astro-ph.IM - 仪器仪表和天体物理学方法

    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.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.PL - 编程语言 cs.RO - 机器人学 cs.SD - 声音处理 econ.EM - 计量经济学 eess.IV - 图像与视频处理 eess.SP - 信号处理 eess.SY - 系统和控制 math.CO - 组合数学 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.ao-ph - 大气和海洋物理 physics.soc-ph - 物理学与社会 q-bio.PE - 人口与发展 q-bio.QM - 定量方法 q-fin.ST - 统计金融学 quant-ph - 量子物理 stat.AP - 应用统计 stat.CO - 统计计算 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [astro-ph.IM]Photometric light curves classification with machine learning
    • [cs.AI]Abstraction for Zooming-In to Unsolvability Reasons of Grid-Cell Problems
    • [cs.AI]Event Representation Learning Enhanced with External Commonsense Knowledge
    • [cs.AI]Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
    • [cs.CL]A Discrete Hard EM Approach for Weakly Supervised Question Answering
    • [cs.CL]BERT-Based Arabic Social Media Author Profiling
    • [cs.CL]BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding
    • [cs.CL]Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings
    • [cs.CL]Definition Frames: Using Definitions for Hybrid Concept Representations
    • [cs.CL]Dependency-Aware Named Entity Recognition with Relative and Global Attentions
    • [cs.CL]Discourse Tagging for Scientific Evidence Extraction
    • [cs.CL]Dynamic Fusion: Attentional Language Model for Neural Machine Translation
    • [cs.CL]Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text
    • [cs.CL]From English to Code-Switching: Transfer Learning with Strong Morphological Clues
    • [cs.CL]Getting Gender Right in Neural Machine Translation
    • [cs.CL]Global Locality in Event Extraction
    • [cs.CL]How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
    • [cs.CL]Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
    • [cs.CL]Jointly embedding the local and global relations of heterogeneous graph for rumor detection
    • [cs.CL]Learning Dynamic Author Representations with Temporal Language Models
    • [cs.CL]MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
    • [cs.CL]Neural Embedding Allocation: Distributed Representations of Topic Models
    • [cs.CL]Proposal Towards a Personalized Knowledge-powered Self-play Based Ensemble Dialog System
    • [cs.CL]Question Generation by Transformers
    • [cs.CL]The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews
    • [cs.CL]WIQA: A dataset for “What if…” reasoning over procedural text
    • [cs.CR]TBT: Targeted Neural Network Attack with Bit Trojan
    • [cs.CV]AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces
    • [cs.CV]Attention-Aware Age-Agnostic Visual Place Recognition
    • [cs.CV]CARS: Continuous Evolution for Efficient Neural Architecture Search
    • [cs.CV]Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos
    • [cs.CV]Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila
    • [cs.CV]DNANet: De-Normalized Attention Based Multi-Resolution Network for Human Pose Estimation
    • [cs.CV]Deep Elastic Networks with Model Selection for Multi-Task Learning
    • [cs.CV]Disentangled Image Matting
    • [cs.CV]Distortion-adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images
    • [cs.CV]Dual-attention Focused Module for Weakly Supervised Object Localization
    • [cs.CV]FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images
    • [cs.CV]How Old Are You? Face Age Translation with Identity Preservation Using GANs
    • [cs.CV]Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents
    • [cs.CV]Image Segmentation using Multi-Threshold technique by Histogram Sampling
    • [cs.CV]Local block-wise self attention for normal organ segmentation
    • [cs.CV]Multi-Sensor 3D Object Box Refinement for Autonomous Driving
    • [cs.CV]Probabilistic framework for solving Visual Dialog
    • [cs.CV]Reasoning About Human-Object Interactions Through Dual Attention Networks
    • [cs.CV]Revisiting Foreground-Background Imbalance in Object Detectors
    • [cs.CV]Sampling Strategies for GAN Synthetic Data
    • [cs.CV]Semantic Similarity Based Softmax Classifier for Zero-Shot Learning
    • [cs.CV]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
    • [cs.CV]Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation
    • [cs.CV]Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction
    • [cs.CV]WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection
    • [cs.CY]Sociotechnical Considerations for Accessible Visualization Design
    • [cs.DC]A Loosely Self-stabilizing Protocol for Randomized Congestion Control with Logarithmic Memory
    • [cs.DC]Characterizing the Deep Neural Networks Inference Performance of Mobile Applications
    • [cs.DC]Cogsworth: Byzantine View Synchronization
    • [cs.DC]Performance Estimation of Container-Based Cloud-to-Fog Offloading
    • [cs.DC]The Nubo Virtual Services Marketplace
    • [cs.HC]ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia
    • [cs.HC]Trust and Cognitive Load During Human-Robot Interaction
    • [cs.HC]Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms
    • [cs.IR]Graph-based data clustering via multiscale community detection
    • [cs.IR]How to make latent factors interpretable by feeding Factorization machines with knowledge graphs
    • [cs.IR]Report on the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019)
    • [cs.IR]Spam filtering on forums: A synthetic oversampling based approach for imbalanced data classification
    • [cs.IT]Bound on Peak-to-Average Power Ratio with Moment and Reduction Method
    • [cs.IT]Faster Johnson-Lindenstrauss Transforms via Kronecker Products
    • [cs.IT]Generalized Optimal Two-way Relays Subsets Pairings in Cloud-based Region Cognitive Networks
    • [cs.IT]Local Large Deviation Principle, Large Deviation Principle for the Signal -to- Interference and Noise Ratio Graph Models
    • [cs.IT]Polar Coding for the Wiretap Broadcast Channel with Multiple Messages
    • [cs.IT]Returning to Shannon’s Original Sense
    • [cs.IT]Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes
    • [cs.LG]A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots
    • [cs.LG]Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
    • [cs.LG]An Implicit Form of Krasulina’s k-PCA Update without the Orthonormality Constraint
    • [cs.LG]Automated Spectral Kernel Learning
    • [cs.LG]Better Communication Complexity for Local SGD
    • [cs.LG]Boosting Classifiers with Noisy Inference
    • [cs.LG]Correlation Priors for Reinforcement Learning
    • [cs.LG]Deep Declarative Networks: A New Hope
    • [cs.LG]Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
    • [cs.LG]DreamTime: Finding AlexNet for Time Series Classification
    • [cs.LG]Effectiveness of Adversarial Examples and Defenses for Malware Classification
    • [cs.LG]FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency
    • [cs.LG]Factorized MultiClass Boosting
    • [cs.LG]Few-Shot Classification on Unseen Domains by Learning Disparate Modulators
    • [cs.LG]First Analysis of Local GD on Heterogeneous Data
    • [cs.LG]Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
    • [cs.LG]GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks
    • [cs.LG]Gradient Descent with Compressed Iterates
    • [cs.LG]Group Representation Theory for Knowledge Graph Embedding
    • [cs.LG]How to detect novelty in textual data streams? A comparative study of existing methods
    • [cs.LG]Identifying and Resisting Adversarial Videos Using Temporal Consistency
    • [cs.LG]Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
    • [cs.LG]LazyBum: Decision tree learning using lazy propositionalization
    • [cs.LG]Learning Vector-valued Functions with Local Rademacher Complexity
    • [cs.LG]Learning to Propagate for Graph Meta-Learning
    • [cs.LG]Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification
    • [cs.LG]Logarithmic Regret for Online Control
    • [cs.LG]Multi-Year Vector Dynamic Time Warping Based Crop Mapping
    • [cs.LG]Neural Belief Reasoner
    • [cs.LG]On weighted uncertainty sampling in active learning
    • [cs.LG]Optimal Machine Intelligence Near the Edge of Chaos
    • [cs.LG]Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls
    • [cs.LG]Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
    • [cs.LG]RecSim: A Configurable Simulation Platform for Recommender Systems
    • [cs.LG]Recognizing Variables from their Data via Deep Embeddings of Distributions
    • [cs.LG]Reinforcement Learning and Video Games
    • [cs.LG]Safe Policy Improvement with an Estimated Baseline Policy
    • [cs.LG]Sparse and Imperceivable Adversarial Attacks
    • [cs.LG]Structural Robustness for Deep Learning Architectures
    • [cs.LG]Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization
    • [cs.LG]Towards Noise-Robust Neural Networks via Progressive Adversarial Training
    • [cs.LG]Towards Understanding the Importance of Shortcut Connections in Residual Networks
    • [cs.LG]When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies
    • [cs.MA]On Memory Mechanism in Multi-Agent Reinforcement Learning
    • [cs.MA]Signal Instructed Coordination in Team Competition
    • [cs.NE]Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes
    • [cs.NE]Covariance Matrix Adaptation Greedy Search Applied to Water Distribution System Optimization
    • [cs.PL]Static Analysis for Probabilistic Programs
    • [cs.RO]3D traffic flow model for UAVs
    • [cs.RO]A Deep Learning Approach to Grasping the Invisible
    • [cs.RO]A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units
    • [cs.RO]Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
    • [cs.RO]Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
    • [cs.RO]Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control
    • [cs.RO]Emotional Distraction for Children Anxiety Reduction During Vaccination
    • [cs.RO]Four-Arm Manipulation via Feet Interfaces
    • [cs.RO]Human-robot Collaborative Navigation Search using Social Reward Sources
    • [cs.RO]MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
    • [cs.RO]MPC-Net: A First Principles Guided Policy Search
    • [cs.RO]Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree
    • [cs.RO]On-Demand Trajectory Predictions for Interaction Aware Highway Driving
    • [cs.RO]On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case
    • [cs.RO]Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning
    • [cs.RO]Probabilistic Model Learning and Long-term Prediction for Contact-rich Manipulation Tasks
    • [cs.RO]Proceedings of the AI-HRI Symposium at AAAI-FSS 2019
    • [cs.RO]SwarmMesh: A Distributed Data Structure for Cooperative Multi-Robot Applications
    • [cs.SD]Computer Assisted Composition in Continuous Time
    • [econ.EM]Virtual Historical Simulation for estimating the conditional VaR of large portfolios
    • [eess.IV]CEREBRuM: a Convolutional Encoder-decodeR for Fully Volumetric Fast sEgmentation of BRain MRI
    • [eess.IV]Hybrid Cascaded Neural Network for Liver Lesion Segmentation
    • [eess.IV]Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks
    • [eess.IV]Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization
    • [eess.IV]Variable Rate Deep Image Compression With a Conditional Autoencoder
    • [eess.SP]Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems
    • [eess.SP]Minimization of Sum Inverse Energy Efficiency for Multiple Base Station Systems
    • [eess.SY]Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access Locations
    • [eess.SY]Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games
    • [eess.SY]Functional Principal Component Analysis as a Versatile Technique to Understand and Predict the Electric Consumption Patterns
    • [eess.SY]Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
    • [math.CO]Coding for Sunflowers
    • [math.OC]Distributed Deep Learning with Event-Triggered Communication
    • [math.PR]Unified $\ell_{2\rightarrow\infty}$ Eigenspace Perturbation Theory for Symmetric Random Matrices
    • [math.ST]Aggregated Hold-Out
    • [math.ST]Bayesian Inference on Volatility in the Presence of Infinite Jump Activity and Microstructure Noise
    • [math.ST]Distorted stochastic dominance: a generalized family of stochastic orders
    • [math.ST]Optimality of the Subgradient Algorithm in the Stochastic Setting
    • [math.ST]Targeted Random Projection for Prediction from High-Dimensional Features
    • [physics.ao-ph]Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz ‘96 Model
    • [physics.soc-ph]Clusters and the entropy in opinion dynamics on complex networks
    • [q-bio.PE]Boltzmann machine learning and regularization methods for inferring evolutionary fields and couplings from a multiple sequence alignment
    • [q-bio.QM]Reconstructing continuously heterogeneous structures from single particle cryo-EM with deep generative models
    • [q-fin.ST]Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning
    • [quant-ph]A Quantum Search Decoder for Natural Language Processing
    • [stat.AP]A sub-critical branching process model for application to analysing Y haplotype DNA mixtures
    • [stat.AP]Design-adherent estimators for network surveys
    • [stat.AP]Home Sweet Home: Quantifying Home Court Advantages For NCAA Basketball Statistics
    • [stat.CO]Efficient Bayesian synthetic likelihood with whitening transformations
    • [stat.ME]Covariate Adaptive False Discovery Rate Control with Applications to Omics-Wide Multiple Testing
    • [stat.ME]Insights and algorithms for the multivariate square-root lasso
    • [stat.ME]Regression to the Mean’s Impact on the Synthetic Control Method: Bias and Sensitivity Analysis
    • [stat.ME]Robust Regression with Compositional Covariates
    • [stat.ML]Anomaly Detection with Inexact Labels
    • [stat.ML]Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
    • [stat.ML]Correcting Predictions for Approximate Bayesian Inference
    • [stat.ML]Goodness-of-fit tests on manifolds
    • [stat.ML]Implicit Regularization for Optimal Sparse Recovery
    • [stat.ML]Practical Calculation of Gittins Indices for Multi-armed Bandits
    • [stat.ML]Regularized deep learning with a non-convex penalty

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

    • [astro-ph.IM]Photometric light curves classification with machine learning
    Tatiana Gabruseva, Sergey Zlobin, Peter Wang
    http://arxiv.org/abs/1909.05032v1

    • [cs.AI]Abstraction for Zooming-In to Unsolvability Reasons of Grid-Cell Problems
    Thomas Eiter, Zeynep G. Saribatur, Peter Schüller
    http://arxiv.org/abs/1909.04998v1

    • [cs.AI]Event Representation Learning Enhanced with External Commonsense Knowledge
    Xiao Ding, Kuo Liao, Ting Liu, Zhongyang Li, Junwen Duan
    http://arxiv.org/abs/1909.05190v1

    • [cs.AI]Learning Transferable Domain Priors for Safe Exploration in Reinforcement Learning
    Thommen George Karimpanal, Santu Rana, Sunil Gupta, Truyen Tran, Svetha Venkatesh
    http://arxiv.org/abs/1909.04307v2

    • [cs.CL]A Discrete Hard EM Approach for Weakly Supervised Question Answering
    Sewon Min, Danqi Chen, Hannaneh Hajishirzi, Luke Zettlemoyer
    http://arxiv.org/abs/1909.04849v1

    • [cs.CL]BERT-Based Arabic Social Media Author Profiling
    Chiyu Zhang, Muhammad Abdul-Mageed
    http://arxiv.org/abs/1909.04181v2

    • [cs.CL]BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding
    Timo I. Denk, Christian Reisswig
    http://arxiv.org/abs/1909.04948v1

    • [cs.CL]Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings
    Łukasz Augustyniak, Tomasz Kajdanowicz, Przemysław Kazienko
    http://arxiv.org/abs/1909.04917v1

    • [cs.CL]Definition Frames: Using Definitions for Hybrid Concept Representations
    Evangelia Spiliopoulou, Eduard Hovy
    http://arxiv.org/abs/1909.04793v1

    • [cs.CL]Dependency-Aware Named Entity Recognition with Relative and Global Attentions
    Gustavo Aguilar, Thamar Solorio
    http://arxiv.org/abs/1909.05166v1

    • [cs.CL]Discourse Tagging for Scientific Evidence Extraction
    Xiangci Li, Gully Burns, Nanyun Peng
    http://arxiv.org/abs/1909.04758v1

    • [cs.CL]Dynamic Fusion: Attentional Language Model for Neural Machine Translation
    Michiki Kurosawa, Mamoru Komachi
    http://arxiv.org/abs/1909.04879v1

    • [cs.CL]Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text
    Bhavana Dalvi Mishra, Niket Tandon, Antoine Bosselut, Wen-tau Yih, Peter Clark
    http://arxiv.org/abs/1909.04745v1

    • [cs.CL]From English to Code-Switching: Transfer Learning with Strong Morphological Clues
    Gustavo Aguilar, Thamar Solorio
    http://arxiv.org/abs/1909.05158v1

    • [cs.CL]Getting Gender Right in Neural Machine Translation
    Eva Vanmassenhove, Christian Hardmeier, Andy Way
    http://arxiv.org/abs/1909.05088v1

    • [cs.CL]Global Locality in Event Extraction
    Elaheh ShafieiBavani, Antonio Jimeno Yepes, Xu Zhong
    http://arxiv.org/abs/1909.04822v1

    • [cs.CL]How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations
    Betty van Aken, Benjamin Winter, Alexander Löser, Felix A. Gers
    http://arxiv.org/abs/1909.04925v1

    • [cs.CL]Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy
    Bowen Yu, Zhenyu Zhang, Jianlin Su, Yubin Wang, Tingwen Liu, Bin Wang, Sujian Li
    http://arxiv.org/abs/1909.04273v2

    • [cs.CL]Jointly embedding the local and global relations of heterogeneous graph for rumor detection
    Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, Songlin Hu
    http://arxiv.org/abs/1909.04465v2

    • [cs.CL]Learning Dynamic Author Representations with Temporal Language Models
    Edouard Delasalles, Sylvain Lamprier, Ludovic Denoyer
    http://arxiv.org/abs/1909.04985v1

    • [cs.CL]MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
    Julian Eisenschlos, Sebastian Ruder, Piotr Czapla, Marcin Kardas, Sylvain Gugger, Jeremy Howard
    http://arxiv.org/abs/1909.04761v1

    • [cs.CL]Neural Embedding Allocation: Distributed Representations of Topic Models
    Kamrun Naher Keya, Yannis Papanikolaou, James R. Foulds
    http://arxiv.org/abs/1909.04702v1

    • [cs.CL]Proposal Towards a Personalized Knowledge-powered Self-play Based Ensemble Dialog System
    Richard Csaky
    http://arxiv.org/abs/1909.05016v1

    • [cs.CL]Question Generation by Transformers
    Kettip Kriangchaivech, Artit Wangperawong
    http://arxiv.org/abs/1909.05017v1

    • [cs.CL]The Longer the Better? The Interplay Between Review Length and Line of Argumentation in Online Consumer Reviews
    Bernhard Lutz, Nicolas Pröllochs, Dirk Neumann
    http://arxiv.org/abs/1909.05192v1

    • [cs.CL]WIQA: A dataset for “What if…” reasoning over procedural text
    Niket Tandon, Bhavana Dalvi Mishra, Keisuke Sakaguchi, Antoine Bosselut, Peter Clark
    http://arxiv.org/abs/1909.04739v1

    • [cs.CR]TBT: Targeted Neural Network Attack with Bit Trojan
    Adnan Siraj Rakin, Zhezhi He, Deliang Fan
    http://arxiv.org/abs/1909.05193v1

    • [cs.CV]AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces
    Muhammad Haris Khan, John McDonagh, Salman Khan, Muhammad Shahabuddin, Aditya Arora, Fahad Shahbaz Khan, Ling Shao, Georgios Tzimiropoulos
    http://arxiv.org/abs/1909.04951v1

    • [cs.CV]Attention-Aware Age-Agnostic Visual Place Recognition
    Ziqi Wang, Jiahui Li, Seyran Khademi, Jan van Gemert
    http://arxiv.org/abs/1909.05163v1

    • [cs.CV]CARS: Continuous Evolution for Efficient Neural Architecture Search
    Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu
    http://arxiv.org/abs/1909.04977v1

    • [cs.CV]Comparative Analysis of CNN-based Spatiotemporal Reasoning in Videos
    Okan Köpüklü, Fabian Herzog, Gerhard Rigoll
    http://arxiv.org/abs/1909.05165v1

    • [cs.CV]Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila
    Khan Faraz, Ahmed Bouridane, Richard Jiang, Tiancheng Xia, Paul Chazot, Abdel Ennaceur
    http://arxiv.org/abs/1909.04974v1

    • [cs.CV]DNANet: De-Normalized Attention Based Multi-Resolution Network for Human Pose Estimation
    Kun Zhang, Peng He, Ping Yao, Ge Chen, Chuanguang Yang, Huimin Li, Li Fu, Tianyao Zheng
    http://arxiv.org/abs/1909.05090v1

    • [cs.CV]Deep Elastic Networks with Model Selection for Multi-Task Learning
    Chanho Ahn, Eunwoo Kim, Songhwai Oh
    http://arxiv.org/abs/1909.04860v1

    • [cs.CV]Disentangled Image Matting
    Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan, Haibin Huang, Jiangyu Liu, Jiaming Liu, Jiaying Liu, Jue Wang, Jian Sun
    http://arxiv.org/abs/1909.04686v1

    • [cs.CV]Distortion-adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images
    Jia Li, Jinming Su, Changqun Xia, Yonghong Tian
    http://arxiv.org/abs/1909.04913v1

    • [cs.CV]Dual-attention Focused Module for Weakly Supervised Object Localization
    Yukun Zhou, Zailiang Chen, Hailan Shen, Qing Liu, Rongchang Zhao, Yixiong Liang
    http://arxiv.org/abs/1909.04813v1

    • [cs.CV]FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images
    Christian Zimmermann, Duygu Ceylan, Jimei Yang, Bryan Russell, Max Argus, Thomas Brox
    http://arxiv.org/abs/1909.04349v2

    • [cs.CV]How Old Are You? Face Age Translation with Identity Preservation Using GANs
    Zipeng Wang, Zhaoxiang Liu, Jianfeng Huang, Shiguo Lian, Yimin Lin
    http://arxiv.org/abs/1909.04988v1

    • [cs.CV]Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents
    Alexander Makrigiorgos, Ali Shafti, Alex Harston, Julien Gerard, A. Aldo Faisal
    http://arxiv.org/abs/1909.05003v1

    • [cs.CV]Image Segmentation using Multi-Threshold technique by Histogram Sampling
    Amit Gurung, Sangyal Lama Tamang
    http://arxiv.org/abs/1909.05084v1

    • [cs.CV]Local block-wise self attention for normal organ segmentation
    Jue Jiang, Elguindi Sharif, Hyemin Um, Sean Berry, Harini Veeraraghavan
    http://arxiv.org/abs/1909.05054v1

    • [cs.CV]Multi-Sensor 3D Object Box Refinement for Autonomous Driving
    Peiliang Li, Siqi Liu, Shaojie Shen
    http://arxiv.org/abs/1909.04942v1

    • [cs.CV]Probabilistic framework for solving Visual Dialog
    Badri N. Patro, Anupriy, Vinay P. Namboodiri
    http://arxiv.org/abs/1909.04800v1

    • [cs.CV]Reasoning About Human-Object Interactions Through Dual Attention Networks
    Tete Xiao, Quanfu Fan, Dan Gutfreund, Mathew Monfort, Aude Oliva, Bolei Zhou
    http://arxiv.org/abs/1909.04743v1

    • [cs.CV]Revisiting Foreground-Background Imbalance in Object Detectors
    Joya Chen, Dong Liu, Tong Xu, Enhong Chen
    http://arxiv.org/abs/1909.04868v1

    • [cs.CV]Sampling Strategies for GAN Synthetic Data
    Binod Bhattarai, Seungryul Baek, Rumeysa Bodur, Tae-Kyun Kim
    http://arxiv.org/abs/1909.04689v1

    • [cs.CV]Semantic Similarity Based Softmax Classifier for Zero-Shot Learning
    Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava
    http://arxiv.org/abs/1909.04790v1

    • [cs.CV]SoftTriple Loss: Deep Metric Learning Without Triplet Sampling
    Qi Qian, Lei Shang, Baigui Sun, Juhua Hu, Hao Li, Rong Jin
    http://arxiv.org/abs/1909.05235v1

    • [cs.CV]Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation
    Arijit Ray, Karan Sikka, Ajay Divakaran, Stefan Lee, Giedrius Burachas
    http://arxiv.org/abs/1909.04696v1

    • [cs.CV]Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction
    Jingwen Wang, Lin Ma, Wenhao Jiang
    http://arxiv.org/abs/1909.05010v1

    • [cs.CV]WSOD^2: Learning Bottom-up and Top-down Objectness Distillation for Weakly-supervised Object Detection
    Zhaoyang Zeng, Bei Liu, Jianlong Fu, Hongyang Chao, Lei Zhang
    http://arxiv.org/abs/1909.04972v1

    • [cs.CY]Sociotechnical Considerations for Accessible Visualization Design
    Alan Lundgard, Crystal Lee, Arvind Satyanarayan
    http://arxiv.org/abs/1909.05118v1

    • [cs.DC]A Loosely Self-stabilizing Protocol for Randomized Congestion Control with Logarithmic Memory
    Michael Feldmann, Thorsten Götte, Christian Scheideler
    http://arxiv.org/abs/1909.04544v2

    • [cs.DC]Characterizing the Deep Neural Networks Inference Performance of Mobile Applications
    Samuel S. Ogden, Tian Guo
    http://arxiv.org/abs/1909.04783v1

    • [cs.DC]Cogsworth: Byzantine View Synchronization
    Oded Naor, Mathieu Baudet, Dahlia Malkhi, Alexander Spiegelman
    http://arxiv.org/abs/1909.05204v1

    • [cs.DC]Performance Estimation of Container-Based Cloud-to-Fog Offloading
    Ayesha Abdul Majeed, Peter Kilpatrick, Ivor Spence, Blesson Varghese
    http://arxiv.org/abs/1909.04945v1

    • [cs.DC]The Nubo Virtual Services Marketplace
    James Kempf, Sambit Nayak, Remi Robert, Jim Feng, Kunal Rajan Deshmukh, Anshu Shukla, Aleksandra Obeso Duque, Nanjangud Narendra, Johan Sjöberg
    http://arxiv.org/abs/1909.04934v1

    • [cs.HC]ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia
    Aaron Halfaker, R. Stuart Geiger
    http://arxiv.org/abs/1909.05189v1

    • [cs.HC]Trust and Cognitive Load During Human-Robot Interaction
    Muneeb Imtiaz Ahmad, Jasmin Bernotat, Katrin Lohan, Friederike Eyssel
    http://arxiv.org/abs/1909.05160v1

    • [cs.HC]Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms
    Meng Xia, Huan Wei, Min Xu, Leo Yu Ho Lo, Yong Wang, Rong Zhang, Huamin Qu
    http://arxiv.org/abs/1909.04749v1

    • [cs.IR]Graph-based data clustering via multiscale community detection
    Zijing Liu, Mauricio Barahona
    http://arxiv.org/abs/1909.04491v1

    • [cs.IR]How to make latent factors interpretable by feeding Factorization machines with knowledge graphs
    Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, Joseph Trotta
    http://arxiv.org/abs/1909.05038v1

    • [cs.IR]Report on the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019)
    Guillaume Cabanac, Ingo Frommholz, Philipp Mayr
    http://arxiv.org/abs/1909.04954v1

    • [cs.IR]Spam filtering on forums: A synthetic oversampling based approach for imbalanced data classification
    Pratik Ratadiya, Rahul Moorthy
    http://arxiv.org/abs/1909.04826v1

    • [cs.IT]Bound on Peak-to-Average Power Ratio with Moment and Reduction Method
    Hirofumi Tsuda
    http://arxiv.org/abs/1909.05110v1

    • [cs.IT]Faster Johnson-Lindenstrauss Transforms via Kronecker Products
    Ruhui Jin, Tamara G. Kolda, Rachel Ward
    http://arxiv.org/abs/1909.04801v1

    • [cs.IT]Generalized Optimal Two-way Relays Subsets Pairings in Cloud-based Region Cognitive Networks
    He Huang, Chaowei Yuan
    http://arxiv.org/abs/1909.05059v1

    • [cs.IT]Local Large Deviation Principle, Large Deviation Principle for the Signal -to- Interference and Noise Ratio Graph Models
    Enoch Sakyi-Yeboah, Kwabena Doku-Amponsah
    http://arxiv.org/abs/1909.04529v2

    • [cs.IT]Polar Coding for the Wiretap Broadcast Channel with Multiple Messages
    Jaume del Olmo, Javier R. Fonollosa
    http://arxiv.org/abs/1909.04898v1

    • [cs.IT]Returning to Shannon’s Original Sense
    Xuezhi Yang
    http://arxiv.org/abs/1909.04978v1

    • [cs.IT]Sublinear Latency for Simplified Successive Cancellation Decoding of Polar Codes
    Marco Mondelli, Seyyed Ali Hashemi, John Cioffi, Andrea Goldsmith
    http://arxiv.org/abs/1909.04892v1

    • [cs.LG]A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots
    Nicolai A. Lynnerup, Laura Nolling, Rasmus Hasle, John Hallam
    http://arxiv.org/abs/1909.03772v2

    • [cs.LG]Addressing Algorithmic Bottlenecks in Elastic Machine Learning with Chicle
    Michael Kaufmann, Kornilios Kourtis, Celestine Mendler-Dünner, Adrian Schüpbach, Thomas Parnell
    http://arxiv.org/abs/1909.04885v1

    • [cs.LG]An Implicit Form of Krasulina’s k-PCA Update without the Orthonormality Constraint
    Ehsan Amid, Manfred K. Warmuth
    http://arxiv.org/abs/1909.04803v1

    • [cs.LG]Automated Spectral Kernel Learning
    Jian Li, Yong Liu, Weiping Wang
    http://arxiv.org/abs/1909.04894v1

    • [cs.LG]Better Communication Complexity for Local SGD
    Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
    http://arxiv.org/abs/1909.04746v1

    • [cs.LG]Boosting Classifiers with Noisy Inference
    Yongjune Kim, Yuval Cassuto, Lav R. Varshney
    http://arxiv.org/abs/1909.04766v1

    • [cs.LG]Correlation Priors for Reinforcement Learning
    Bastian Alt, Adrian Šošić, Heinz Koeppl
    http://arxiv.org/abs/1909.05106v1

    • [cs.LG]Deep Declarative Networks: A New Hope
    Stephen Gould, Richard Hartley, Dylan Campbell
    http://arxiv.org/abs/1909.04866v1

    • [cs.LG]Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
    Haidong Rong, Yangzihao Wang, Feihu Zhou, Junjie Zhai, Haiyang Wu, Rui Lan, Fan Li, Han Zhang, Yuekui Yang, Zhenyu Guo, Di Wang
    http://arxiv.org/abs/1909.04823v1

    • [cs.LG]DreamTime: Finding AlexNet for Time Series Classification
    Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, François Petitjean
    http://arxiv.org/abs/1909.04939v1

    • [cs.LG]Effectiveness of Adversarial Examples and Defenses for Malware Classification
    Robert Podschwadt, Hassan Takabi
    http://arxiv.org/abs/1909.04778v1

    • [cs.LG]FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency
    Kacper Sokol, Raul Santos-Rodriguez, Peter Flach
    http://arxiv.org/abs/1909.05167v1

    • [cs.LG]Factorized MultiClass Boosting
    Igor E. Kuralenok, Yurii Rebryk, Ruslan Solovev, Anton Ermilov
    http://arxiv.org/abs/1909.04904v1

    • [cs.LG]Few-Shot Classification on Unseen Domains by Learning Disparate Modulators
    Yongseok Choi, Junyoung Park, Subin Yi, Dong-Yeon Cho
    http://arxiv.org/abs/1909.04999v1

    • [cs.LG]First Analysis of Local GD on Heterogeneous Data
    Ahmed Khaled, Konstantin Mishchenko, Peter Richtárik
    http://arxiv.org/abs/1909.04715v1

    • [cs.LG]Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
    Yang Li, José M. F. Moura
    http://arxiv.org/abs/1909.04019v2

    • [cs.LG]GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks
    Ruud van Deursen, Peter Ertl, Igor V. Tetko, Guillaume Godin
    http://arxiv.org/abs/1909.04825v1

    • [cs.LG]Gradient Descent with Compressed Iterates
    Ahmed Khaled, Peter Richtárik
    http://arxiv.org/abs/1909.04716v1

    • [cs.LG]Group Representation Theory for Knowledge Graph Embedding
    Chen Cai
    http://arxiv.org/abs/1909.05100v1

    • [cs.LG]How to detect novelty in textual data streams? A comparative study of existing methods
    Clément Christophe, Julien Velcin, Jairo Cugliari, Philippe Suignard, Manel Boumghar
    http://arxiv.org/abs/1909.05099v1

    • [cs.LG]Identifying and Resisting Adversarial Videos Using Temporal Consistency
    Xiaojun Jia, Xingxing Wei, Xiaochun Cao
    http://arxiv.org/abs/1909.04837v1

    • [cs.LG]Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks
    Jiaxiang Tang, Wei Hu, Xiang Gao, Zongming Guo
    http://arxiv.org/abs/1909.04931v1

    • [cs.LG]LazyBum: Decision tree learning using lazy propositionalization
    Jonas Schouterden, Jesse Davis, Hendrik Blockeel
    http://arxiv.org/abs/1909.05044v1

    • [cs.LG]Learning Vector-valued Functions with Local Rademacher Complexity
    Jian Li, Yong Liu, Weiping Wang
    http://arxiv.org/abs/1909.04883v1

    • [cs.LG]Learning to Propagate for Graph Meta-Learning
    Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
    http://arxiv.org/abs/1909.05024v1

    • [cs.LG]Localized Adversarial Training for Increased Accuracy and Robustness in Image Classification
    Eitan Rothberg, Tingting Chen, Luo Jie, Hao Ji
    http://arxiv.org/abs/1909.04779v1

    • [cs.LG]Logarithmic Regret for Online Control
    Naman Agarwal, Elad Hazan, Karan Singh
    http://arxiv.org/abs/1909.05062v1

    • [cs.LG]Multi-Year Vector Dynamic Time Warping Based Crop Mapping
    Mustafa Teke, Yasemin Yardımcı
    http://arxiv.org/abs/1909.04930v1

    • [cs.LG]Neural Belief Reasoner
    Haifeng Qian
    http://arxiv.org/abs/1909.04719v1

    • [cs.LG]On weighted uncertainty sampling in active learning
    Vinay Jethava
    http://arxiv.org/abs/1909.04928v1

    • [cs.LG]Optimal Machine Intelligence Near the Edge of Chaos
    Ling Feng, Choy Heng Lai
    http://arxiv.org/abs/1909.05176v1

    • [cs.LG]Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls
    Jose F Rodrigues-Jr, Gabriel Spadon, Bruno Brandoli, Sihem Amer-Yahia
    http://arxiv.org/abs/1909.04605v2

    • [cs.LG]Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning
    Arpan Kusari, Jonathan P. How
    http://arxiv.org/abs/1909.05004v1

    • [cs.LG]RecSim: A Configurable Simulation Platform for Recommender Systems
    Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, Craig Boutilier
    http://arxiv.org/abs/1909.04847v1

    • [cs.LG]Recognizing Variables from their Data via Deep Embeddings of Distributions
    Jonas Mueller, Alex Smola
    http://arxiv.org/abs/1909.04844v1

    • [cs.LG]Reinforcement Learning and Video Games
    Yue Zheng
    http://arxiv.org/abs/1909.04751v1

    • [cs.LG]Safe Policy Improvement with an Estimated Baseline Policy
    Thiago D. Simão, Romain Laroche, Rémi Tachet des Combes
    http://arxiv.org/abs/1909.05236v1

    • [cs.LG]Sparse and Imperceivable Adversarial Attacks
    Francesco Croce, Matthias Hein
    http://arxiv.org/abs/1909.05040v1

    • [cs.LG]Structural Robustness for Deep Learning Architectures
    Carlos Lassance, Vincent Gripon, Jian Tang, Antonio Ortega
    http://arxiv.org/abs/1909.05095v1

    • [cs.LG]Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization
    Alireza Ghods, Diane J Cook
    http://arxiv.org/abs/1909.04791v1

    • [cs.LG]Towards Noise-Robust Neural Networks via Progressive Adversarial Training
    Hang Yu, Aishan Liu, Xianglong Liu, Jichen Yang, Chongzhi Zhang
    http://arxiv.org/abs/1909.04839v1

    • [cs.LG]Towards Understanding the Importance of Shortcut Connections in Residual Networks
    Tianyi Liu, Minshuo Chen, Mo Zhou, Simon S. Du, Enlu Zhou, Tuo Zhao
    http://arxiv.org/abs/1909.04653v2

    • [cs.LG]When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies
    Zheyu Yan, Yiyu Shi, Wang Liao, Masanori Hashimoto, Xichuan Zhou, Cheng Zhuo
    http://arxiv.org/abs/1909.04697v1

    • [cs.MA]On Memory Mechanism in Multi-Agent Reinforcement Learning
    Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah
    http://arxiv.org/abs/1909.05232v1

    • [cs.MA]Signal Instructed Coordination in Team Competition
    Liheng Chen, Hongyi Guo, Haifeng Zhang, Fei Fang, Yaoming Zhu, Ming Zhou, Weinan Zhang, Qing Wang, Yong Yu
    http://arxiv.org/abs/1909.04224v1

    • [cs.NE]Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators using Time Compression Supporting Multiple Spike Codes
    Changqing Xu, Wenrui Zhang, Yu Liu, Peng Li
    http://arxiv.org/abs/1909.04757v1

    • [cs.NE]Covariance Matrix Adaptation Greedy Search Applied to Water Distribution System Optimization
    Mehdi Neshat, Bradley Alexander, Angus Simpson
    http://arxiv.org/abs/1909.04846v1

    • [cs.PL]Static Analysis for Probabilistic Programs
    Ryan Bernstein
    http://arxiv.org/abs/1909.05076v1

    • [cs.RO]3D traffic flow model for UAVs
    Mirmojtaba Gharibi, Raouf Boutaba, Steven L. Waslander
    http://arxiv.org/abs/1909.04838v1

    • [cs.RO]A Deep Learning Approach to Grasping the Invisible
    Yang Yang, Hengyue Liang, Changhyun Choi
    http://arxiv.org/abs/1909.04840v1

    • [cs.RO]A Lightweight and Accurate Localization Algorithm Using Multiple Inertial Measurement Units
    Ming Zhang, Yiming Chen, Xiangyu Xu, Mingyang Li
    http://arxiv.org/abs/1909.04869v1

    • [cs.RO]Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
    Abulikemu Abuduweili, Siyan Li, Changliu Liu
    http://arxiv.org/abs/1909.05089v1

    • [cs.RO]Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
    Sulabh Kumra, Shirin Joshi, Ferat Sahin
    http://arxiv.org/abs/1909.04810v1

    • [cs.RO]Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control
    Carlos Mastalli, Rohan Budhiraja, Wolfgang Merkt, Guilhem Saurel, Bilal Hammoud, Maximilien Naveau, Justin Carpentier, Sethu Vijayakumar, Nicolas Mansard
    http://arxiv.org/abs/1909.04947v1

    • [cs.RO]Emotional Distraction for Children Anxiety Reduction During Vaccination
    Martina Ruocco, Marwa Larafa, Silvia Rossi
    http://arxiv.org/abs/1909.04961v1

    • [cs.RO]Four-Arm Manipulation via Feet Interfaces
    Jacob Hernandez Sanchez, Walid Amanhoud, Anaïs Haget, Hannes Bleuler, Aude Billard, Mohamed Bouri
    http://arxiv.org/abs/1909.04993v1

    • [cs.RO]Human-robot Collaborative Navigation Search using Social Reward Sources
    Marc Dalmasso, Anaís Garrell, Pablo Jiménez, Alberto Sanfeliu
    http://arxiv.org/abs/1909.04768v1

    • [cs.RO]MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
    Bohan Wu, Iretiayo Akinola, Jacob Varley, Peter Allen
    http://arxiv.org/abs/1909.04787v1

    • [cs.RO]MPC-Net: A First Principles Guided Policy Search
    Jan Carius, Farbod Farshidian, Marco Hutter
    http://arxiv.org/abs/1909.05197v1

    • [cs.RO]Motion Planning Explorer: Visualizing Local Minima using a Local-Minima Tree
    Andreas Orthey, Benjamin Frész, Marc Toussaint
    http://arxiv.org/abs/1909.05035v1

    • [cs.RO]On-Demand Trajectory Predictions for Interaction Aware Highway Driving
    Cyrus Anderson, Ram Vasudevan, Matthew Johnson-Roberson
    http://arxiv.org/abs/1909.05227v1

    • [cs.RO]On-line collision avoidance for collaborative robot manipulators by adjusting off-line generated paths: An industrial use case
    Mohammad Safeea, Pedro Neto, Richard Bearee
    http://arxiv.org/abs/1909.05159v1

    • [cs.RO]Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning
    Carlos E. Luis, Marijan Vukosavljev, Angela P. Schoellig
    http://arxiv.org/abs/1909.05150v1

    • [cs.RO]Probabilistic Model Learning and Long-term Prediction for Contact-rich Manipulation Tasks
    Shahbaz Abdul Khader, Hang Yin, Pietro Falco, Danica Kragic
    http://arxiv.org/abs/1909.04915v1

    • [cs.RO]Proceedings of the AI-HRI Symposium at AAAI-FSS 2019
    Justin W. Hart, Nick DePalma, Richard G. Freedman, Luca Iocchi, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Elin A. Topp, Tom Williams
    http://arxiv.org/abs/1909.04812v1

    • [cs.RO]SwarmMesh: A Distributed Data Structure for Cooperative Multi-Robot Applications
    Nathalie Majcherczyk, Carlo Pinciroli
    http://arxiv.org/abs/1909.04905v1

    • [cs.SD]Computer Assisted Composition in Continuous Time
    Chamin Hewa Koneputugodage, Rhys Healy, Sean Lamont, Ian Mallett, Matt Brown, Matt Walters, Ushini Attanayake, Libo Zhang, Roger T. Dean, Alexander Hunter, Charles Gretton, Christian Walder
    http://arxiv.org/abs/1909.05030v1

    • [econ.EM]Virtual Historical Simulation for estimating the conditional VaR of large portfolios
    Christian Francq, Jean-Michel Zakoian
    http://arxiv.org/abs/1909.04661v1

    • [eess.IV]CEREBRuM: a Convolutional Encoder-decodeR for Fully Volumetric Fast sEgmentation of BRain MRI
    Dennis Bontempi, Sergio Benini, Alberto Signoroni, Michele Svanera, Lars Muckli
    http://arxiv.org/abs/1909.05085v1

    • [eess.IV]Hybrid Cascaded Neural Network for Liver Lesion Segmentation
    Raunak Dey, Yi Hong
    http://arxiv.org/abs/1909.04797v1

    • [eess.IV]Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks
    Piotr Woznicki, Przemyslaw Przybyszewski, Norbert Kapinski, Jakub Zielinski, Beata Ciszkowska-Lyson, Bartosz A. Borucki, Tomasz Trzcinski, Krzysztof S. Nowinski
    http://arxiv.org/abs/1909.04973v1

    • [eess.IV]Multi-stage domain adversarial style reconstruction for cytopathological image stain normalization
    Xihao Chen, Jingya Yu, Li Chen, Shaoqun Zeng, Xiuli Liu, Shenghua Cheng
    http://arxiv.org/abs/1909.05184v1

    • [eess.IV]Variable Rate Deep Image Compression With a Conditional Autoencoder
    Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
    http://arxiv.org/abs/1909.04802v1

    • [eess.SP]Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems
    Gui Zhou, Cunhua Pan, Hong Ren, Kezhi Wang, Wei Xu, Arumugam Nallanathan
    http://arxiv.org/abs/1909.04606v1

    • [eess.SP]Minimization of Sum Inverse Energy Efficiency for Multiple Base Station Systems
    Zijian Wang, Luc Vandendorpe, Mateen Ashraf, Yuting Mou, Nafiseh Janatian
    http://arxiv.org/abs/1909.04355v1

    • [eess.SY]Deep Reinforcement Learning Algorithm for Dynamic Pricing of Express Lanes with Multiple Access Locations
    Venktesh Pandey, Evana Wang, Stephen D. Boyles
    http://arxiv.org/abs/1909.04760v1

    • [eess.SY]Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games
    David Fridovich-Keil, Ellis Ratner, Jennifer C. Shih, Anca D. Dragan, Claire J. Tomlin
    http://arxiv.org/abs/1909.04694v1

    • [eess.SY]Functional Principal Component Analysis as a Versatile Technique to Understand and Predict the Electric Consumption Patterns
    Davide Beretta, Samuele Grillo, Davide Pigoli, Enea Bionda, Claudio Bossi, Carlo Tornelli
    http://arxiv.org/abs/1909.05237v1

    • [eess.SY]Towards Safe Machine Learning for CPS: Infer Uncertainty from Training Data
    Xiaozhe Gu, Arvind Easwaran
    http://arxiv.org/abs/1909.04886v1

    • [math.CO]Coding for Sunflowers
    Anup Rao
    http://arxiv.org/abs/1909.04774v1

    • [math.OC]Distributed Deep Learning with Event-Triggered Communication
    Jemin George, Prudhvi Gurram
    http://arxiv.org/abs/1909.05020v1

    • [math.PR]Unified $\ell_{2\rightarrow\infty}$ Eigenspace Perturbation Theory for Symmetric Random Matrices
    Lihua Lei
    http://arxiv.org/abs/1909.04798v1

    • [math.ST]Aggregated Hold-Out
    Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle
    http://arxiv.org/abs/1909.04890v1

    • [math.ST]Bayesian Inference on Volatility in the Presence of Infinite Jump Activity and Microstructure Noise
    Qi Wang, José E. Figueroa-López, Todd Kuffner
    http://arxiv.org/abs/1909.04853v1

    • [math.ST]Distorted stochastic dominance: a generalized family of stochastic orders
    Tommaso Lando, Lucio Bertoli-Barsotti
    http://arxiv.org/abs/1909.04767v1

    • [math.ST]Optimality of the Subgradient Algorithm in the Stochastic Setting
    Daron Anderson, Douglas Leith
    http://arxiv.org/abs/1909.05007v1

    • [math.ST]Targeted Random Projection for Prediction from High-Dimensional Features
    Minerva Mukhopadhyay, David B. Dunson
    http://arxiv.org/abs/1909.05117v1

    • [physics.ao-ph]Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz ‘96 Model
    David John Gagne II, Hannah M. Christensen, Aneesh C. Subramanian, Adam H. Monahan
    http://arxiv.org/abs/1909.04711v1

    • [physics.soc-ph]Clusters and the entropy in opinion dynamics on complex networks
    Wenchen Han, Yuee Feng, Xiaolan Qian, Qihui Yang, Changwei Huang
    http://arxiv.org/abs/1909.04843v1

    • [q-bio.PE]Boltzmann machine learning and regularization methods for inferring evolutionary fields and couplings from a multiple sequence alignment
    Sanzo Miyazawa
    http://arxiv.org/abs/1909.05006v1

    • [q-bio.QM]Reconstructing continuously heterogeneous structures from single particle cryo-EM with deep generative models
    Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger
    http://arxiv.org/abs/1909.05215v1

    • [q-fin.ST]Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning
    Samuel Showalter, Jeffrey Gropp
    http://arxiv.org/abs/1909.05151v1

    • [quant-ph]A Quantum Search Decoder for Natural Language Processing
    Johannes Bausch, Sathyawageeswar Subramanian, Stephen Piddock
    http://arxiv.org/abs/1909.05023v1

    • [stat.AP]A sub-critical branching process model for application to analysing Y haplotype DNA mixtures
    Robert G. Cowell
    http://arxiv.org/abs/1909.04926v1

    • [stat.AP]Design-adherent estimators for network surveys
    Steve Thompson
    http://arxiv.org/abs/1909.05018v1

    • [stat.AP]Home Sweet Home: Quantifying Home Court Advantages For NCAA Basketball Statistics
    Matthew van Bommel, Luke Bornn, Peter Chow-White, Chuancong Gao
    http://arxiv.org/abs/1909.04817v1

    • [stat.CO]Efficient Bayesian synthetic likelihood with whitening transformations
    Jacob W. Priddle, Scott A. Sisson, Christopher Drovandi
    http://arxiv.org/abs/1909.04857v1

    • [stat.ME]Covariate Adaptive False Discovery Rate Control with Applications to Omics-Wide Multiple Testing
    Xianyang Zhang, Jun Chen
    http://arxiv.org/abs/1909.04811v1

    • [stat.ME]Insights and algorithms for the multivariate square-root lasso
    Aaron J. Molstad
    http://arxiv.org/abs/1909.05041v1

    • [stat.ME]Regression to the Mean’s Impact on the Synthetic Control Method: Bias and Sensitivity Analysis
    Nicholas Illenberger, Dylan S. Small, Pamela A. Shaw
    http://arxiv.org/abs/1909.04706v1

    • [stat.ME]Robust Regression with Compositional Covariates
    Aditya Mishra, Christian L. Muller
    http://arxiv.org/abs/1909.04990v1

    • [stat.ML]Anomaly Detection with Inexact Labels
    Tomoharu Iwata, Machiko Toyoda, Shotaro Tora, Naonori Ueda
    http://arxiv.org/abs/1909.04807v1

    • [stat.ML]Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging
    Luis Muñoz-González, Kenneth T. Co, Emil C. Lupu
    http://arxiv.org/abs/1909.05125v1

    • [stat.ML]Correcting Predictions for Approximate Bayesian Inference
    Tomasz Kuśmierczyk, Joseph Sakaya, Arto Klami
    http://arxiv.org/abs/1909.04919v1

    • [stat.ML]Goodness-of-fit tests on manifolds
    Alexander Shapiro, Yao Xie, Rui Zhang
    http://arxiv.org/abs/1909.05229v1

    • [stat.ML]Implicit Regularization for Optimal Sparse Recovery
    Tomas Vaškevičius, Varun Kanade, Patrick Rebeschini
    http://arxiv.org/abs/1909.05122v1

    • [stat.ML]Practical Calculation of Gittins Indices for Multi-armed Bandits
    James Edwards
    http://arxiv.org/abs/1909.05075v1

    • [stat.ML]Regularized deep learning with a non-convex penalty
    Sujit Vettam, Majnu John
    http://arxiv.org/abs/1909.05142v1