cs.AI - 人工智能

    cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.ET - 新兴技术 cs.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.MA - 多代理系统 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.GN - 一般经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.CA - 古典分析与常微分方程 math.CO - 组合数学 math.OC - 优化与控制 math.ST - 统计理论 physics.comp-ph - 计算物理学 physics.ed-ph - 物理教育 physics.flu-dyn - 流体动力学 q-bio.GN - 基因组学 q-bio.QM - 定量方法 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

    • [cs.AI]Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
    • [cs.AI]Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
    • [cs.AI]Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents
    • [cs.AI]Phase Transition Behavior of Cardinality and XOR Constraints
    • [cs.AI]Question Answering over Knowledge Graphs via Structural Query Patterns
    • [cs.AI]Towards Combinational Relation Linking over Knowledge Graphs
    • [cs.AI]Towards More Sample Efficiency inReinforcement Learning with Data Augmentation
    • [cs.CL]Automatic Extraction of Personality from Text: Challenges and Opportunities
    • [cs.CL]Building Dynamic Knowledge Graphs from Text-based Games
    • [cs.CL]Depth-Adaptive Transformer
    • [cs.CL]Domain-agnostic Question-Answering with Adversarial Training
    • [cs.CL]Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
    • [cs.CL]Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
    • [cs.CL]GPU-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition
    • [cs.CL]Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity
    • [cs.CL]Improving Transformer-based Speech Recognition Using Unsupervised Pre-training
    • [cs.CL]Kernel Graph Attention Network for Fact Verification
    • [cs.CL]MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
    • [cs.CL]Scalable Neural Dialogue State Tracking
    • [cs.CL]Speech-VGG: A deep feature extractor for speech processing
    • [cs.CL]Toward estimating personal well-being using voice
    • [cs.CL]Transductive Parsing for Universal Decompositional Semantics
    • [cs.CL]Transformer-based Acoustic Modeling for Hybrid Speech Recognition
    • [cs.CR]The Security Reference Architecture for Blockchains: Towards a Standardized Model for Studying Vulnerabilities, Threats, and Defenses
    • [cs.CV]4-Connected Shift Residual Networks
    • [cs.CV]A Review of Visual Trackers and Analysis of its Application to Mobile Robot
    • [cs.CV]A low-power end-to-end hybrid neuromorphic framework for surveillance applications
    • [cs.CV]Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters
    • [cs.CV]Attacking Optical Flow
    • [cs.CV]Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
    • [cs.CV]CPWC: Contextual Point Wise Convolution for Object Recognition
    • [cs.CV]Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition
    • [cs.CV]Deep Set-to-Set Matching and Learning
    • [cs.CV]Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework
    • [cs.CV]Gaze360: Physically Unconstrained Gaze Estimation in the Wild
    • [cs.CV]Hetero-Center Loss for Cross-Modality Person Re-Identification
    • [cs.CV]Human Action Recognition in Drone Videos using a Few Aerial Training Examples
    • [cs.CV]J Regularization Improves Imbalanced Multiclass Segmentation
    • [cs.CV]Notable Site Recognition on Mobile Devices using Deep Learning with Crowd-sourced Imagery
    • [cs.CV]Predictive Coding Networks Meet Action Recognition
    • [cs.CV]Self-Correction for Human Parsing
    • [cs.CV]Structure Matters: Towards Generating Transferable Adversarial Images
    • [cs.CV]The SWAX Benchmark: Attacking Biometric Systems with Wax Figures
    • [cs.CV]Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
    • [cs.CV]Towards Automatic Annotation for Semantic Segmentation in Drone Videos
    • [cs.CV]Unsupervised particle sorting for high-resolution single-particle cryo-EM
    • [cs.CV]Vehicle detection and counting from VHR satellite images: efforts and open issues
    • [cs.CV]Weakly-Supervised Completion Moment Detection using Temporal Attention
    • [cs.CV]WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
    • [cs.CY]Artificial Intelligence and the Future of Psychiatry: Qualitative Findings from a Global Physician Survey
    • [cs.CY]Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community
    • [cs.CY]Quantifying the Carbon Emissions of Machine Learning
    • [cs.DC]Distributed Voting in Beep Model
    • [cs.DC]Mitigating the Performance-Efficiency Tradeoff in Resilient Memory Disaggregation
    • [cs.DC]Node-Aware Improvements to Allreduce
    • [cs.DC]On Fairness in Committee-based Blockchains
    • [cs.DC]Performance Evaluation of Advanced Features in CUDA Unified Memory
    • [cs.DC]XPC: Fast and Reliable Synchronous Transmission Protocols for 2-Phase Commit and 3-Phase Commit
    • [cs.ET]Kernel computations from large-scale random features obtained by Optical Processing Units
    • [cs.HC]On Automating Conversations
    • [cs.IR]From Personalization to Privatization: Meta Matrix Factorization for Private Rating Predictions
    • [cs.IR]Markov Random Fields for Collaborative Filtering
    • [cs.IR]One-Shot Template Matching for Automatic Document Data Capture
    • [cs.IR]Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
    • [cs.IT]An enhanced decoding algorithm for coded compressed sensing
    • [cs.IT]Convolutional Neural Networks for Space-Time Block Coding Recognition
    • [cs.IT]Energy-Efficient Resource Allocation for Secure NOMA-Enabled Mobile Edge Computing Networks
    • [cs.IT]On the Beneficial Role of a Finite Number of Scatterers for Wireless Physical Layer Security
    • [cs.IT]Parallel Stochastic Optimization Framework for Large-Scale Non-Convex Stochastic Problems
    • [cs.IT]Zero-Crossing Precoding With Maximum Distance to the Decision Threshold for Channels With 1-Bit Quantization and Oversampling
    • [cs.LG]A Prototypical Triplet Loss for Cover Detection
    • [cs.LG]A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data
    • [cs.LG]A deep active learning system for species identification and counting in camera trap images
    • [cs.LG]Abnormal Client Behavior Detection in Federated Learning
    • [cs.LG]Adversarial Example Detection by Classification for Deep Speech Recognition
    • [cs.LG]An Efficient EKF Based Algorithm For LSTM-Based Online Learning
    • [cs.LG]Bridging the Gap Between $f$-GANs and Wasserstein GANs
    • [cs.LG]Causal bootstrapping
    • [cs.LG]Class Mean Vectors, Self Monitoring and Self Learning for Neural Classifiers
    • [cs.LG]Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
    • [cs.LG]Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
    • [cs.LG]Composite Neural Network: Theory and Application to PM2.5 Prediction
    • [cs.LG]Convolutional Prototype Learning for Zero-Shot Recognition
    • [cs.LG]Detecting Extrapolation with Local Ensembles
    • [cs.LG]Edge Dithering for Robust Adaptive Graph Convolutional Networks
    • [cs.LG]Establishing an Evaluation Metric to Quantify Climate Change Image Realism
    • [cs.LG]Explicitly Bayesian Regularizations in Deep Learning
    • [cs.LG]Face representation by deep learning: a linear encoding in a parameter space?
    • [cs.LG]Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
    • [cs.LG]GANspection
    • [cs.LG]GraphSAC: Detecting anomalies in large-scale graphs
    • [cs.LG]IPO: Interior-point Policy Optimization under Constraints
    • [cs.LG]Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions
    • [cs.LG]Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy
    • [cs.LG]Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
    • [cs.LG]Mask Combination of Multi-layer Graphs for Global Structure Inference
    • [cs.LG]Multiphase flow prediction with deep neural networks
    • [cs.LG]Multiple Sample Clustering
    • [cs.LG]Neural Network Training with Approximate Logarithmic Computations
    • [cs.LG]Non-Gaussianity of Stochastic Gradient Noise
    • [cs.LG]On Predictive Information Sub-optimality of RNNs
    • [cs.LG]Orthogonal variance decomposition based feature selection
    • [cs.LG]Robust Training with Ensemble Consensus
    • [cs.LG]Self-supervised pre-training with acoustic configurations for replay spoofing detection
    • [cs.LG]Signal Combination for Language Identification
    • [cs.LG]Single Versus Union: Non-parallel Support Vector Machine Frameworks
    • [cs.LG]Smoothness-Adaptive Stochastic Bandits
    • [cs.LG]Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening
    • [cs.LG]Stability of Graph Neural Networks to Relative Perturbations
    • [cs.LG]Stochastic Feedforward Neural Networks: Universal Approximation
    • [cs.LG]Toward Automated Website Classification by Deep Learning
    • [cs.LG]Towards best practice in explaining neural network decisions with LRP
    • [cs.LG]Two-Step Sound Source Separation: Training on Learned Latent Targets
    • [cs.LG]Universal flow approximation with deep residual networks
    • [cs.LG]Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
    • [cs.LG]Vanishing Nodes: Another Phenomenon That Makes Training Deep Neural Networks Difficult
    • [cs.LG]Weakly Supervised Disentanglement with Guarantees
    • [cs.LG]Who wants accurate models? Arguing for a different metrics to take classification models seriously
    • [cs.LG]You May Not Need Order in Time Series Forecasting
    • [cs.MA]Distributed interference cancellation in multi-agent scenarios
    • [cs.NE]Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
    • [cs.NE]Improving the Gating Mechanism of Recurrent Neural Networks
    • [cs.NI]Scalable and Accurate Modeling of the Millimeter Wave Channel
    • [cs.RO]ALGAMES: A Fast Solver for Constrained Dynamic Games
    • [cs.RO]Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning
    • [cs.RO]Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments
    • [cs.RO]Learning Resilient Behaviors for Navigation Under Uncertainty Environments
    • [cs.RO]Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight
    • [cs.RO]Multiple criteria decision-making for lane-change model
    • [cs.RO]Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry
    • [cs.RO]Real-Time Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration
    • [cs.SD]Cross-Representation Transferability of Adversarial Perturbations: From Spectrograms to Audio Waveforms
    • [cs.SD]Cross-task pre-training for acoustic scene classification
    • [cs.SD]Deep speech inpainting of time-frequency masks
    • [cs.SD]Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer
    • [cs.SE]Non-cognitive abilities of exceptional software engineers: a Delphi study
    • [cs.SE]Software Engineering Education Beyond the Technical: A Systematic Literature Review
    • [cs.SI]A Theory of Extended Working Memory and its Role in Online Conversation Dynamics
    • [cs.SI]Hypergraph clustering with categorical edge labels
    • [cs.SI]Multiscale Evolutionary Perturbation Attack on Community Detection
    • [cs.SI]Simplification of networks via conservation of path diversity and minimisation of the search information
    • [econ.GN]Relative Net Utility and the Saint Petersburg Paradox
    • [eess.AS]Discriminative Neural Clustering for Speaker Diarisation
    • [eess.AS]Modeling plate and spring reverberation using a DSP-informed deep neural network
    • [eess.AS]Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
    • [eess.IV]A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs
    • [eess.IV]Fixed Pattern Noise Reduction for Infrared Images Based on Cascade Residual Attention CNN
    • [eess.IV]Image processing in DNA
    • [eess.IV]Image recovery from rotational and translational invariants
    • [eess.IV]Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
    • [eess.IV]Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning
    • [eess.IV]Penalizing small errors using an Adaptive Logarithmic Loss
    • [eess.IV]Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
    • [eess.IV]Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates
    • [eess.SP]A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding
    • [eess.SP]A Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification
    • [eess.SP]Learning to Communicate in a Noisy Environment
    • [eess.SP]Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
    • [eess.SP]New RLL Code with Improved Error Performance for Visible Light Communication
    • [eess.SP]Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task
    • [eess.SP]Secrecy Analyses of a Full-Duplex MIMOME Network
    • [math.CA]Finding duality and Riesz bases of exponentials on multi-tiles
    • [math.CO]A King in every two consecutive tournaments
    • [math.CO]Sequential metric dimension for random graphs
    • [math.OC]Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation
    • [math.OC]Geometry of Graph Partitions via Optimal Transport
    • [math.OC]Learning Adaptive Regularization for Image Labeling Using Geometric Assignment
    • [math.OC]The Practicality of Stochastic Optimization in Imaging Inverse Problems
    • [math.ST]Berry-Esseen bounds for Chernoff-type non-standard asymptotics in isotonic regression
    • [math.ST]Confidence intervals centred on bootstrap smoothed estimators: an impossibility result
    • [math.ST]Optimization Hierarchy for Fair Statistical Decision Problems
    • [physics.comp-ph]Coercing Machine Learning to Output Physically Accurate Results
    • [physics.ed-ph]Easy Java/JavaScript Simulations as a tool for Learning Analytics
    • [physics.flu-dyn]Optimal sensing for fish school identification
    • [q-bio.GN]Is graph biased feature selection of genes better than random?
    • [q-bio.QM]Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
    • [q-bio.QM]ProDyn0: Inferring calponin homology domain stretching behavior using graph neural networks
    • [quant-ph]A Domain-agnostic, Noise-resistant Evolutionary Variational Quantum Eigensolver for Hardware-efficient Optimization in the Hilbert Space
    • [stat.AP]Associations between park features, park satisfaction and park use in a multi-ethnic deprived urban area
    • [stat.AP]Ranking, and other Properties, of Elite Swimmers using Extreme Value Theory
    • [stat.ME]Hypothesis Testing in High-Dimensional Instrumental Variables Regression with an Application to Genomics Data
    • [stat.ME]Integrated Quantile RAnk Test (iQRAT) for gene-level associations in sequencing studies
    • [stat.ME]Linear Mixed Models for Comparing Dynamic Treatment Regimens on a Longitudinal Outcome in Sequentially Randomized Trials
    • [stat.ME]Principal Component Analysis: A Generalized Gini Approach
    • [stat.ME]Soft Tensor Regression
    • [stat.ME]Sparse Networks with Core-Periphery Structure
    • [stat.ML]Compressive Learning for Semi-Parametric Models
    • [stat.ML]Continual Learning for Infinite Hierarchical Change-Point Detection
    • [stat.ML]Direct Estimation of Differential Functional Graphical Models
    • [stat.ML]Embedded Bayesian Network Classifiers
    • [stat.ML]Kernelized Wasserstein Natural Gradient
    • [stat.ML]Orthogonal Nonnegative Tucker Decomposition
    • [stat.ML]Uncertainty Quantification with Generative Models

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    • [cs.AI]Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
    Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina, Richard Benjamins, Raja Chatila, Francisco Herrera
    http://arxiv.org/abs/1910.10045v1

    • [cs.AI]Faster and Safer Training by Embedding High-Level Knowledge into Deep Reinforcement Learning
    Haodi Zhang, Zihang Gao, Yi Zhou, Hao Zhang, Kaishun Wu, Fangzhen Lin
    http://arxiv.org/abs/1910.09986v1

    • [cs.AI]Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents
    Samuel Allen Alexander
    http://arxiv.org/abs/1910.09721v1

    • [cs.AI]Phase Transition Behavior of Cardinality and XOR Constraints
    Yash Pote, Saurabh Joshi, Kuldeep S. Meel
    http://arxiv.org/abs/1910.09755v1

    • [cs.AI]Question Answering over Knowledge Graphs via Structural Query Patterns
    Weiguo Zheng, Mei Zhang
    http://arxiv.org/abs/1910.09760v1

    • [cs.AI]Towards Combinational Relation Linking over Knowledge Graphs
    Weiguo Zheng, Mei Zhang
    http://arxiv.org/abs/1910.09879v1

    • [cs.AI]Towards More Sample Efficiency inReinforcement Learning with Data Augmentation
    Yijiong Lin, Jiancong Huang, Matthieu Zimmer, Juan Rojas, Paul Weng
    http://arxiv.org/abs/1910.09959v1

    • [cs.CL]Automatic Extraction of Personality from Text: Challenges and Opportunities
    Nazar Akrami, Johan Fernquist, Tim Isbister, Lisa Kaati, Björn Pelzer
    http://arxiv.org/abs/1910.09916v1

    • [cs.CL]Building Dynamic Knowledge Graphs from Text-based Games
    Mikulas Zelinka, Xingdi Yuan, Marc-Alexandre Cote, Romain Laroche, Adam Trischler
    http://arxiv.org/abs/1910.09532v2

    • [cs.CL]Depth-Adaptive Transformer
    Maha Elbayad, Jiatao Gu, Edouard Grave, Michael Auli
    http://arxiv.org/abs/1910.10073v1

    • [cs.CL]Domain-agnostic Question-Answering with Adversarial Training
    Seanie Lee, Donggyu Kim, Jangwon Park
    http://arxiv.org/abs/1910.09342v2

    • [cs.CL]Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection
    Giovanni Da San Martino, Alberto Barrón-Cedeño, Preslav Nakov
    http://arxiv.org/abs/1910.09982v1

    • [cs.CL]Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels
    Tariq Alhindi, Jonas Pfeiffer, Smaranda Muresan
    http://arxiv.org/abs/1910.09702v1

    • [cs.CL]GPU-Accelerated Viterbi Exact Lattice Decoder for Batched Online and Offline Speech Recognition
    Hugo Braun, Justin Luitjens, Ryan Leary
    http://arxiv.org/abs/1910.10032v1

    • [cs.CL]Grammatical Gender, Neo-Whorfianism, and Word Embeddings: A Data-Driven Approach to Linguistic Relativity
    Katharina Kann
    http://arxiv.org/abs/1910.09729v1

    • [cs.CL]Improving Transformer-based Speech Recognition Using Unsupervised Pre-training
    Dongwei Jiang, Xiaoning Lei, Wubo Li, Ne Luo, Yuxuan Hu, Wei Zou, Xiangang Li
    http://arxiv.org/abs/1910.09932v1

    • [cs.CL]Kernel Graph Attention Network for Fact Verification
    Zhenghao Liu, Chenyan Xiong, Maosong Sun
    http://arxiv.org/abs/1910.09796v1

    • [cs.CL]MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
    Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen
    http://arxiv.org/abs/1910.09753v1

    • [cs.CL]Scalable Neural Dialogue State Tracking
    Vevake Balaraman, Bernardo Magnini
    http://arxiv.org/abs/1910.09942v1

    • [cs.CL]Speech-VGG: A deep feature extractor for speech processing
    Pierre Beckmann, Mikolaj Kegler, Hugues Saltini, Milos Cernak
    http://arxiv.org/abs/1910.09909v1

    • [cs.CL]Toward estimating personal well-being using voice
    Samuel Kim, Namhee Kwon, Henry O’Connell
    http://arxiv.org/abs/1910.10082v1

    • [cs.CL]Transductive Parsing for Universal Decompositional Semantics
    Elias Stengel-Eskin, Aaron Steven White, Sheng Zhang, Benjamin Van Durme
    http://arxiv.org/abs/1910.10138v1

    • [cs.CL]Transformer-based Acoustic Modeling for Hybrid Speech Recognition
    Yongqiang Wang, Abdelrahman Mohamed, Duc Le, Chunxi Liu, Alex Xiao, Jay Mahadeokar, Hongzhao Huang, Andros Tjandra, Xiaohui Zhang, Frank Zhang, Christian Fuegen, Geoffrey Zweig, Michael L. Seltzer
    http://arxiv.org/abs/1910.09799v1

    • [cs.CR]The Security Reference Architecture for Blockchains: Towards a Standardized Model for Studying Vulnerabilities, Threats, and Defenses
    Ivan Homoliak, Sarad Venugopalan, Qingze Hum, Daniel Reijsbergen, Richard Schumi, Pawel Szalachowski
    http://arxiv.org/abs/1910.09775v1

    • [cs.CV]4-Connected Shift Residual Networks
    Andrew Brown, Pascal Mettes, Marcel Worring
    http://arxiv.org/abs/1910.09931v1

    • [cs.CV]A Review of Visual Trackers and Analysis of its Application to Mobile Robot
    Shaoze You, Hua Zhu, Menggang Li, Yutan Li
    http://arxiv.org/abs/1910.09761v1

    • [cs.CV]A low-power end-to-end hybrid neuromorphic framework for surveillance applications
    Andres Ussa, Luca Della Vedova, Vandana Reddy Padala, Deepak Singla, Jyotibdha Acharya, Charles Zhang Lei, Garrick Orchard, Arindam Basu, Bharath Ramesh
    http://arxiv.org/abs/1910.09806v1

    • [cs.CV]Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters
    Andre Barczak, Napoleon Reyes, Teo Susnjak
    http://arxiv.org/abs/1910.09758v1

    • [cs.CV]Attacking Optical Flow
    Anurag Ranjan, Joel Janai, Andreas Geiger, Michael J. Black
    http://arxiv.org/abs/1910.10053v1

    • [cs.CV]Beyond Human Parts: Dual Part-Aligned Representations for Person Re-Identification
    Jianyuan Guo, Yuhui Yuan, Lang Huang, Chao Zhang, Jinge Yao, Kai Han
    http://arxiv.org/abs/1910.10111v1

    • [cs.CV]CPWC: Contextual Point Wise Convolution for Object Recognition
    Pratik Mazumder, Pravendra Singh, Vinay Namboodiri
    http://arxiv.org/abs/1910.09643v1

    • [cs.CV]Conquering the CNN Over-Parameterization Dilemma: A Volterra Filtering Approach for Action Recognition
    Siddharth Roheda, Hamid Krim
    http://arxiv.org/abs/1910.09616v1

    • [cs.CV]Deep Set-to-Set Matching and Learning
    Yuki Saito, Takuma Nakamura, Hirotaka Hachiya, Kenji Fukumizu
    http://arxiv.org/abs/1910.09972v1

    • [cs.CV]Drivers Drowsiness Detection using Condition-Adaptive Representation Learning Framework
    Jongmin Yu, Sangwoo Park, Sangwook Lee, Moongu Jeon
    http://arxiv.org/abs/1910.09722v1

    • [cs.CV]Gaze360: Physically Unconstrained Gaze Estimation in the Wild
    Petr Kellnhofer, Adria Recasens, Simon Stent, Wojciech Matusik, Antonio Torralba
    http://arxiv.org/abs/1910.10088v1

    • [cs.CV]Hetero-Center Loss for Cross-Modality Person Re-Identification
    Yuanxin Zhu, Zhao Yang, Li Wang, Sai Zhao, Xiao Hu, Dapeng Tao
    http://arxiv.org/abs/1910.09830v1

    • [cs.CV]Human Action Recognition in Drone Videos using a Few Aerial Training Examples
    Waqas Sultani, Mubarak Shah
    http://arxiv.org/abs/1910.10027v1

    • [cs.CV]J Regularization Improves Imbalanced Multiclass Segmentation
    Fidel A. Guerrero Peña, Pedro D. Marrero Fernandez, Paul T. Tarr, Tsang Ing Ren, Elliot M. Meyerowitz, Alexandre Cunha
    http://arxiv.org/abs/1910.09783v1

    • [cs.CV]Notable Site Recognition on Mobile Devices using Deep Learning with Crowd-sourced Imagery
    Jimin Tan, Anastasios Noulas, Diego Sáez, Rossano Schifanella
    http://arxiv.org/abs/1910.09705v1

    • [cs.CV]Predictive Coding Networks Meet Action Recognition
    Xia Huang, Hossein Mousavi, Gemma Roig
    http://arxiv.org/abs/1910.10056v1

    • [cs.CV]Self-Correction for Human Parsing
    Peike Li, Yunqiu Xu, Yunchao Wei, Yi Yang
    http://arxiv.org/abs/1910.09777v1

    • [cs.CV]Structure Matters: Towards Generating Transferable Adversarial Images
    Dan Peng, Zizhan Zheng, Linhao Luo, Xiaofeng Zhang
    http://arxiv.org/abs/1910.09821v1

    • [cs.CV]The SWAX Benchmark: Attacking Biometric Systems with Wax Figures
    Rafael Henrique Vareto, Araceli Marcia Sandanha, William Robson Schwartz
    http://arxiv.org/abs/1910.09642v1

    • [cs.CV]Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
    Kaiyang Zhou, Tao Xiang
    http://arxiv.org/abs/1910.10093v1

    • [cs.CV]Towards Automatic Annotation for Semantic Segmentation in Drone Videos
    Alina Marcu, Dragos Costea, Vlad Licaret, Marius Leordeanu
    http://arxiv.org/abs/1910.10026v1

    • [cs.CV]Unsupervised particle sorting for high-resolution single-particle cryo-EM
    Ye Zhou, Amit Moscovich, Tamir Bendory, Alberto Bartesaghi
    http://arxiv.org/abs/1910.10051v1

    • [cs.CV]Vehicle detection and counting from VHR satellite images: efforts and open issues
    Alice Froidevaux, Andréa Julier, Agustin Lifschitz, Minh-Tan Pham, Romain Dambreville, Sébastien Lefèvre, Pierre Lassalle
    http://arxiv.org/abs/1910.10017v1

    • [cs.CV]Weakly-Supervised Completion Moment Detection using Temporal Attention
    Farnoosh Heidarivincheh, Majid Mirmehdi, Dima Damen
    http://arxiv.org/abs/1910.09920v1

    • [cs.CV]WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning
    Mohamed R. Ibrahim, James Haworth, Tao Cheng
    http://arxiv.org/abs/1910.09910v1

    • [cs.CY]Artificial Intelligence and the Future of Psychiatry: Qualitative Findings from a Global Physician Survey
    Charlotte Blease, Cosima Locher, Marisa Leon-Carlyle, P. Murali Doraiswamy
    http://arxiv.org/abs/1910.09956v1

    • [cs.CY]Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community
    Kumar Bhargav Srinivasan, Cristian Danescu-Niculescu-Mizil, Lillian Lee, Chenhao Tan
    http://arxiv.org/abs/1910.09563v1

    • [cs.CY]Quantifying the Carbon Emissions of Machine Learning
    Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres
    http://arxiv.org/abs/1910.09700v1

    • [cs.DC]Distributed Voting in Beep Model
    Benyamin Ghojogh, Saber Salehkaleybar
    http://arxiv.org/abs/1910.09882v1

    • [cs.DC]Mitigating the Performance-Efficiency Tradeoff in Resilient Memory Disaggregation
    Youngmoon Lee, Hassan Al Maruf, Mosharaf Chowdhury, Kang G. Shin
    http://arxiv.org/abs/1910.09727v1

    • [cs.DC]Node-Aware Improvements to Allreduce
    Amanda Bienz, Luke N. Olson, William D. Gropp
    http://arxiv.org/abs/1910.09650v1

    • [cs.DC]On Fairness in Committee-based Blockchains
    Yackolley Amoussou-Guenou, Antonella del Pozzo, Maria Potop-Butucaru, Sara Tucci-Piergiovanni
    http://arxiv.org/abs/1910.09786v1

    • [cs.DC]Performance Evaluation of Advanced Features in CUDA Unified Memory
    Steven W. D. Chien, Ivy B. Peng, Stefano Markidis
    http://arxiv.org/abs/1910.09598v1

    • [cs.DC]XPC: Fast and Reliable Synchronous Transmission Protocols for 2-Phase Commit and 3-Phase Commit
    Alberto Spina, Michael Breza, Naranker Dulay, Julie McCann
    http://arxiv.org/abs/1910.09941v1

    • [cs.ET]Kernel computations from large-scale random features obtained by Optical Processing Units
    Ruben Ohana, Jonas Wacker, Jonathan Dong, Sébastien Marmin, Florent Krzakala, Maurizio Filippone, Laurent Daudet
    http://arxiv.org/abs/1910.09880v1

    • [cs.HC]On Automating Conversations
    Huang, Ting-Hao ‘Kenneth’
    http://arxiv.org/abs/1910.09621v1

    • [cs.IR]From Personalization to Privatization: Meta Matrix Factorization for Private Rating Predictions
    Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, Xiuzhen Cheng
    http://arxiv.org/abs/1910.10086v1

    • [cs.IR]Markov Random Fields for Collaborative Filtering
    Harald Steck
    http://arxiv.org/abs/1910.09645v1

    • [cs.IR]One-Shot Template Matching for Automatic Document Data Capture
    Pranjal Dhakal, Manish Munikar, Bikram Dahal
    http://arxiv.org/abs/1910.10037v1

    • [cs.IR]Self-Attentive Document Interaction Networks for Permutation Equivariant Ranking
    Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, Marc Najork
    http://arxiv.org/abs/1910.09676v1

    • [cs.IT]An enhanced decoding algorithm for coded compressed sensing
    Vamsi K. Amalladinne, Jean-Francois Chamberland, Krishna R. Narayanan
    http://arxiv.org/abs/1910.09704v1

    • [cs.IT]Convolutional Neural Networks for Space-Time Block Coding Recognition
    Wenjun Yan, Qing Ling, Limin Zhang
    http://arxiv.org/abs/1910.09952v1

    • [cs.IT]Energy-Efficient Resource Allocation for Secure NOMA-Enabled Mobile Edge Computing Networks
    Wei Wu, Fuhui Zhou, Rose Qingyang Hu, Baoyun Wang
    http://arxiv.org/abs/1910.09886v1

    • [cs.IT]On the Beneficial Role of a Finite Number of Scatterers for Wireless Physical Layer Security
    Pablo Ramírez-Espinosa, R. José Sánchez-Alarcón, F. Javier López-Martínez
    http://arxiv.org/abs/1910.09856v1

    • [cs.IT]Parallel Stochastic Optimization Framework for Large-Scale Non-Convex Stochastic Problems
    Naeimeh Omidvar, An Liu, Vincent Lau, Danny H. K. Tsang, Mohammad Reza Pakravan
    http://arxiv.org/abs/1910.09901v1

    • [cs.IT]Zero-Crossing Precoding With Maximum Distance to the Decision Threshold for Channels With 1-Bit Quantization and Oversampling
    Diana M. V. Melo, Lukas T. N. Landau, Rodrigo C. de Lamare
    http://arxiv.org/abs/1910.10031v1

    • [cs.LG]A Prototypical Triplet Loss for Cover Detection
    Guillaume Doras, Geoffroy Peeters
    http://arxiv.org/abs/1910.09862v1

    • [cs.LG]A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data
    Lorenzo Gigoni, Alessandro Betti, Mauro Tucci, Emanuele Crisostomi
    http://arxiv.org/abs/1910.09808v1

    • [cs.LG]A deep active learning system for species identification and counting in camera trap images
    Mohammad Sadegh Norouzzadeh, Dan Morris, Sara Beery, Neel Joshi, Nebojsa Jojic, Jeff Clune
    http://arxiv.org/abs/1910.09716v1

    • [cs.LG]Abnormal Client Behavior Detection in Federated Learning
    Suyi Li, Yong Cheng, Yang Liu, Wei Wang, Tianjian Chen
    http://arxiv.org/abs/1910.09933v1

    • [cs.LG]Adversarial Example Detection by Classification for Deep Speech Recognition
    Saeid Samizade, Zheng-Hua Tan, Chao Shen, Xiaohong Guan
    http://arxiv.org/abs/1910.10013v1

    • [cs.LG]An Efficient EKF Based Algorithm For LSTM-Based Online Learning
    N. Mert Vural, Suleyman S. Kozat
    http://arxiv.org/abs/1910.09857v1

    • [cs.LG]Bridging the Gap Between $f$-GANs and Wasserstein GANs
    Jiaming Song, Stefano Ermon
    http://arxiv.org/abs/1910.09779v1

    • [cs.LG]Causal bootstrapping
    Max A. Little, Reham Badawy
    http://arxiv.org/abs/1910.09648v1

    • [cs.LG]Class Mean Vectors, Self Monitoring and Self Learning for Neural Classifiers
    Eugene Wong
    http://arxiv.org/abs/1910.10122v1

    • [cs.LG]Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
    Uchenna Akujuobi, Han Yufei, Qiannan Zhang, Xiangliang Zhang
    http://arxiv.org/abs/1910.09706v1

    • [cs.LG]Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
    Zhe Dong, Bryan A. Seybold, Kevin P. Murphy, Hung H. Bui
    http://arxiv.org/abs/1910.09588v1

    • [cs.LG]Composite Neural Network: Theory and Application to PM2.5 Prediction
    Ming-Chuan Yang, Meng Chang Chen
    http://arxiv.org/abs/1910.09739v1

    • [cs.LG]Convolutional Prototype Learning for Zero-Shot Recognition
    Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu, ShuaiZheng, YaoZhao, JianCheng
    http://arxiv.org/abs/1910.09728v1

    • [cs.LG]Detecting Extrapolation with Local Ensembles
    David Madras, James Atwood, Alex D’Amour
    http://arxiv.org/abs/1910.09573v1

    • [cs.LG]Edge Dithering for Robust Adaptive Graph Convolutional Networks
    Vassilis N. Ioannidis, Georgios B. Giannakis
    http://arxiv.org/abs/1910.09590v1

    • [cs.LG]Establishing an Evaluation Metric to Quantify Climate Change Image Realism
    Sharon Zhou, Alexandra Luccioni, Gautier Cosne, Michael S. Bernstein, Yoshua Bengio
    http://arxiv.org/abs/1910.10143v1

    • [cs.LG]Explicitly Bayesian Regularizations in Deep Learning
    Xinjie Lan, Kenneth E. Barner
    http://arxiv.org/abs/1910.09732v1

    • [cs.LG]Face representation by deep learning: a linear encoding in a parameter space?
    Qiulei Dong, Jiayin Sun, Zhanyi Hu
    http://arxiv.org/abs/1910.09768v1

    • [cs.LG]Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
    Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
    http://arxiv.org/abs/1910.09594v1

    • [cs.LG]GANspection
    Hammad A. Ayyubi
    http://arxiv.org/abs/1910.09638v1

    • [cs.LG]GraphSAC: Detecting anomalies in large-scale graphs
    Vassilis N. Ioannidis, Dimitris Berberidis, Georgios B. Giannakis
    http://arxiv.org/abs/1910.09589v1

    • [cs.LG]IPO: Interior-point Policy Optimization under Constraints
    Yongshuai Liu, Jiaxin Ding, Xin Liu
    http://arxiv.org/abs/1910.09615v1

    • [cs.LG]Improving Siamese Networks for One Shot Learning using Kernel Based Activation functions
    Shruti Jadon, Aditya Acrot Srinivasan
    http://arxiv.org/abs/1910.09798v1

    • [cs.LG]Improving singing voice separation with the Wave-U-Net using Minimum Hyperspherical Energy
    Joaquin Perez-Lapillo, Oleksandr Galkin, Tillman Weyde
    http://arxiv.org/abs/1910.10071v1

    • [cs.LG]Learning to Make Generalizable and Diverse Predictions for Retrosynthesis
    Benson Chen, Tianxiao Shen, Tommi S. Jaakkola, Regina Barzilay
    http://arxiv.org/abs/1910.09688v1

    • [cs.LG]Mask Combination of Multi-layer Graphs for Global Structure Inference
    Eda Bayram, Dorina Thanou, Elif Vural, Pascal Frossard
    http://arxiv.org/abs/1910.10114v1

    • [cs.LG]Multiphase flow prediction with deep neural networks
    Gege Wen, Meng Tang, Sally M. Benson
    http://arxiv.org/abs/1910.09657v1

    • [cs.LG]Multiple Sample Clustering
    Xiang Wang, Tie Liu
    http://arxiv.org/abs/1910.09731v1

    • [cs.LG]Neural Network Training with Approximate Logarithmic Computations
    Arnab Sanyal, Peter A. Beerel, Keith M. Chugg
    http://arxiv.org/abs/1910.09876v1

    • [cs.LG]Non-Gaussianity of Stochastic Gradient Noise
    Abhishek Panigrahi, Raghav Somani, Navin Goyal, Praneeth Netrapalli
    http://arxiv.org/abs/1910.09626v1

    • [cs.LG]On Predictive Information Sub-optimality of RNNs
    Zhe Dong, Deniz Oktay, Ben Poole, Alexander A. Alemi
    http://arxiv.org/abs/1910.09578v1

    • [cs.LG]Orthogonal variance decomposition based feature selection
    Firuz Kamalov
    http://arxiv.org/abs/1910.09851v1

    • [cs.LG]Robust Training with Ensemble Consensus
    Jisoo Lee, Sae-Young Chung
    http://arxiv.org/abs/1910.09792v1

    • [cs.LG]Self-supervised pre-training with acoustic configurations for replay spoofing detection
    Hye-jin Shim, Hee-Soo Heo, Jee-weon Jung, Ha-Jin Yu
    http://arxiv.org/abs/1910.09778v1

    • [cs.LG]Signal Combination for Language Identification
    Shengye Wang, Li Wan, Yang Yu, Ignacio Lopez Moreno
    http://arxiv.org/abs/1910.09687v1

    • [cs.LG]Single Versus Union: Non-parallel Support Vector Machine Frameworks
    Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Ling-Wei Huang, Naihua Xiu, Nai-Yang Deng
    http://arxiv.org/abs/1910.09734v1

    • [cs.LG]Smoothness-Adaptive Stochastic Bandits
    Yonatan Gur, Ahmadreza Momeni, Stefan Wager
    http://arxiv.org/abs/1910.09714v1

    • [cs.LG]Spatiotemporal Emotion Recognition using Deep CNN Based on EEG during Music Listening
    Panayu Keelawat, Nattapong Thammasan, Masayuki Numao, Boonserm Kijsirikul
    http://arxiv.org/abs/1910.09719v1

    • [cs.LG]Stability of Graph Neural Networks to Relative Perturbations
    Fernando Gama, Joan Bruna, Alejandro Ribeiro
    http://arxiv.org/abs/1910.09655v1

    • [cs.LG]Stochastic Feedforward Neural Networks: Universal Approximation
    Thomas Merkh, Guido Montúfar
    http://arxiv.org/abs/1910.09763v1

    • [cs.LG]Toward Automated Website Classification by Deep Learning
    Fabrizio De Fausti, Francesco Pugliese, Diego Zardetto
    http://arxiv.org/abs/1910.09991v1

    • [cs.LG]Towards best practice in explaining neural network decisions with LRP
    Maximilian Kohlbrenner, Alexander Bauer, Shinichi Nakajima, Alexander Binder, Wojciech Samek, Sebastian Lapuschkin
    http://arxiv.org/abs/1910.09840v1

    • [cs.LG]Two-Step Sound Source Separation: Training on Learned Latent Targets
    Efthymios Tzinis, Shrikant Venkataramani, Zhepei Wang, Cem Subakan, Paris Smaragdis
    http://arxiv.org/abs/1910.09804v1

    • [cs.LG]Universal flow approximation with deep residual networks
    Johannes Müller
    http://arxiv.org/abs/1910.09599v1

    • [cs.LG]Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
    Hamed Sarvari, Carlotta Domeniconi, Bardh Prenkaj, Giovanni Stilo
    http://arxiv.org/abs/1910.09754v1

    • [cs.LG]Vanishing Nodes: Another Phenomenon That Makes Training Deep Neural Networks Difficult
    Wen-Yu Chang, Tsung-Nan Lin
    http://arxiv.org/abs/1910.09745v1

    • [cs.LG]Weakly Supervised Disentanglement with Guarantees
    Rui Shu, Yining Chen, Abhishek Kumar, Stefano Ermon, Ben Poole
    http://arxiv.org/abs/1910.09772v1

    • [cs.LG]Who wants accurate models? Arguing for a different metrics to take classification models seriously
    Federico Cabitza, Andrea Campagner
    http://arxiv.org/abs/1910.09246v2

    • [cs.LG]You May Not Need Order in Time Series Forecasting
    Yunkai Zhang, Qiao Jiang, Shurui Li, Xiaoyong Jin, Xueying Ma, Xifeng Yan
    http://arxiv.org/abs/1910.09620v1

    • [cs.MA]Distributed interference cancellation in multi-agent scenarios
    Mahdi Shamsi, Alireza Moslemi Haghighi, Farokh Marvasti
    http://arxiv.org/abs/1910.10109v1

    • [cs.NE]Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning
    Zhenzhong Wang, Min Jiang, Xing Gao, Liang Feng, Weizhen Hu, Kay Chen Tan
    http://arxiv.org/abs/1910.08753v2

    • [cs.NE]Improving the Gating Mechanism of Recurrent Neural Networks
    Albert Gu, Caglar Gulcehre, Tom Le Paine, Matt Hoffman, Razvan Pascanu
    http://arxiv.org/abs/1910.09890v1

    • [cs.NI]Scalable and Accurate Modeling of the Millimeter Wave Channel
    Paolo Testolina, Mattia Lecci, Michele Polese, Marco Giordani, Michele Zorzi
    http://arxiv.org/abs/1910.09912v1

    • [cs.RO]ALGAMES: A Fast Solver for Constrained Dynamic Games
    Simon Le Cleac’h, Mac Schwager, Zachary Manchester
    http://arxiv.org/abs/1910.09713v1

    • [cs.RO]Combining Benefits from Trajectory Optimization and Deep Reinforcement Learning
    Guillaume Bellegarda, Katie Byl
    http://arxiv.org/abs/1910.09667v1

    • [cs.RO]Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments
    Siddharth Patki, Ethan Fahnestock, Thomas M. Howard, Matthew R. Walter
    http://arxiv.org/abs/1910.10034v1

    • [cs.RO]Learning Resilient Behaviors for Navigation Under Uncertainty Environments
    Tingxiang Fan, Pinxin Long, Wenxi Liu, Jia Pan, Ruigang Yang, Dinesh Manocha
    http://arxiv.org/abs/1910.09998v1

    • [cs.RO]Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated Flight
    Valts Blukis, Yannick Terme, Eyvind Niklasson, Ross A. Knepper, Yoav Artzi
    http://arxiv.org/abs/1910.09664v1

    • [cs.RO]Multiple criteria decision-making for lane-change model
    Ao Li, Liting Sun, Wei Zhan, Masayoshi Tomizuka
    http://arxiv.org/abs/1910.10142v1

    • [cs.RO]Preintegrated Velocity Bias Estimation to Overcome Contact Nonlinearities in Legged Robot Odometry
    David Wisth, Marco Camurri, Maurice Fallon
    http://arxiv.org/abs/1910.09875v1

    • [cs.RO]Real-Time Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration
    Karin de Langis, Junaed Sattar
    http://arxiv.org/abs/1910.09636v1

    • [cs.SD]Cross-Representation Transferability of Adversarial Perturbations: From Spectrograms to Audio Waveforms
    Karl M. Koerich, Mohammad Esmailpour, Sajjad Abdoli, Alceu S. Britto Jr., Alessandro L. Koerich
    http://arxiv.org/abs/1910.10106v1

    • [cs.SD]Cross-task pre-training for acoustic scene classification
    Ruixiong Zhang, Wei Zou, Xiangang Li
    http://arxiv.org/abs/1910.09935v1

    • [cs.SD]Deep speech inpainting of time-frequency masks
    Mikolaj Kegler, Pierre Beckmann, Milos Cernak
    http://arxiv.org/abs/1910.09058v2

    • [cs.SD]Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer
    Merlijn Blaauw, Jordi Bonada
    http://arxiv.org/abs/1910.09989v1

    • [cs.SE]Non-cognitive abilities of exceptional software engineers: a Delphi study
    Wouter Groeneveld, Hans Jacobs, Joost Vennekens, Kris Aerts
    http://arxiv.org/abs/1910.09861v1

    • [cs.SE]Software Engineering Education Beyond the Technical: A Systematic Literature Review
    Wouter Groeneveld, Joost Vennekens, Kris Aerts
    http://arxiv.org/abs/1910.09865v1

    • [cs.SI]A Theory of Extended Working Memory and its Role in Online Conversation Dynamics
    Chathika Gunaratne, Nisha Baral, William Rand, Ivan Garibay, Chathura Jayalath, Chathurani Senevirathna
    http://arxiv.org/abs/1910.09686v1

    • [cs.SI]Hypergraph clustering with categorical edge labels
    Ilya Amburg, Nate Veldt, Austin R. Benson
    http://arxiv.org/abs/1910.09943v1

    • [cs.SI]Multiscale Evolutionary Perturbation Attack on Community Detection
    Jinyin Chen, Yixian Chen, Lihong Chen, Minghao Zhao, Qi Xuan
    http://arxiv.org/abs/1910.09741v1

    • [cs.SI]Simplification of networks via conservation of path diversity and minimisation of the search information
    Hengda Yin, Richard. G. Clegg, Raul. J. Mondragon
    http://arxiv.org/abs/1910.09896v1

    • [econ.GN]Relative Net Utility and the Saint Petersburg Paradox
    Daniel Muller, Tshilidzi Marwala
    http://arxiv.org/abs/1910.09544v1

    • [eess.AS]Discriminative Neural Clustering for Speaker Diarisation
    Qiujia Li, Florian L. Kreyssig, Chao Zhang, Philip C. Woodland
    http://arxiv.org/abs/1910.09703v1

    • [eess.AS]Modeling plate and spring reverberation using a DSP-informed deep neural network
    Marco A. Martínez Ramírez, Emmanouil Benetos, Joshua D. Reiss
    http://arxiv.org/abs/1910.10105v1

    • [eess.AS]Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
    Flavio Martinelli, Giorgia Dellaferrera, Pablo Mainar, Milos Cernak
    http://arxiv.org/abs/1910.09993v1

    • [eess.IV]A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs
    Jiwei Liu, Junyu Liu, Yang Liu, Rui Yang, Dongjun Lv, Zhengting Cai, Jingjing Cui
    http://arxiv.org/abs/1910.09900v1

    • [eess.IV]Fixed Pattern Noise Reduction for Infrared Images Based on Cascade Residual Attention CNN
    Juntao Guan, Rui Lai, Ai Xiong, Zesheng Liu, Lin Gu
    http://arxiv.org/abs/1910.09858v1

    • [eess.IV]Image processing in DNA
    Chao Pan, S. M. Hossein Tabatabaei Yazdi, S Kasra Tabatabaei, Alvaro G. Hernandez, Charles Schroeder, Olgica Milenkovic
    http://arxiv.org/abs/1910.10095v1

    • [eess.IV]Image recovery from rotational and translational invariants
    Nicholas F. Marshall, Ti-Yen Lan, Tamir Bendory, Amit Singer
    http://arxiv.org/abs/1910.10006v1

    • [eess.IV]Learning a Generic Adaptive Wavelet Shrinkage Function for Denoising
    Tobias Alt, Joachim Weickert
    http://arxiv.org/abs/1910.09234v2

    • [eess.IV]Modeling Disease Progression In Retinal OCTs With Longitudinal Self-Supervised Learning
    Antoine Rivail, Ursula Schmidt-Erfurth, Wolf-Dieter Vogel, Sebastian M. Waldstein, Sophie Riedl, Christoph Grechenig, Zhichao Wu, Hrvoje Bogunović
    http://arxiv.org/abs/1910.09420v2

    • [eess.IV]Penalizing small errors using an Adaptive Logarithmic Loss
    Chaitanya Kaul, Nick Pears, Suresh Manandhar
    http://arxiv.org/abs/1910.09717v1

    • [eess.IV]Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
    Shahab Aslani, Vittorio Murino, Michael Dayan, Roger Tam, Diego Sona, Ghassan Hamarneh
    http://arxiv.org/abs/1910.10035v1

    • [eess.IV]Trident Segmentation CNN: A Spatiotemporal Transformation CNN for Punctate White Matter Lesions Segmentation in Preterm Neonates
    Yalong Liu, Jie Li, Miaomiao Wang, Zhicheng Jiao, Jian Yang, Xianjun Li
    http://arxiv.org/abs/1910.09773v1

    • [eess.SP]A Complexity Efficient DMT-Optimal Tree Pruning Based Sphere Decoding
    Mohammad Neinavaie, Mostafa Derakhtian, Negar Daryanavardan, Sergiy Vorobyov
    http://arxiv.org/abs/1910.09177v1

    • [eess.SP]A Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification
    Farid Kenarangi, Inna Partin-Vaisband
    http://arxiv.org/abs/1910.09597v1

    • [eess.SP]Learning to Communicate in a Noisy Environment
    Anant Sahai, Joshua Sanz, Vignesh Subramanian, Caryn Tran, Kailas Vodrahalli
    http://arxiv.org/abs/1910.09630v1

    • [eess.SP]Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels
    Sangwoo Park, Osvaldo Simeone, Joonhyuk Kang
    http://arxiv.org/abs/1910.09945v1

    • [eess.SP]New RLL Code with Improved Error Performance for Visible Light Communication
    Vitalio Alfonso Reguera
    http://arxiv.org/abs/1910.10079v1

    • [eess.SP]Prediction of Reaction Time and Vigilance Variability from Spatiospectral Features of Resting-State EEG in a Long Sustained Attention Task
    Mastaneh Torkamani-Azar, Sumeyra Demir Kanik, Serap Aydin, Mujdat Cetin
    http://arxiv.org/abs/1910.10076v1

    • [eess.SP]Secrecy Analyses of a Full-Duplex MIMOME Network
    Reza Sohrabi, Qiping Zhu, Yingbo Hua
    http://arxiv.org/abs/1910.09647v1

    • [math.CA]Finding duality and Riesz bases of exponentials on multi-tiles
    Christina Frederick, Kasso Okoudjou
    http://arxiv.org/abs/1910.09257v2

    • [math.CO]A King in every two consecutive tournaments
    Yehuda Afek, Eli Gafni, Nati Linial
    http://arxiv.org/abs/1910.09684v1

    • [math.CO]Sequential metric dimension for random graphs
    Gergely Odor, Patrick Thiran
    http://arxiv.org/abs/1910.10116v1

    • [math.OC]Faster Stochastic Algorithms via History-Gradient Aided Batch Size Adaptation
    Kaiyi Ji, Zhe Wang, Yi Zhou, Yingbin Liang
    http://arxiv.org/abs/1910.09670v1

    • [math.OC]Geometry of Graph Partitions via Optimal Transport
    Tara Abrishami, Nestor Guillen, Parker Rule, Zachary Schutzman, Justin Solomon, Thomas Weighill, Si Wu
    http://arxiv.org/abs/1910.09618v1

    • [math.OC]Learning Adaptive Regularization for Image Labeling Using Geometric Assignment
    Ruben Hühnerbein, Fabrizio Savarino, Stefania Petra, Christoph Schnörr
    http://arxiv.org/abs/1910.09976v1

    • [math.OC]The Practicality of Stochastic Optimization in Imaging Inverse Problems
    Junqi Tang, Karen Egiazarian, Mohammad Golbabaee, Mike Davies
    http://arxiv.org/abs/1910.10100v1

    • [math.ST]Berry-Esseen bounds for Chernoff-type non-standard asymptotics in isotonic regression
    Qiyang Han, Kengo Kato
    http://arxiv.org/abs/1910.09662v1

    • [math.ST]Confidence intervals centred on bootstrap smoothed estimators: an impossibility result
    Paul Kabaila, Christeen Wijethunga
    http://arxiv.org/abs/1910.09695v1

    • [math.ST]Optimization Hierarchy for Fair Statistical Decision Problems
    Anil Aswani, Matt Olfat
    http://arxiv.org/abs/1910.08520v2

    • [physics.comp-ph]Coercing Machine Learning to Output Physically Accurate Results
    Zhenglin Geng, Dan Johnson, Ronald Fedkiw
    http://arxiv.org/abs/1910.09671v1

    • [physics.ed-ph]Easy Java/JavaScript Simulations as a tool for Learning Analytics
    Francisco Esquembre, Félix J. García Clemente, Rafael Chicón, Lawrence Wee, Leong Tze Kwang, Darren Tan
    http://arxiv.org/abs/1910.09156v1

    • [physics.flu-dyn]Optimal sensing for fish school identification
    Pascal Weber, Georgios Arampatzis, Guido Novati, Siddhartha Verma, Costas Papadimitriou, Petros Koumoutsakos
    http://arxiv.org/abs/1910.09937v1

    • [q-bio.GN]Is graph biased feature selection of genes better than random?
    Mohammad Hashir, Paul Bertin, Martin Weiss, Vincent Frappier, Theodore Perkins, Geneviève Boucher, Joseph Paul Cohen
    http://arxiv.org/abs/1910.09600v1

    • [q-bio.QM]Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
    Shawn Tan, Guillaume Androz, Ahmad Chamseddine, Pierre Fecteau, Aaron Courville, Yoshua Bengio, Joseph Paul Cohen
    http://arxiv.org/abs/1910.09570v1

    • [q-bio.QM]ProDyn0: Inferring calponin homology domain stretching behavior using graph neural networks
    Ali Madani, Cyna Shirazinejad, Jia Rui Ong, Hengameh Shams, Mohammad Mofrad
    http://arxiv.org/abs/1910.09738v1

    • [quant-ph]A Domain-agnostic, Noise-resistant Evolutionary Variational Quantum Eigensolver for Hardware-efficient Optimization in the Hilbert Space
    Arthur G. Rattew, Shaohan Hu, Marco Pistoia, Richard Chen, Steve Wood
    http://arxiv.org/abs/1910.09694v1

    • [stat.AP]Associations between park features, park satisfaction and park use in a multi-ethnic deprived urban area
    Hannah Roberts, Ian Kellar, Mark Conner, Christopher Gidlow, Brian Kelly, Mark Nieuwenhuijsen, Rosemary McEachan
    http://arxiv.org/abs/1910.09922v1

    • [stat.AP]Ranking, and other Properties, of Elite Swimmers using Extreme Value Theory
    Harry Spearing, Jonathan Tawn, David Irons, Tim Paulden, Grace Bennett
    http://arxiv.org/abs/1910.10070v1

    • [stat.ME]Hypothesis Testing in High-Dimensional Instrumental Variables Regression with an Application to Genomics Data
    Jiarui Lu, Hongzhe Li
    http://arxiv.org/abs/1910.09628v1

    • [stat.ME]Integrated Quantile RAnk Test (iQRAT) for gene-level associations in sequencing studies
    Tianying Wang, Iuliana Ionita-Laza, Ying Wei
    http://arxiv.org/abs/1910.10102v1

    • [stat.ME]Linear Mixed Models for Comparing Dynamic Treatment Regimens on a Longitudinal Outcome in Sequentially Randomized Trials
    Brook Luers, Min Qian, Inbal Nahum-Shani, Connie Kasari, Daniel Almirall
    http://arxiv.org/abs/1910.10078v1

    • [stat.ME]Principal Component Analysis: A Generalized Gini Approach
    Charpentier, Arthur, Mussard, Stephane, Tea Ouraga
    http://arxiv.org/abs/1910.10133v1

    • [stat.ME]Soft Tensor Regression
    Georgia Papadogeorgou, Zhengwu Zhang, David B. Dunson
    http://arxiv.org/abs/1910.09699v1

    • [stat.ME]Sparse Networks with Core-Periphery Structure
    Cian Naik, François Caron, Judith Rousseau
    http://arxiv.org/abs/1910.09679v1

    • [stat.ML]Compressive Learning for Semi-Parametric Models
    Michael P. Sheehan, Antoine Gonon, Mike E. Davies
    http://arxiv.org/abs/1910.10024v1

    • [stat.ML]Continual Learning for Infinite Hierarchical Change-Point Detection
    Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
    http://arxiv.org/abs/1910.10087v1

    • [stat.ML]Direct Estimation of Differential Functional Graphical Models
    Boxin Zhao, Y. Samuel Wang, Mladen Kolar
    http://arxiv.org/abs/1910.09701v1

    • [stat.ML]Embedded Bayesian Network Classifiers
    David Heckerman, Chris Meek
    http://arxiv.org/abs/1910.09715v1

    • [stat.ML]Kernelized Wasserstein Natural Gradient
    Michael Arbel, Arthur Gretton, Wuchen Li, Guido Montufar
    http://arxiv.org/abs/1910.09652v1

    • [stat.ML]Orthogonal Nonnegative Tucker Decomposition
    Junjun Pan, Michael K. Ng, Ye Liu, Xiongjun Zhang, Hong Yan
    http://arxiv.org/abs/1910.09979v1

    • [stat.ML]Uncertainty Quantification with Generative Models
    Vanessa Böhm, François Lanusse, Uroš Seljak
    http://arxiv.org/abs/1910.10046v1