    • [math.ST]Empirical tail copulas for functional data
    John H. J. Einmahl, Johan Segers
    http://arxiv.org/abs/2001.11408v1

    • [math.ST]Finite-time Analysis of Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards
    Vrettos Moulos
    http://arxiv.org/abs/2001.11201v1

    • [math.ST]Reproducing kernels based schemes for nonparametric regression
    Bilel Bousselmi, Jean-François Dupuy, Abderrazek Karoui
    http://arxiv.org/abs/2001.11213v1

    • [physics.comp-ph]Hamiltonian Neural Networks for solving differential equations
    Marios Mattheakis, David Sondak, Akshunna S. Dogra, Pavlos Protopapas
    http://arxiv.org/abs/2001.11107v1

    • [q-bio.NC]Retrieving the structure of probabilistic sequences of auditory stimuli from EEG data
    Noslen Hernández, Raymundo Machado de Azevedo Neto, Aline Duarte, Guilherme Ost, Ricardo Fraiman, Antonio Galves, Claudia D. Vargas
    http://arxiv.org/abs/2001.11502v1

    • [q-fin.CP]Deep combinatorial optimisation for optimal stopping time problems and stochastic impulse control. Application to swing options pricing and fixed transaction costs options hedging
    Thomas Deschatre, Joseph Mikael
    http://arxiv.org/abs/2001.11247v1

    • [q-fin.GN]Nonparametric sign prediction of high-dimensional correlation matrix coefficients
    Christian Bongiorno, Damien Challet
    http://arxiv.org/abs/2001.11214v1

    • [q-fin.ST]The Impact of Oil and Gold Prices Shock on Tehran Stock Exchange: A Copula Approach
    Amir T. Payandeh Najafabadi, Marjan Qazvini, Reza Ofoghi
    http://arxiv.org/abs/2001.11275v1

    • [quant-ph]Analysis of Y00 Protocol under Quantum Generalization of a Fast Correlation Attack: Toward Information-Theoretic Security
    Takehisa Iwakoshi
    http://arxiv.org/abs/2001.11150v1

    • [quant-ph]Hierarchical decoding to reduce hardware requirements for quantum computing
    Nicolas Delfosse
    http://arxiv.org/abs/2001.11427v1

    • [quant-ph]Kelly Betting with Quantum Payoff: a continuous variable approach
    Salvatore Tirone, Maddalena Ghio, Giulia Livieri, Vittorio Giovannetti, Stefano Marmi
    http://arxiv.org/abs/2001.11395v1

    • [stat.AP]Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database
    Hananeh Alambeigi, Anthony D. McDonald, Srinivas R. Tankasala
    http://arxiv.org/abs/2001.11087v1

    • [stat.AP]Uncovering life-course patterns with causal discovery and survival analysis
    Bojan Kostic, Romain Crastes dit Sourd, Stephane Hess, Joachim Scheiner, Christian Holz-Rau, Francisco C. Pereira
    http://arxiv.org/abs/2001.11399v1

    • [stat.CO]Real-time Linear Operator Construction and State Estimation with Kalman Filter
    Tsuyoshi Ishizone, Kazuyuki Nakamura
    http://arxiv.org/abs/2001.11256v1

    • [stat.ME]A Comparison of Prior Elicitation Aggregation using the Classical Method and SHELF
    Cameron J. Williams, Kevin J. Wilson, Nina Wilson
    http://arxiv.org/abs/2001.11365v1

    • [stat.ME]Assessing the Calibration of Subdistribution Hazard Models in Discrete Time
    Moritz Berger, Matthias Schmid
    http://arxiv.org/abs/2001.11240v1

    • [stat.ME]Supervised Functional PCA with Covariate Dependent Mean and Covariance Structure
    Fei Ding, Shiyuan He, David E. Jones, Jianhua Z. Huang
    http://arxiv.org/abs/2001.11425v1

    • [stat.ML]A Hybrid Two-layer Feature Selection Method Using GeneticAlgorithm and Elastic Net
    Fatemeh Amini, Guiping Hu
    http://arxiv.org/abs/2001.11177v1

    • [stat.ML]A scale-dependent notion of effective dimension
    Oksana Berezniuk, Alessio Figalli, Raffaele Ghigliazza, Kharen Musaelian
    http://arxiv.org/abs/
    6c80
    /2001.10872v1
    6c80
    /2001.10872v1)

    • [stat.ML]An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
    Samuele Tosatto, Riad Akrour, Jan Peters
    http://arxiv.org/abs/2001.10972v2

    • [stat.ML]Kernel Selection for Modal Linear Regression: Optimal Kernel and IRLS Algorithm
    Ryoya Yamasaki, Toshiyuki Tanaka
    http://arxiv.org/abs/2001.11168v1

    • [stat.ML]NCVis: Noise Contrastive Approach for Scalable Visualization
    Aleksandr Artemenkov, Maxim Panov
    http://arxiv.org/abs/2001.11411v1

    • [stat.ML]TCMI: a non-parametric mutual-dependence estimator for multivariate continuous distributions
    Benjamin Regler, Matthias Scheffler, Luca M. Ghiringhelli
    http://arxiv.org/abs/2001.11212v1

    • [stat.ML]Transport Gaussian Processes for Regression
    Gonzalo Rios
    http://arxiv.org/abs/2001.11473v1