astro-ph.IM - 仪器仪表和天体物理学方法

    cond-mat.mes-hall - 尺度和物理纳米 cs.AI - 人工智能 cs.CG - 计算几何学 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.DS - 数据结构与算法 cs.IT - 信息论 cs.LG - 自动学习 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.SI - 社交网络与信息网络 eess.AS - 语音处理 eess.IV - 图像与视频处理 math.NA - 数值分析 math.OC - 优化与控制 math.ST - 统计理论 physics.med-ph - 医学物理学 q-bio.MN - 分子网络 q-bio.NC - 神经元与认知 q-bio.QM - 定量方法 quant-ph - 量子物理 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习 stat.OT - 其他统计学

    • [astro-ph.IM]Development and Testing of an Engineering Model for an Asteroid Hopping Robot
    • [cond-mat.mes-hall]Improvement in Retention Time of Capacitorless DRAM with Access Transistor
    • [cs.AI]Detecting AI Trojans Using Meta Neural Analysis
    • [cs.AI]Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning
    • [cs.AI]Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
    • [cs.AI]Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
    • [cs.AI]Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents
    • [cs.CG]On geodesic triangles with right angles in a dually flat space
    • [cs.CL]Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
    • [cs.CL]Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction
    • [cs.CL]BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories
    • [cs.CL]Do People Prefer “Natural” code?
    • [cs.CL]Executing Instructions in Situated Collaborative Interactions
    • [cs.CL]Is Multilingual BERT Fluent in Language Generation?
    • [cs.CL]Knowledge Distillation from Internal Representations
    • [cs.CL]Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
    • [cs.CL]Novel Applications of Factored Neural Machine Translation
    • [cs.CL]The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation
    • [cs.CL]Towards De-identification of Legal Texts
    • [cs.CL]Transformers: State-of-the-art Natural Language Processing
    • [cs.CL]Word Embedding Visualization Via Dictionary Learning
    • [cs.CR]Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service
    • [cs.CV]3D Manhattan Room Layout Reconstruction from a Single 360 Image
    • [cs.CV]A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification
    • [cs.CV]BIAS: Transparent reporting of biomedical image analysis challenges
    • [cs.CV]Bias-Resilient Neural Network
    • [cs.CV]Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion
    • [cs.CV]Detection and Identification of Objects and Humans in Thermal Images
    • [cs.CV]ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
    • [cs.CV]Exploring Hate Speech Detection in Multimodal Publications
    • [cs.CV]Fast Panoptic Segmentation Network
    • [cs.CV]FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
    • [cs.CV]Gradient Information Guided Deraining with A Novel Network and Adversarial Training
    • [cs.CV]Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
    • [cs.CV]Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation
    • [cs.CV]Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
    • [cs.CV]Learning to Generalize One Sample at a Time with Self-Supervision
    • [cs.CV]MIDV-2019: Challenges of the modern mobile-based document OCR
    • [cs.CV]Meta-Transfer Learning through Hard Tasks
    • [cs.CV]MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge
    • [cs.CV]Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification
    • [cs.CV]NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks
    • [cs.CV]Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives
    • [cs.CV]Patch Refinement — Localized 3D Object Detection
    • [cs.CV]Person re-identification based on Res2Net network
    • [cs.CV]Prose for a Painting
    • [cs.CV]REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
    • [cs.CV]SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
    • [cs.CV]Semantic Understanding of Foggy Scenes with Purely Synthetic Data
    • [cs.CV]Semantic-aware Image Deblurring
    • [cs.CV]Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
    • [cs.CV]Unaligned Image-to-Sequence Transformation with Loop Consistency
    • [cs.CV]Vehicle Re-identification with Viewpoint-aware Metric Learning
    • [cs.CV]View Confusion Feature Learning for Person Re-identification
    • [cs.DC]Fog Computing as Privacy Enabler
    • [cs.DC]Performance Impact of Memory Channels on Sparse and Irregular Algorithms
    • [cs.DL]The role of mainstreamness and interdisciplinarity for the relevance of scientific papers
    • [cs.DS]Span-core Decomposition for Temporal Networks: Algorithms and Applications
    • [cs.IT]A Data Concealing Technique with Random Noise Disturbance and A Restoring Technique for the Concealed Data by Stochastic Process Estimation
    • [cs.IT]A Survey on Deep-Learning based Techniques for Modeling and Estimation of MassiveMIMO Channels
    • [cs.IT]Alternate Distributed Beamforming for Decode-and-Forward Multi-Relay Systems Using Buffers
    • [cs.IT]Approaching the Finite Blocklength Capacity within 0.025dB by Short Polar Codes and CRC-Aided Hybrid Decoding
    • [cs.IT]Counterexamples on the monotonicity of delay optimal strategies for energy harvesting transmitters
    • [cs.IT]DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access
    • [cs.IT]DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation
    • [cs.IT]Entanglement-Enabled Communication
    • [cs.IT]Evaluation of Error Probability of Classification Based on the Analysis of the Bayes Code
    • [cs.IT]High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
    • [cs.IT]Overcoming DoF Limitation in Robust Beamforming: A Penalized Inequality-Constrained Approach
    • [cs.IT]Secret key agreement from correlated data, with no prior information
    • [cs.IT]Understanding the Performance of Bluetooth Mesh: Reliability, Delay and Scalability Analysis
    • [cs.LG]A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks
    • [cs.LG]Accelerating Federated Learning via Momentum Gradient Descent
    • [cs.LG]Adversarial Learning of Deepfakes in Accounting
    • [cs.LG]Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
    • [cs.LG]An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection
    • [cs.LG]AutoML using Metadata Language Embeddings
    • [cs.LG]Beyond Vector Spaces: Compact Data Representationas Differentiable Weighted Graphs
    • [cs.LG]Compatible features for Monotonic Policy Improvement
    • [cs.LG]Continual Learning Using Bayesian Neural Networks
    • [cs.LG]Ctrl-Z: Recovering from Instability in Reinforcement Learning
    • [cs.LG]DEVDAN: Deep Evolving Denoising Autoencoder
    • [cs.LG]Deep Latent Defence
    • [cs.LG]Derivative-Free & Order-Robust Optimisation
    • [cs.LG]Dissecting Deep Neural Networks
    • [cs.LG]Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
    • [cs.LG]FedMD: Heterogenous Federated Learning via Model Distillation
    • [cs.LG]How Well Do WGANs Estimate the Wasserstein Metric?
    • [cs.LG]Improving Generalization in Meta Reinforcement Learning using Learned Objectives
    • [cs.LG]Investigation on the generalization of the Sampled Policy Gradient algorithm
    • [cs.LG]Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
    • [cs.LG]Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees
    • [cs.LG]Linking emotions to behaviors through deep transfer learning
    • [cs.LG]Loss Surface Sightseeing by Multi-Point Optimization
    • [cs.LG]MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
    • [cs.LG]Model-Based Reinforcement Learning Exploiting State-Action Equivalence
    • [cs.LG]Multi-Modal Simultaneous Forecasting of Vehicle Position Sequences using Social Attention
    • [cs.LG]Multiple-objective Reinforcement Learning for Inverse Design and Identification
    • [cs.LG]Nearly Minimal Over-Parametrization of Shallow Neural Networks
    • [cs.LG]On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications
    • [cs.LG]On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets
    • [cs.LG]Policy Optimization Through Approximated Importance Sampling
    • [cs.LG]Receding Horizon Curiosity
    • [cs.LG]Sequential VAE-LSTM for Anomaly Detection on Time Series
    • [cs.LG]SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
    • [cs.LG]Social Learning in Multi Agent Multi Armed Bandits
    • [cs.LG]Supervised feature selection with orthogonal regression and feature weighting
    • [cs.LG]Text-to-Image Synthesis Based on Machine Generated Captions
    • [cs.LG]The fastest $\ell_{1,\infty}$ prox in the west
    • [cs.NE]Deep neural network for pier scour prediction
    • [cs.NE]Large Scale Global Optimization by Hybrid Evolutionary Computation
    • [cs.NI]Explaining Deep Learning-Based Networked Systems
    • [cs.NI]Hierarchical Deep Double Q-Routing
    • [cs.RO]A Brain-Inspired Compact Cognitive Mapping System
    • [cs.RO]Automated Multidisciplinary Design and Control of Hopping Robots for Exploration of Extreme Environments on the Moon and Mars
    • [cs.RO]Autonomous Multirobot Technologies for Mars Mining Base Construction and Operation
    • [cs.RO]Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
    • [cs.RO]Knowledge Induced Deep Q-Network for a Slide-to-Wall Object Grasping
    • [cs.RO]Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
    • [cs.RO]Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
    • [cs.RO]Multimodal representation models for prediction and control from partial information
    • [cs.RO]Stochastic Triangular Mesh Mapping
    • [cs.RO]Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
    • [cs.SI]A k-hop Collaborate Game Model: Adaptive Strategy to Maximize Total Revenue
    • [cs.SI]Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks
    • [cs.SI]Rejection-Based Simulation of Non-Markovian Agents on Complex Networks
    • [eess.AS]MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms
    • [eess.IV]Cribriform pattern detection in prostate histopathological images using deep learning models
    • [eess.IV]Deep Learning Accelerated Light Source Experiments
    • [eess.IV]Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists’ screening performance
    • [eess.IV]FastSurfer — A fast and accurate deep learning based neuroimaging pipeline
    • [eess.IV]Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
    • [eess.IV]Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution
    • [math.NA]Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
    • [math.OC]Bregman Proximal Framework for Deep Linear Neural Networks
    • [math.ST]Nonparametric principal subspace regression
    • [math.ST]On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise
    • [math.ST]On the feasibility of parsimonious variable selection for Hotelling’s $T^2$-test
    • [math.ST]Penalized regression via the restricted bridge estimator
    • [math.ST]Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm
    • [math.ST]Variance reduction for Markov chains with application to MCMC
    • [physics.med-ph]A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT
    • [q-bio.MN]BoolSi: a tool for distributed simulations and analysis of Boolean networks
    • [q-bio.NC]Dynamic Brain Functional Networks Guided By Anatomical Knowledge
    • [q-bio.QM]A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning
    • [quant-ph]Second-order coding rates for key distillation in quantum key distribution
    • [stat.AP]On Post-Selection Inference in A/B Tests
    • [stat.CO]Distilling importance sampling
    • [stat.ME]A Bayesian Nonparametric Framework for Uncertainty Quantification in Simulation
    • [stat.ME]A New Confidence Interval for Odds Ratio
    • [stat.ME]Controlling Costs: Feature Selection on a Budget
    • [stat.ME]Estimands and Inference in Cluster-Randomized Vaccine Trials
    • [stat.ME]Forecast Aggregation via Peer Prediction
    • [stat.ME]Percentile-Based Residuals for Model Assessment
    • [stat.ME]Semi-parametric Bayes Regression with Network Valued Covariates
    • [stat.ML]Active ordinal tuplewise querying for similarity learning
    • [stat.ML]Deterministic Completion of Rectangular Matrices Using Ramanujan Bigraphs — II: Explicit Constructions and Phase Transitions
    • [stat.ML]Estimating Density Models with Complex Truncation Boundaries
    • [stat.ML]Estimating regression errors without ground truth values
    • [stat.ML]Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
    • [stat.ML]Learning with minibatch Wasserstein : asymptotic and gradient properties
    • [stat.ML]Optimal Training of Fair Predictive Models
    • [stat.ML]Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
    • [stat.ML]Practical Posterior Error Bounds from Variational Objectives
    • [stat.ML]Private Protocols for U-Statistics in the Local Model and Beyond
    • [stat.ML]Probabilistic sequential matrix factorization
    • [stat.ML]Spatio-Temporal Alignments: Optimal transport through space and time
    • [stat.ML]The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
    • [stat.OT]Computing the Expected Value of Sample Information Efficiently: Expertise and Skills Required for Four Model-Based Methods

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    • [astro-ph.IM]Development and Testing of an Engineering Model for an Asteroid Hopping Robot
    Greg Wilburn, Himangshu Kalita, Jekan Thangavelautham
    http://arxiv.org/abs/1910.03831v1

    • [cond-mat.mes-hall]Improvement in Retention Time of Capacitorless DRAM with Access Transistor
    Md. Hasan Raza Ansari, Jawar Singh Member
    http://arxiv.org/abs/1910.03907v1

    • [cs.AI]Detecting AI Trojans Using Meta Neural Analysis
    Xiaojun Xu, Qi Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, Bo Li
    http://arxiv.org/abs/1910.03137v2

    • [cs.AI]Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning
    Matthias Hutsebaut-Buysse, Kevin Mets, Steven Latré
    http://arxiv.org/abs/1910.04040v1

    • [cs.AI]Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding
    Wenqiang Liu, Hongyun Cai, Xu Cheng, Sifa Xie, Yipeng Yu, Hanyu Zhang
    http://arxiv.org/abs/1910.03891v1

    • [cs.AI]Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
    Haoran Wei, Yuanbo Wang, Lidia Mangu, Keith Decker
    http://arxiv.org/abs/1910.03743v1

    • [cs.AI]Toward a Computational Theory of Evidence-Based Reasoning for Instructable Cognitive Agents
    Gheorghe Tecuci, Dorin Marcu, Mihai Boicu, Steven Meckl, Chirag Uttamsingh
    http://arxiv.org/abs/1910.03990v1

    • [cs.CG]On geodesic triangles with right angles in a dually flat space
    Frank Nielsen
    http://arxiv.org/abs/1910.03935v1

    • [cs.CL]Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
    Qingyang Wu, Yichi Zhang, Yu Li, Zhou Yu
    http://arxiv.org/abs/1910.03756v1

    • [cs.CL]Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction
    Elena Alvarez-Mellado, Eben Holderness, Nicholas Miller, Fyonn Dhang, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
    http://arxiv.org/abs/1910.04006v1

    • [cs.CL]BHAAV- A Text Corpus for Emotion Analysis from Hindi Stories
    Yaman Kumar, Debanjan Mahata, Sagar Aggarwal, Anmol Chugh, Rajat Maheshwari, Rajiv Ratn Shah
    http://arxiv.org/abs/1910.04073v1

    • [cs.CL]Do People Prefer “Natural” code?
    Casey Casalnuovo, Kevin Lee, Hulin Wang, Prem Devanbu, Emily Morgan
    http://arxiv.org/abs/1910.03704v1

    • [cs.CL]Executing Instructions in Situated Collaborative Interactions
    Alane Suhr, Claudia Yan, Jacob Schluger, Stanley Yu, Hadi Khader, Marwa Mouallem, Iris Zhang, Yoav Artzi
    http://arxiv.org/abs/1910.03655v1

    • [cs.CL]Is Multilingual BERT Fluent in Language Generation?
    Samuel Rönnqvist, Jenna Kanerva, Tapio Salakoski, Filip Ginter
    http://arxiv.org/abs/1910.03806v1

    • [cs.CL]Knowledge Distillation from Internal Representations
    Gustavo Aguilar, Yuan Ling, Yu Zhang, Benjamin Yao, Xing Fan, Edward Guo
    http://arxiv.org/abs/1910.03723v1

    • [cs.CL]Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
    Oana-Maria Camburu, Brendan Shillingford, Pasquale Minervini, Thomas Lukasiewicz, Phil Blunsom
    http://arxiv.org/abs/1910.03065v2

    • [cs.CL]Novel Applications of Factored Neural Machine Translation
    Patrick Wilken, Evgeny Matusov
    http://arxiv.org/abs/1910.03912v1

    • [cs.CL]The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation
    Richard Yuanzhe Pang
    http://arxiv.org/abs/1910.03747v1

    • [cs.CL]Towards De-identification of Legal Texts
    Diego Garat, Dina Wonsever
    http://arxiv.org/abs/1910.03739v1

    • [cs.CL]Transformers: State-of-the-art Natural Language Processing
    Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Jamie Brew
    http://arxiv.org/abs/1910.03771v1

    • [cs.CL]Word Embedding Visualization Via Dictionary Learning
    Juexiao Zhang, Yubei Chen, Brian Cheung, Bruno A Olshausen
    http://arxiv.org/abs/1910.03833v1

    • [cs.CR]Privacy-preserving and yet Robust Collaborative Filtering Recommender as a Service
    Qiang Tang
    http://arxiv.org/abs/1910.03846v1

    • [cs.CV]3D Manhattan Room Layout Reconstruction from a Single 360 Image
    Chuhang Zou, Jheng-Wei Su, Chi-Han Peng, Alex Colburn, Qi Shan, Peter Wonka, Hung-Kuo Chu, Derek Hoiem
    http://arxiv.org/abs/1910.04099v1

    • [cs.CV]A Semi-Supervised Maximum Margin Metric Learning Approach for Small Scale Person Re-identification
    T M Feroz Ali, Subhasis Chaudhuri
    http://arxiv.org/abs/1910.03905v1

    • [cs.CV]BIAS: Transparent reporting of biomedical image analysis challenges
    Lena Maier-Hein, Annika Reinke, Michal Kozubek, Anne L. Martel, Tal Arbel, Matthias Eisenmann, Allan Hanbuary, Pierre Jannin, Henning Müller, Sinan Onogur, Julio Saez-Rodriguez, Bram van Ginneken, Annette Kopp-Schneider, Bennett Landman
    http://arxiv.org/abs/1910.04071v1

    • [cs.CV]Bias-Resilient Neural Network
    Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V. Sullivan, Li Fei-Fei, Juan Carlos Niebles, Kilian M. Pohl
    http://arxiv.org/abs/1910.03676v1

    • [cs.CV]Deep Convolutional Neural Network for Multi-modal Image Restoration and Fusion
    Xin Deng, Pier Luigi Dragotti
    http://arxiv.org/abs/1910.04066v1

    • [cs.CV]Detection and Identification of Objects and Humans in Thermal Images
    Manish Bhattarai, Manel Martínez-Ramón
    http://arxiv.org/abs/1910.03617v1

    • [cs.CV]ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations
    Vivek Sharma, Praneeth Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, Ramesh Raskar
    http://arxiv.org/abs/1910.03731v1

    • [cs.CV]Exploring Hate Speech Detection in Multimodal Publications
    Raul Gomez, Jaume Gibert, Lluis Gomez, Dimosthenis Karatzas
    http://arxiv.org/abs/1910.03814v1

    • [cs.CV]Fast Panoptic Segmentation Network
    Daan de Geus, Panagiotis Meletis, Gijs Dubbelman
    http://arxiv.org/abs/1910.03892v1

    • [cs.CV]FisheyeDistanceNet: Self-Supervised Scale-Aware Distance Estimation using Monocular Fisheye Camera for Autonomous Driving
    Varun Ravi Kumar, Sandesh Athni Hiremath, Stefan Milz, Christian Witt, Clement Pinnard, Senthil Yogamani, Patrick Mader
    http://arxiv.org/abs/1910.04076v1

    • [cs.CV]Gradient Information Guided Deraining with A Novel Network and Adversarial Training
    Yinglong Wang, Haokui Zhang, Yu Liu, Qinfeng Shi, Bing Zeng
    http://arxiv.org/abs/1910.03839v1

    • [cs.CV]Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
    Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos
    http://arxiv.org/abs/1910.03579v1

    • [cs.CV]Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation
    Zhijie Fang, Antonio M. López
    http://arxiv.org/abs/1910.03858v1

    • [cs.CV]Learning deep forest with multi-scale Local Binary Pattern features for face anti-spoofing
    Rizhao Cai, Changsheng Chen
    http://arxiv.org/abs/1910.03850v1

    • [cs.CV]Learning to Generalize One Sample at a Time with Self-Supervision
    Antonio D’Innocente, Silvia Bucci, Tatiana Tommasi, Barbara Caputo
    http://arxiv.org/abs/1910.03915v1

    • [cs.CV]MIDV-2019: Challenges of the modern mobile-based document OCR
    Konstantin Bulatov, Daniil Matalov, Vladimir V. Arlazarov
    http://arxiv.org/abs/1910.04009v1

    • [cs.CV]Meta-Transfer Learning through Hard Tasks
    Qianru Sun, Yaoyao Liu, Zhaozheng Chen, Tat-Seng Chua, Bernt Schiele
    http://arxiv.org/abs/1910.03648v1

    • [cs.CV]MixMatch Domain Adaptaion: Prize-winning solution for both tracks of VisDA 2019 challenge
    Danila Rukhovich, Danil Galeev
    http://arxiv.org/abs/1910.03903v1

    • [cs.CV]Multiple Kernel Fisher Discriminant Metric Learning for Person Re-identification
    T M Feroz Ali, Kalpesh K Patel, Rajbabu Velmurugan, Subhasis Chaudhuri
    http://arxiv.org/abs/1910.03923v1

    • [cs.CV]NADS-Net: A Nimble Architecture for Driver and Seat Belt Detection via Convolutional Neural Networks
    Sehyun Chun, Nima Hamidi Ghalehjegh, Joseph B. Choi, Chris W. Schwarz, John G. Gaspar, Daniel V. McGehee, Stephen S. Baek
    http://arxiv.org/abs/1910.03695v1

    • [cs.CV]Next integrated result modelling for stopping the text field recognition process in a video using a result model with per-character alternatives
    Konstantin Bulatov, Boris Savelyev, Vladimir V. Arlazarov
    http://arxiv.org/abs/1910.04107v1

    • [cs.CV]Patch Refinement — Localized 3D Object Detection
    Johannes Lehner, Andreas Mitterecker, Thomas Adler, Markus Hofmarcher, Bernhard Nessler, Sepp Hochreiter
    http://arxiv.org/abs/1910.04093v1

    • [cs.CV]Person re-identification based on Res2Net network
    Zongjing Cao, Hyo Jong Lee
    http://arxiv.org/abs/1910.04061v1

    • [cs.CV]Prose for a Painting
    Prerna Kashyap, Samrat Phatale, Iddo Drori
    http://arxiv.org/abs/1910.03634v1

    • [cs.CV]REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs
    José Ignacio Orlando, Huazhu Fu, João Barbossa Breda, Karel van Keer, Deepti R. Bathula, Andrés Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton van den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Xiulan Zhang, Yanwu Xu, Xiulan Zhang, Hrvoje Bogunović
    http://arxiv.org/abs/1910.03667v1

    • [cs.CV]SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition
    Younkwan Lee, Juhyun Lee, Hoyeon Ahn, Moongu Jeon
    http://arxiv.org/abs/1910.03876v1

    • [cs.CV]Semantic Understanding of Foggy Scenes with Purely Synthetic Data
    Martin Hahner, Dengxin Dai, Christos Sakaridis, Jan-Nico Zaech, Luc Van Gool
    http://arxiv.org/abs/1910.03997v1

    • [cs.CV]Semantic-aware Image Deblurring
    Fuhai Chen, Rongrong Ji, Chengpeng Dai, Xiaoshuai Sun, Chia-Wen Lin, Jiayi Ji, Baochang Zhang, Feiyue Huang, Liujuan Cao
    http://arxiv.org/abs/1910.03853v1

    • [cs.CV]Skin Lesion Classification Using Ensembles of Multi-Resolution EfficientNets with Meta Data
    Nils Gessert, Maximilian Nielsen, Mohsin Shaikh, René Werner, Alexander Schlaefer
    http://arxiv.org/abs/1910.03910v1

    • [cs.CV]Unaligned Image-to-Sequence Transformation with Loop Consistency
    Siyang Wang, Justin Lazarow, Kwonjoon Lee, Zhuowen Tu
    http://arxiv.org/abs/1910.04149v1

    • [cs.CV]Vehicle Re-identification with Viewpoint-aware Metric Learning
    Ruihang Chu, Yifan Sun, Yadong Li, Zheng Liu, Chi Zhang, Yichen Wei
    http://arxiv.org/abs/1910.04104v1

    • [cs.CV]View Confusion Feature Learning for Person Re-identification
    Fangyi Liu, Lei Zhang
    http://arxiv.org/abs/1910.03849v1

    • [cs.DC]Fog Computing as Privacy Enabler
    Frank Pallas, Philip Raschke, David Bermbach
    http://arxiv.org/abs/1910.04032v1

    • [cs.DC]Performance Impact of Memory Channels on Sparse and Irregular Algorithms
    Oded Green, James Fox, Jeffrey Young, Jun Shirako, David Bader
    http://arxiv.org/abs/1910.03679v1

    • [cs.DL]The role of mainstreamness and interdisciplinarity for the relevance of scientific papers
    Stefan Thurner, Wenyuan Liu, Peter Klimek, Siew Ann Cheong
    http://arxiv.org/abs/1910.03628v1

    • [cs.DS]Span-core Decomposition for Temporal Networks: Algorithms and Applications
    Edoardo Galimberti, Martino Ciaperoni, Alain Barrat, Francesco Bonchi, Ciro Cattuto, Francesco Gullo
    http://arxiv.org/abs/1910.03645v1

    • [cs.IT]A Data Concealing Technique with Random Noise Disturbance and A Restoring Technique for the Concealed Data by Stochastic Process Estimation
    Tomohiro Fujii, Masao Hirokawa
    http://arxiv.org/abs/1910.03214v1

    • [cs.IT]A Survey on Deep-Learning based Techniques for Modeling and Estimation of MassiveMIMO Channels
    Makan Zamanipour
    http://arxiv.org/abs/1910.03390v1

    • [cs.IT]Alternate Distributed Beamforming for Decode-and-Forward Multi-Relay Systems Using Buffers
    Jiayu Zhou, Deli Qiao
    http://arxiv.org/abs/1910.03954v1

    • [cs.IT]Approaching the Finite Blocklength Capacity within 0.025dB by Short Polar Codes and CRC-Aided Hybrid Decoding
    Jinnan Piao, Kai Niu, Jincheng Dai, Chao Dong
    http://arxiv.org/abs/1910.03254v1

    • [cs.IT]Counterexamples on the monotonicity of delay optimal strategies for energy harvesting transmitters
    Borna Sayedana, Aditya Mahajan
    http://arxiv.org/abs/1910.03556v1

    • [cs.IT]DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access
    Zhaoji Zhang, Ying Li, Chongwen Huang, Qinghua Guo, Chau Yuen, Yong Liang Guan
    http://arxiv.org/abs/1910.02953v1

    • [cs.IT]DNN-Aided Message Passing Based Block Sparse Bayesian Learning for Joint User Activity Detection and Channel Estimation
    Zhaoji Zhang, Ying Li, Chongwen Huang, Qinghua Guo, Chau Yuen, Yong Liang Guan
    http://arxiv.org/abs/1910.04154v1

    • [cs.IT]Entanglement-Enabled Communication
    Janis Nötzel
    http://arxiv.org/abs/1910.03796v1

    • [cs.IT]Evaluation of Error Probability of Classification Based on the Analysis of the Bayes Code
    Shota Saito, Toshiyasu Matsushima
    http://arxiv.org/abs/1910.03257v1

    • [cs.IT]High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
    Yuqing Du, Sheng Yang, Kaibin Huang
    http://arxiv.org/abs/1910.03865v1

    • [cs.IT]Overcoming DoF Limitation in Robust Beamforming: A Penalized Inequality-Constrained Approach
    Wenqiang Pu, Jinjun Xiao, Tao Zhang, Zhi-Quan Luo
    http://arxiv.org/abs/1910.03365v1

    • [cs.IT]Secret key agreement from correlated data, with no prior information
    Marius Zimand
    http://arxiv.org/abs/1910.03757v1

    • [cs.IT]Understanding the Performance of Bluetooth Mesh: Reliability, Delay and Scalability Analysis
    Raúl Rondón, Aamir Mahmood, Simone Grimaldi, Mikael Gidlund
    http://arxiv.org/abs/1910.03345v1

    • [cs.LG]A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks
    Takanori Maehara, Hoang NT
    http://arxiv.org/abs/1910.03802v1

    • [cs.LG]Accelerating Federated Learning via Momentum Gradient Descent
    Wei Liu, Li Chen, Yunfei Chen, Wenyi Zhang
    http://arxiv.org/abs/1910.03197v2

    • [cs.LG]Adversarial Learning of Deepfakes in Accounting
    Marco Schreyer, Timur Sattarov, Bernd Reimer, Damian Borth
    http://arxiv.org/abs/1910.03810v1

    • [cs.LG]Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
    Santiago Hernández-Orozco, Hector Zenil, Jürgen Riedel, Adam Uccello, Narsis A. Kiani, Jesper Tegnér
    http://arxiv.org/abs/1910.02758v2

    • [cs.LG]An MDL-Based Classifier for Transactional Datasets with Application in Malware Detection
    Behzad Asadi, Vijay Varadharajan
    http://arxiv.org/abs/1910.03751v1

    • [cs.LG]AutoML using Metadata Language Embeddings
    Iddo Drori, Lu Liu, Yi Nian, Sharath C. Koorathota, Jie S. Li, Antonio Khalil Moretti, Juliana Freire, Madeleine Udell
    http://arxiv.org/abs/1910.03698v1

    • [cs.LG]Beyond Vector Spaces: Compact Data Representationas Differentiable Weighted Graphs
    Denis Mazur, Vage Egiazarian, Stanislav Morozov, Artem Babenko
    http://arxiv.org/abs/1910.03524v2

    • [cs.LG]Compatible features for Monotonic Policy Improvement
    Marcin B. Tomczak, Enrique Munoz de Cote, Sergio Valcarcel Macua, Peter Vrancx
    http://arxiv.org/abs/1910.03880v1

    • [cs.LG]Continual Learning Using Bayesian Neural Networks
    HongLin Li, Payam Barnaghi, Shirin Enshaeifar, Frieder Ganz
    http://arxiv.org/abs/1910.04112v1

    • [cs.LG]Ctrl-Z: Recovering from Instability in Reinforcement Learning
    Vibhavari Dasagi, Jake Bruce, Thierry Peynot, Jürgen Leitner
    http://arxiv.org/abs/1910.03732v1

    • [cs.LG]DEVDAN: Deep Evolving Denoising Autoencoder
    Andri Ashfahani, Mahardhika Pratama, Edwin Lughofer, Yew Soon Ong
    http://arxiv.org/abs/1910.04062v1

    • [cs.LG]Deep Latent Defence
    Giulio Zizzo, Chris Hankin, Sergio Maffeis, Kevin Jones
    http://arxiv.org/abs/1910.03916v1

    • [cs.LG]Derivative-Free & Order-Robust Optimisation
    Victor Gabillon, Rasul Tutunov, Michal Valko, Haitham Bou Ammar
    http://arxiv.org/abs/1910.04034v1

    • [cs.LG]Dissecting Deep Neural Networks
    Haakon Robinson, Adil Rasheed, Omer San
    http://arxiv.org/abs/1910.03879v1

    • [cs.LG]Electric Load and Power Forecasting Using Ensemble Gaussian Process Regression
    Tong Ma, Renke Huang, David Barajas-Solano, Ramakrishna Tipireddy, Alexandre M. Tartakovsky
    http://arxiv.org/abs/1910.03783v1

    • [cs.LG]FedMD: Heterogenous Federated Learning via Model Distillation
    Daliang Li, Junpu Wang
    http://arxiv.org/abs/1910.03581v1

    • [cs.LG]How Well Do WGANs Estimate the Wasserstein Metric?
    Anton Mallasto, Guido Montúfar, Augusto Gerolin
    http://arxiv.org/abs/1910.03875v1

    • [cs.LG]Improving Generalization in Meta Reinforcement Learning using Learned Objectives
    Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber
    http://arxiv.org/abs/1910.04098v1

    • [cs.LG]Investigation on the generalization of the Sampled Policy Gradient algorithm
    Nil Stolt Ansó
    http://arxiv.org/abs/1910.03728v1

    • [cs.LG]Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?
    Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang
    http://arxiv.org/abs/1910.03016v2

    • [cs.LG]Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees
    Tan Nguyen, Nan Ye, Peter L. Bartlett
    http://arxiv.org/abs/1910.03742v1

    • [cs.LG]Linking emotions to behaviors through deep transfer learning
    Haoqi Li, Brian Baucom, Panayiotis Georgiou
    http://arxiv.org/abs/1910.03641v1

    • [cs.LG]Loss Surface Sightseeing by Multi-Point Optimization
    Ivan Skorokhodov, Mikhail Burtsev
    http://arxiv.org/abs/1910.03867v1

    • [cs.LG]MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions
    Viswanath Sivakumar, Tim Rocktäschel, Alexander H. Miller, Heinrich Küttler, Nantas Nardelli, Mike Rabbat, Joelle Pineau, Sebastian Riedel
    http://arxiv.org/abs/1910.04054v1

    • [cs.LG]Model-Based Reinforcement Learning Exploiting State-Action Equivalence
    Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel, Odalric-Ambrym Maillard
    http://arxiv.org/abs/1910.04077v1

    • [cs.LG]Multi-Modal Simultaneous Forecasting of Vehicle Position Sequences using Social Attention
    Jean Mercat, Thomas Gilles, Nicole El Zoghby, Guillaume Sandou, Dominique Beauvois, Guillermo Pita Gil
    http://arxiv.org/abs/1910.03650v1

    • [cs.LG]Multiple-objective Reinforcement Learning for Inverse Design and Identification
    Haoran Wei, Mariefel Olarte, Garrett B. Goh
    http://arxiv.org/abs/1910.03741v1

    • [cs.LG]Nearly Minimal Over-Parametrization of Shallow Neural Networks
    Armin Eftekhari, ChaeHwan Song, Volkan Cevher
    http://arxiv.org/abs/1910.03948v1

    • [cs.LG]On Dimension-free Tail Inequalities for Sums of Random Matrices and Applications
    Chao Zhang, Min-Hsiu Hsieh, Dacheng Tao
    http://arxiv.org/abs/1910.03718v1

    • [cs.LG]On the Possibility of Rewarding Structure Learning Agents: Mutual Information on Linguistic Random Sets
    Ignacio Arroyo-Fernández, Mauricio Carrasco-Ruíz, J. Anibal Arias-Aguilar
    http://arxiv.org/abs/1910.04023v1

    • [cs.LG]Policy Optimization Through Approximated Importance Sampling
    Marcin B. Tomczak, Dongho Kim, Peter Vrancx, Kee-Eung Kim
    http://arxiv.org/abs/1910.03857v1

    • [cs.LG]Receding Horizon Curiosity
    Matthias Schultheis, Boris Belousov, Hany Abdulsamad, Jan Peters
    http://arxiv.org/abs/1910.03620v1

    • [cs.LG]Sequential VAE-LSTM for Anomaly Detection on Time Series
    Run-Qing Chen, Guang-Hui Shi, Wan-Lei Zhao, Chang-Hui Liang
    http://arxiv.org/abs/1910.03818v1

    • [cs.LG]SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
    Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Jeremy Dawson, Nasser M. Nasrabadi
    http://arxiv.org/abs/1910.03624v1

    • [cs.LG]Social Learning in Multi Agent Multi Armed Bandits
    Abishek Sankararaman, Ayalvadi Ganesh, Sanjay Shakkottai
    http://arxiv.org/abs/1910.02100v2

    • [cs.LG]Supervised feature selection with orthogonal regression and feature weighting
    Xia Wu, Xueyuan Xu, Jianhong Liu, Hailing Wang, Bin Hu, Feiping Nie
    http://arxiv.org/abs/1910.03787v1

    • [cs.LG]Text-to-Image Synthesis Based on Machine Generated Captions
    Marco Menardi, Alex Falcon, Saida S. Mohamed, Lorenzo Seidenari, Giuseppe Serra, Alberto Del Bimbo, Carlo Tasso
    http://arxiv.org/abs/1910.04056v1

    • [cs.LG]The fastest $\ell_{1,\infty}$ prox in the west
    Benjamín Béjar, Ivan Dokmanić, René Vidal
    http://arxiv.org/abs/1910.03749v1

    • [cs.NE]Deep neural network for pier scour prediction
    Mahesh Pal
    http://arxiv.org/abs/1910.03804v1

    • [cs.NE]Large Scale Global Optimization by Hybrid Evolutionary Computation
    Gutha Jaya Krishna, Vadlamani Ravi
    http://arxiv.org/abs/1910.03799v1

    • [cs.NI]Explaining Deep Learning-Based Networked Systems
    Zili Meng, Minhu Wang, Mingwei Xu, Hongzi Mao, Jiasong Bai, Hongxin Hu
    http://arxiv.org/abs/1910.03835v1

    • [cs.NI]Hierarchical Deep Double Q-Routing
    Ramy E. Ali, Bilgehan Erman, Ejder Baştuğ, Bruce Cilli
    http://arxiv.org/abs/1910.04041v1

    • [cs.RO]A Brain-Inspired Compact Cognitive Mapping System
    Taiping Zeng, Bailu Si
    http://arxiv.org/abs/1910.03913v1

    • [cs.RO]Automated Multidisciplinary Design and Control of Hopping Robots for Exploration of Extreme Environments on the Moon and Mars
    Himangshu Kalita, Jekan Thangavelautham
    http://arxiv.org/abs/1910.03827v1

    • [cs.RO]Autonomous Multirobot Technologies for Mars Mining Base Construction and Operation
    Jekan Thangavelautham, Aman Chandra, Erik Jensen
    http://arxiv.org/abs/1910.03829v1

    • [cs.RO]Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
    Arunkumar Byravan, Jost Tobias Springenberg, Abbas Abdolmaleki, Roland Hafner, Michael Neunert, Thomas Lampe, Noah Siegel, Nicolas Heess, Martin Riedmiller
    http://arxiv.org/abs/1910.04142v1

    • [cs.RO]Knowledge Induced Deep Q-Network for a Slide-to-Wall Object Grasping
    Hengyue Liang, Xibai Lou, Changhyun Choi
    http://arxiv.org/abs/1910.03781v1

    • [cs.RO]Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
    Brian Ichter, Edward Schmerling, Tsang-Wei Edward Lee, Aleksandra Faust
    http://arxiv.org/abs/1910.03701v1

    • [cs.RO]Multi-Vehicle Interaction Scenarios Generation with Interpretable Traffic Primitives and Gaussian Process Regression
    Weiyang Zhang, Wenshuo Wang, Ding Zhao
    http://arxiv.org/abs/1910.03633v1

    • [cs.RO]Multimodal representation models for prediction and control from partial information
    Martina Zambelli, Antoine Cully, Yiannis Demiris
    http://arxiv.org/abs/1910.03854v1

    • [cs.RO]Stochastic Triangular Mesh Mapping
    Clint D. Lombard, Corné E. van Daalen
    http://arxiv.org/abs/1910.03644v1

    • [cs.RO]Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors
    Yazhan Zhang, Weihao Yuan, Zicheng Kan, Michael Yu Wang
    http://arxiv.org/abs/1910.03973v1

    • [cs.SI]A k-hop Collaborate Game Model: Adaptive Strategy to Maximize Total Revenue
    Jianxiong Guo, Weili Wu
    http://arxiv.org/abs/1910.04125v1

    • [cs.SI]Link Prediction Under Imperfect Detection: Collaborative Filtering for Ecological Networks
    Xiao Fu, Eugene Seo, Justin Clarke, Rebecca A. Hutchinson
    http://arxiv.org/abs/1910.03659v1

    • [cs.SI]Rejection-Based Simulation of Non-Markovian Agents on Complex Networks
    Gerrit Großmann, Luca Bortolussi, Verena Wolf
    http://arxiv.org/abs/1910.03964v1

    • [eess.AS]MelGAN-VC: Voice Conversion and Audio Style Transfer on arbitrarily long samples using Spectrograms
    Marco Pasini
    http://arxiv.org/abs/1910.03713v1

    • [eess.IV]Cribriform pattern detection in prostate histopathological images using deep learning models
    Malay Singh, Emarene Mationg Kalaw, Wang Jie, Mundher Al-Shabi, Chin Fong Wong, Danilo Medina Giron, Kian-Tai Chong, Maxine Tan, Zeng Zeng, Hwee Kuan Lee
    http://arxiv.org/abs/1910.04030v1

    • [eess.IV]Deep Learning Accelerated Light Source Experiments
    Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Ian Foster
    http://arxiv.org/abs/1910.04081v1

    • [eess.IV]Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists’ screening performance
    Guilherme Aresta, Carlos Ferreira, João Pedrosa, Teresa Araújo, João Rebelo, Eduardo Negrão, Margarida Morgado, Filipe Alves, António Cunha, Isabel Ramos, Aurélio Campilho
    http://arxiv.org/abs/1910.03986v1

    • [eess.IV]FastSurfer — A fast and accurate deep learning based neuroimaging pipeline
    Leonie Henschel, Sailesh Conjeti, Santiago Estrada, Kersten Diers, Bruce Fischl, Martin Reuter
    http://arxiv.org/abs/1910.03866v1

    • [eess.IV]Large-scale Gastric Cancer Screening and Localization Using Multi-task Deep Neural Network
    Hong Yu, Xiaofan Zhang, Lingjun Song, Liren Jiang, Xiaodi Huang, Wen Chen, Chenbin Zhang, Jiahui Li, Jiji Yang, Zhiqiang Hu, Qi Duan, Wanyuan Chen, Xianglei He, Jinshuang Fan, Weihai Jiang, Li Zhang, Chengmin Qiu, Minmin Gu, Weiwei Sun, Yangqiong Zhang, Guangyin Peng, Weiwei Shen, Guohui Fu
    http://arxiv.org/abs/1910.03729v1

    • [eess.IV]Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution
    Xin Deng, Ren Yang, Mai Xu, Pier Luigi Dragotti
    http://arxiv.org/abs/1910.04074v1

    • [math.NA]Implicit Neural Solver for Time-dependent Linear PDEs with Convergence Guarantee
    Suprosanna Shit, Abinav Ravi Venkatakrishnan, Ivan Ezhov, Jana Lipkova, Marie Piraud, Bjoern Menze
    http://arxiv.org/abs/1910.03452v2

    • [math.OC]Bregman Proximal Framework for Deep Linear Neural Networks
    Mahesh Chandra Mukkamala, Felix Westerkamp, Emanuel Laude, Daniel Cremers, Peter Ochs
    http://arxiv.org/abs/1910.03638v1

    • [math.ST]Nonparametric principal subspace regression
    Mark Koudstaal, Dengdeng Yu, Dehan Kong, Fang Yao
    http://arxiv.org/abs/1910.02866v3

    • [math.ST]On adaptivity of wavelet thresholding estimators with negatively super-additive dependent noise
    Yuncai Yu, Xinsheng Liu, Ling Liu, Weisi Liu
    http://arxiv.org/abs/1910.03911v1

    • [math.ST]On the feasibility of parsimonious variable selection for Hotelling’s $T^2$-test
    Michael D. Perlman
    http://arxiv.org/abs/1910.03669v1

    • [math.ST]Penalized regression via the restricted bridge estimator
    Bahadır Yüzbaşı, Mohammad Arashi, Fikri Akdeniz
    http://arxiv.org/abs/1910.03660v1

    • [math.ST]Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm
    Matteo Barigozzi, Matteo Luciani
    http://arxiv.org/abs/1910.03821v1

    • [math.ST]Variance reduction for Markov chains with application to MCMC
    D. Belomestny, L. Iosipoi, E. Moulines, A. Naumov, S. Samsonov
    http://arxiv.org/abs/1910.03643v1

    • [physics.med-ph]A cascaded dual-domain deep learning reconstruction method for sparsely spaced multidetector helical CT
    Ao Zheng, Hewei Gao, Li Zhang, Yuxiang Xing
    http://arxiv.org/abs/1910.03746v1

    • [q-bio.MN]BoolSi: a tool for distributed simulations and analysis of Boolean networks
    Vladyslav Oles, Anton Kukushkin
    http://arxiv.org/abs/1910.03736v1

    • [q-bio.NC]Dynamic Brain Functional Networks Guided By Anatomical Knowledge
    Suprateek Kundu, Jin Ming, Jennifer Stevens
    http://arxiv.org/abs/1910.03577v1

    • [q-bio.QM]A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning
    Taiyu Zhu, Kezhi Li, Pantelis Georgiou
    http://arxiv.org/abs/1910.04059v1

    • [quant-ph]Second-order coding rates for key distillation in quantum key distribution
    Mark M. Wilde, Sumeet Khatri, Eneet Kaur, Saikat Guha
    http://arxiv.org/abs/1910.03883v1

    • [stat.AP]On Post-Selection Inference in A/B Tests
    Alex Deng, Yicheng Li, Jiannan Lu
    http://arxiv.org/abs/1910.03788v1

    • [stat.CO]Distilling importance sampling
    Dennis Prangle
    http://arxiv.org/abs/1910.03632v1

    • [stat.ME]A Bayesian Nonparametric Framework for Uncertainty Quantification in Simulation
    Wei Xie, Cheng Li, Yuefeng Wu, Pu Zhang
    http://arxiv.org/abs/1910.03766v1

    • [stat.ME]A New Confidence Interval for Odds Ratio
    Wojciech Zieliński
    http://arxiv.org/abs/1910.03832v1

    • [stat.ME]Controlling Costs: Feature Selection on a Budget
    Guo Yu, Daniela Witten, Jacob Bien
    http://arxiv.org/abs/1910.03627v1

    • [stat.ME]Estimands and Inference in Cluster-Randomized Vaccine Trials
    Kayla W. Kilpatrick, Michael G. Hudgens, M. Elizabeth Halloran
    http://arxiv.org/abs/1910.03675v1

    • [stat.ME]Forecast Aggregation via Peer Prediction
    Juntao Wang, Yang Liu, Yiling Chen
    http://arxiv.org/abs/1910.03779v1

    • [stat.ME]Percentile-Based Residuals for Model Assessment
    Sophie Bérubé, Abhirup Datta, Qingfeng Li, Chenguang Wang, Thomas A. Louis
    http://arxiv.org/abs/1910.03709v1

    • [stat.ME]Semi-parametric Bayes Regression with Network Valued Covariates
    Xin Ma, Suprateek Kundu, Jennifer Stevens
    http://arxiv.org/abs/1910.03772v1

    • [stat.ML]Active ordinal tuplewise querying for similarity learning
    Gregory Canal, Stefano Fenu, Christopher Rozell
    http://arxiv.org/abs/1910.04115v1

    • [stat.ML]Deterministic Completion of Rectangular Matrices Using Ramanujan Bigraphs — II: Explicit Constructions and Phase Transitions
    Shantanu Prasad Burnwal, Mathukumalli Vidyasagar, Kaneenika Sinha
    http://arxiv.org/abs/1910.03937v1

    • [stat.ML]Estimating Density Models with Complex Truncation Boundaries
    Song Liu, Takafumi Kanamori
    http://arxiv.org/abs/1910.03834v1

    • [stat.ML]Estimating regression errors without ground truth values
    Henri Tiittanen, Emilia Oikarinen, Andreas Henelius, Kai Puolamäki
    http://arxiv.org/abs/1910.04069v1

    • [stat.ML]Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization
    Poompol Buathong, David Ginsbourger, Tipaluck Krityakierne
    http://arxiv.org/abs/1910.04086v1

    • [stat.ML]Learning with minibatch Wasserstein : asymptotic and gradient properties
    Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, Nicolas Courty
    http://arxiv.org/abs/1910.04091v1

    • [stat.ML]Optimal Training of Fair Predictive Models
    Razieh Nabi, Daniel Malinsky, Ilya Shpitser
    http://arxiv.org/abs/1910.04109v1

    • [stat.ML]Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
    Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf, Adrian Weller
    http://arxiv.org/abs/1910.03962v1

    • [stat.ML]Practical Posterior Error Bounds from Variational Objectives
    Jonathan H. Huggins, Mikołaj Kasprzak, Trevor Campbell, Tamara Broderick
    http://arxiv.org/abs/1910.04102v1

    • [stat.ML]Private Protocols for U-Statistics in the Local Model and Beyond
    James Bell, Aurélien Bellet, Adrià Gascón, Tejas Kulkarni
    http://arxiv.org/abs/1910.03861v1

    • [stat.ML]Probabilistic sequential matrix factorization
    Ömer Deniz Akyildiz, Theodoros Damoulas, Mark F. J. Steel
    http://arxiv.org/abs/1910.03906v1

    • [stat.ML]Spatio-Temporal Alignments: Optimal transport through space and time
    Hicham Janati, Marco Cuturi, Alexandre Gramfort
    http://arxiv.org/abs/1910.03860v1

    • [stat.ML]The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
    Guillaume Staerman, Pavlo Mozharovskyi, Stephan Clémençon
    http://arxiv.org/abs/1910.04085v1

    • [stat.OT]Computing the Expected Value of Sample Information Efficiently: Expertise and Skills Required for Four Model-Based Methods
    Natalia R. Kunst, Edward Wilson, Fernando Alarid-Escudero, Gianluca Baio, Alan Brennan, Michael Fairley, David Glynn, Jeremy D. Goldhaber-Fiebert, Chris Jackson, Hawre Jalal, Nicolas A. Menzies, Mark Strong, Howard Thom, Anna Heath
    http://arxiv.org/abs/1910.03368v1