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