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
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• [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