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.DB - 数据库 cs.DC - 分布式、并行与集群计算 cs.DL - 数字图书馆 cs.GT - 计算机科学与博弈论 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.SI - 社交网络与信息网络 cs.SY - 系统与控制 econ.EM - 计量经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.AC - 交换代数 math.OC - 优化与控制 math.ST - 统计理论 q-bio.NC - 神经元与认知 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]Glancing Through Massive Binary Radio Lenses: Hardware-Aware Interferometry With 1-Bit Sensors
• [cs.AI]A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
• [cs.AI]Approaching Adaptation Guided Retrieval in Case-Based Reasoning through Inference in Undirected Graphical Models
• [cs.AI]Asymptotically Unambitious Artificial General Intelligence
• [cs.AI]Exploiting Persona Information for Diverse Generation of Conversational Responses
• [cs.AI]Guarantees for Sound Abstractions for Generalized Planning (Extended Paper)
• [cs.AI]Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
• [cs.CL]Anti-efficient encoding in emergent communication
• [cs.CL]Automatic Ambiguity Detection
• [cs.CL]Defending Against Neural Fake News
• [cs.CL]Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
• [cs.CL]Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
• [cs.CL]Learning Multilingual Word Embeddings Using Image-Text Data
• [cs.CL]Learning Task-specific Representation for Novel Words in Sequence Labeling
• [cs.CL]Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
• [cs.CL]Racial Bias in Hate Speech and Abusive Language Detection Datasets
• [cs.CL]Revision in Continuous Space: Fine-Grained Control of Text Style Transfer
• [cs.CL]SEMA: an Extended Semantic Evaluation Metric for AMR
• [cs.CL]Target-Guided Open-Domain Conversation
• [cs.CL]TopExNet: Entity-Centric Network Topic Exploration in News Streams
• [cs.CL]Towards better substitution-based word sense induction
• [cs.CL]VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019
• [cs.CL]Word-order biases in deep-agent emergent communication
• [cs.CR]ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks
• [cs.CR]Automatically Dismantling Online Dating Fraud
• [cs.CR]On Evaluating the Effectiveness of the HoneyBot: A Case Study
• [cs.CV]Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs
• [cs.CV]CGaP: Continuous Growth and Pruning for Efficient Deep Learning
• [cs.CV]Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation
• [cs.CV]Coherent Semantic Attention for Image Inpainting
• [cs.CV]Compositional Convolutional Networks For Robust Object Classification under Occlusion
• [cs.CV]Disentangling Monocular 3D Object Detection
• [cs.CV]Flat2Layout: Flat Representation for Estimating Layout of General Room Types
• [cs.CV]GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
• [cs.CV]Hierarchical Feature Aggregation Networks for Video Action Recognition
• [cs.CV]Image Alignment in Unseen Domains via Domain Deep Generalization
• [cs.CV]Image-to-Image Translation with Multi-Path Consistency Regularization
• [cs.CV]Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images
• [cs.CV]Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
• [cs.CV]KG-GAN: Knowledge-Guided Generative Adversarial Networks
• [cs.CV]Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification
• [cs.CV]Leveraging Medical Visual Question Answering with Supporting Facts
• [cs.CV]Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition
• [cs.CV]NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds
• [cs.CV]OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
• [cs.CV]Probabilistic Category-Level Pose Estimation via Segmentation and Predicted-Shape Priors
• [cs.CV]Smooth Shells: Multi-Scale Shape Registration with Functional Maps
• [cs.CV]Super Interaction Neural Network
• [cs.CV]Texture CNN for Histopathological Image Classification
• [cs.CV]Texture CNN for Thermoelectric Metal Pipe Image Classification
• [cs.CV]Video-to-Video Translation for Visual Speech Synthesis
• [cs.CV]Vision-to-Language Tasks Based on Attributes and Attention Mechanism
• [cs.CV]Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
• [cs.CY]A Platform to Collect, Unify, and Distribute Inertial Labeled Signals for Human Activity Recognition
• [cs.CY]Algorithmic Bias and the Biases of the Bias Catchers
• [cs.CY]Complexity Analysis of Approaching Clinical Psychiatry with Predictive Analytics and Neural Networks
• [cs.CY]Data Breach e-Crime, A Case Study and Legal Analysis
• [cs.CY]Exploiting Cognitive Structure for Adaptive Learning
• [cs.CY]Food for thought: Ethical considerations of user trust in computer vision
• [cs.CY]Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter
• [cs.DB]Designing and Implementing Data Warehouse for Agricultural Big Data
• [cs.DC]Energy Efficiency Features of the Intel Skylake-SP Processor and Their Impact on Performance
• [cs.DC]Putting Strong Linearizability in Context: Preserving Hyperproperties in Programs that Use Concurrent Objects
• [cs.DC]Read-Uncommitted Transactions for Smart Contract Performance
• [cs.DC]The Impact of RDMA on Agreement
• [cs.DL]Using Micro-collections in Social Media to Generate Seeds for Web Archive Collections
• [cs.GT]Heuristics in Multi-Winner Approval Voting
• [cs.HC]Minimizing Time-to-Rank: A Learning and Recommendation Approach
• [cs.IR]Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
• [cs.IR]NRPA: Neural Recommendation with Personalized Attention
• [cs.IR]Predicting next shopping stage using Google Analytics data for E-commerce applications
• [cs.IT]A Method of Expressing the Magnitude of Merit of Being Able to Access a Side Information During Encoding
• [cs.LG]A Control-Model-Based Approach for Reinforcement Learning
• [cs.LG]A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning
• [cs.LG]A New Distribution on the Simplex with Auto-Encoding Applications
• [cs.LG]A Simple Saliency Method That Passes the Sanity Checks
• [cs.LG]A Study of BFLOAT16 for Deep Learning Training
• [cs.LG]A Topology Layer for Machine Learning
• [cs.LG]Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
• [cs.LG]Adaptive Deep Kernel Learning
• [cs.LG]Address Instance-level Label Prediction in Multiple Instance Learning
• [cs.LG]Adversarial Imitation Learning from Incomplete Demonstrations
• [cs.LG]An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
• [cs.LG]An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
• [cs.LG]An Inertial Newton Algorithm for Deep Learning
• [cs.LG]An Investigation of Data Poisoning Defenses for Online Learning
• [cs.LG]Analyzing the Interpretability Robustness of Self-Explaining Models
• [cs.LG]Approximate Guarantees for Dictionary Learning
• [cs.LG]Are Disentangled Representations Helpful for Abstract Visual Reasoning?
• [cs.LG]Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting
• [cs.LG]Brain-inspired reverse adversarial examples
• [cs.LG]Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
• [cs.LG]Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
• [cs.LG]Certifiably Robust Interpretation in Deep Learning
• [cs.LG]Complex-valued neural networks for machine learning on non-stationary physical data
• [cs.LG]Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning
• [cs.LG]Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
• [cs.LG]Differential Privacy Has Disparate Impact on Model Accuracy
• [cs.LG]Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?
• [cs.LG]Dimension Reduction Approach for Interpretability of Sequence to Sequence Recurrent Neural Networks
• [cs.LG]EM Converges for a Mixture of Many Linear Regressions
• [cs.LG]Efficient EM-Variational Inference for Hawkes Process
• [cs.LG]Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
• [cs.LG]Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
• [cs.LG]Fast and Robust Rank Aggregation against Model Misspecification
• [cs.LG]Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
• [cs.LG]Flexible Mining of Prefix Sequences from Time-Series Traces
• [cs.LG]From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone
• [cs.LG]Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
• [cs.LG]G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
• [cs.LG]GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
• [cs.LG]Generation of Policy-Level Explanations for Reinforcement Learning
• [cs.LG]Generative Parameter Sampler For Scalable Uncertainty Quantification
• [cs.LG]Graph Convolutional Modules for Traffic Forecasting
• [cs.LG]Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
• [cs.LG]Harnessing Slow Dynamics in Neuromorphic Computation
• [cs.LG]Improved Generalisation Bounds for Deep Learning Through $L^\infty$ Covering Numbers
• [cs.LG]Instant Quantization of Neural Networks using Monte Carlo Methods
• [cs.LG]Learning Bayesian Networks with Low Rank Conditional Probability Tables
• [cs.LG]Learning Portable Representations for High-Level Planning
• [cs.LG]Learning the Non-linearity in Convolutional Neural Networks
• [cs.LG]Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings
• [cs.LG]Limitations of the Empirical Fisher Approximation
• [cs.LG]Meta-Learning Representations for Continual Learning
• [cs.LG]Minimizing approximately submodular functions
• [cs.LG]Misleading Authorship Attribution of Source Code using Adversarial Learning
• [cs.LG]Mixed Precision Training With 8-bit Floating Point
• [cs.LG]Model Similarity Mitigates Test Set Overuse
• [cs.LG]Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
• [cs.LG]Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
• [cs.LG]Nyström landmark sampling and regularized Christoffel functions
• [cs.LG]On the Expected Dynamics of Nonlinear TD Learning
• [cs.LG]On the Expressive Power of Deep Polynomial Neural Networks
• [cs.LG]On the equivalence between graph isomorphism testing and function approximation with GNNs
• [cs.LG]Pre-training Graph Neural Networks
• [cs.LG]Privacy Amplification by Mixing and Diffusion Mechanisms
• [cs.LG]Private Causal Inference using Propensity Scores
• [cs.LG]Probabilistic Decoupling of Labels in Classification
• [cs.LG]Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
• [cs.LG]Rethinking Full Connectivity in Recurrent Neural Networks
• [cs.LG]SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
• [cs.LG]SECRET: Semantically Enhanced Classification of Real-world Tasks
• [cs.LG]Scalable and transferable learning of algorithms via graph embedding for multi-robot reward collection
• [cs.LG]Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems
• [cs.LG]Size-free generalization bounds for convolutional neural networks
• [cs.LG]Solving graph compression via optimal transport
• [cs.LG]SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
• [cs.LG]Stabilizing GANs with Octave Convolutions
• [cs.LG]Strategic Prediction with Latent Aggregative Games
• [cs.LG]Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations
• [cs.LG]Understanding Generalization of Deep Neural Networks Trained with Noisy Labels
• [cs.LG]Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
• [cs.LG]Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
• [cs.LG]Weakly-paired Cross-Modal Hashing
• [cs.LG]Where is the Information in a Deep Neural Network?
• [cs.MA]Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use
• [cs.NE]Attention Based Pruning for Shift Networks
• [cs.NE]Composing Neural Algorithms with Fugu
• [cs.PL]Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support
• [cs.RO]CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failures
• [cs.RO]LeTS-Drive: Driving in a Crowd by Learning from Tree Search
• [cs.RO]Learning Navigation Subroutines by Watching Videos
• [cs.RO]ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle
• [cs.RO]Planning with State Abstractions for Non-Markovian Task Specifications
• [cs.RO]Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework
• [cs.SI]Understanding the Effectiveness of Data Reduction in Public Transportation Networks
• [cs.SY]Research on fuzzy PID Shared control method of small brain-controlled uav
• [econ.EM]Centered and non-centered variance inflation factor
• [econ.EM]Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions
• [eess.AS]Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect
• [eess.IV]Application of Different Simulated Spectral Data and Machine Learning to Estimate the Chlorophyll $a$ Concentration of Several Inland Waters
• [eess.IV]Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
• [eess.IV]Image Denoising with Graph-Convolutional Neural Networks
• [eess.IV]Segmentation of blood vessels in retinal fundus images
• [eess.IV]Towards Real Scene Super-Resolution with Raw Images
• [eess.SP]Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
• [eess.SP]Statistical Modeling of the FSO Fronthaul Channel for UAV-based Networks
• [eess.SP]The Meta Distributions of the SIR/SNR and Data Rate in Coexisting Sub-6GHz and Millimeter-wave Cellular Networks
• [math.AC]On the generalized Hamming weights of certain Reed-Muller-type codes
• [math.OC]A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with Outliers
• [math.OC]A unified variance-reduced accelerated gradient method for convex optimization
• [math.OC]Accelerated Sparsified SGD with Error Feedback
• [math.OC]Global Guarantees for Blind Demodulation with Generative Priors
• [math.ST]Array-RQMC for option pricing under stochastic volatility models
• [math.ST]Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
• [math.ST]On Some Resampling Procedures with the Empirical Beta Copula
• [math.ST]Rank-one Multi-Reference Factor Analysis
• [math.ST]The cost-free nature of optimally tuning Tikhonov regularizers and other ordered smoothers
• [math.ST]The spiked matrix model with generative priors
• [math.ST]Tight Recovery Guarantees for Orthogonal Matching Pursuit Under Gaussian Noise
• [q-bio.NC]NeoGuard: a public, online learning platform for neonatal seizures
• [q-bio.NC]Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
• [q-bio.NC]Using local plasticity rules to train recurrent neural networks
• [stat.AP]Calculating the Expected Value of Sample Information in Practice: Considerations from Three Case Studies
• [stat.CO]Gradients do grow on trees: a linear-time ${\cal O}\hspace{-0.2em}\left( N \right)$-dimensional gradient for statistical phylogenetics
• [stat.ME]Bayesian Dynamic Fused LASSO
• [stat.ME]Bayesian Inference for Polya Inverse Gamma Models
• [stat.ME]Topological Techniques in Model Selection
• [stat.ML]AdaOja: Adaptive Learning Rates for Streaming PCA
• [stat.ML]Bayesian Anomaly Detection Using Extreme Value Theory
• [stat.ML]Bayesian Nonparametric Federated Learning of Neural Networks
• [stat.ML]Deep Factors for Forecasting
• [stat.ML]Deep Generalized Method of Moments for Instrumental Variable Analysis
• [stat.ML]Discovering Conditionally Salient Features with Statistical Guarantees
• [stat.ML]Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
• [stat.ML]Extra-gradient with player sampling for provable fast convergence in n-player games
• [stat.ML]Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
• [stat.ML]Hijacking Malaria Simulators with Probabilistic Programming
• [stat.ML]How to iron out rough landscapes and get optimal performances: Replicated Gradient Descent and its application to tensor PCA
• [stat.ML]Lifelong Bayesian Optimization
• [stat.ML]Multi-task Learning in Deep Gaussian Processes with Multi-kernel Layers
• [stat.ML]On the Inductive Bias of Neural Tangent Kernels
• [stat.ML]Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
• [stat.ML]Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
• [stat.ML]Switching Linear Dynamics for Variational Bayes Filtering
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• [astro-ph.IM]Glancing Through Massive Binary Radio Lenses: Hardware-Aware Interferometry With 1-Bit Sensors
Manuel S. Stein
http://arxiv.org/abs/1905.12528v1
• [cs.AI]A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
Frank van Harmelen, Annette ten Teije
http://arxiv.org/abs/1905.12389v1
• [cs.AI]Approaching Adaptation Guided Retrieval in Case-Based Reasoning through Inference in Undirected Graphical Models
Luigi Portinale
http://arxiv.org/abs/1905.12464v1
• [cs.AI]Asymptotically Unambitious Artificial General Intelligence
Michael K Cohen, Badri Vellambi, Marcus Hutter
http://arxiv.org/abs/1905.12186v1
• [cs.AI]Exploiting Persona Information for Diverse Generation of Conversational Responses
Haoyu Song, Wei-Nan Zhang, Yiming Cui, Dong Wang, Ting Liu
http://arxiv.org/abs/1905.12188v1
• [cs.AI]Guarantees for Sound Abstractions for Generalized Planning (Extended Paper)
Blai Bonet, Raquel Fuentetaja, Yolanda E-Martin, Daniel Borrajo
http://arxiv.org/abs/1905.12071v1
• [cs.AI]Stay on the Path: Instruction Fidelity in Vision-and-Language Navigation
Vihan Jain, Gabriel Magalhaes, Alex Ku, Ashish Vaswani, Eugene Ie, Jason Baldridge
http://arxiv.org/abs/1905.12255v1
• [cs.CL]Anti-efficient encoding in emergent communication
Rahma Chaabouni, Eugene Kharitonov, Emmanuel Dupoux, Marco Baroni
http://arxiv.org/abs/1905.12561v1
• [cs.CL]Automatic Ambiguity Detection
Richard Sproat, Jan van Santen
http://arxiv.org/abs/1905.12065v1
• [cs.CL]Defending Against Neural Fake News
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, Yejin Choi
http://arxiv.org/abs/1905.12616v1
• [cs.CL]Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation
Jiangjie Chen, Ao Wang, Haiyun Jiang, Suo Feng, Chenguang Li, Yanghua Xiao
http://arxiv.org/abs/1905.12198v1
• [cs.CL]Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
Naoyuki Kanda, Christoph Boeddeker, Jens Heitkaemper, Yusuke Fujita, Shota Horiguchi, Kenji Nagamatsu, Reinhold Haeb-Umbach
http://arxiv.org/abs/1905.12230v1
• [cs.CL]Learning Multilingual Word Embeddings Using Image-Text Data
Karan Singhal, Karthik Raman, Balder ten Cate
http://arxiv.org/abs/1905.12260v1
• [cs.CL]Learning Task-specific Representation for Novel Words in Sequence Labeling
Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Jinlan Fu, Xuanjing Huang
http://arxiv.org/abs/1905.12277v1
• [cs.CL]Parallax: Visualizing and Understanding the Semantics of Embedding Spaces via Algebraic Formulae
Piero Molino, Yang Wang, Jiawei Zhang
http://arxiv.org/abs/1905.12099v1
• [cs.CL]Racial Bias in Hate Speech and Abusive Language Detection Datasets
Thomas Davidson, Debasmita Bhattacharya, Ingmar Weber
http://arxiv.org/abs/1905.12516v1
• [cs.CL]Revision in Continuous Space: Fine-Grained Control of Text Style Transfer
Dayiheng Liu, Jie Fu, Yidan Zhang, Chris Pal, Jiancheng Lv
http://arxiv.org/abs/1905.12304v1
• [cs.CL]SEMA: an Extended Semantic Evaluation Metric for AMR
Rafael T. Anchieta, Marco A. S. Cabezudo, Thiago A. S. Pardo
http://arxiv.org/abs/1905.12069v1
• [cs.CL]Target-Guided Open-Domain Conversation
Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, Xiaodan Liang, Eric P. Xing, Zhiting Hu
http://arxiv.org/abs/1905.11553v2
• [cs.CL]TopExNet: Entity-Centric Network Topic Exploration in News Streams
Andreas Spitz, Satya Almasian, Michael Gertz
http://arxiv.org/abs/1905.12335v1
• [cs.CL]Towards better substitution-based word sense induction
Asaf Amrami, Yoav Goldberg
http://arxiv.org/abs/1905.12598v1
• [cs.CL]VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019
Andros Tjandra, Berrak Sisman, Mingyang Zhang, Sakriani Sakti, Haizhou Li, Satoshi Nakamura
http://arxiv.org/abs/1905.11449v2
• [cs.CL]Word-order biases in deep-agent emergent communication
Rahma Chaabouni, Eugene Kharitonov, Alessandro Lazaric, Emmanuel Dupoux, Marco Baroni
http://arxiv.org/abs/1905.12330v1
• [cs.CR]ATTACK2VEC: Leveraging Temporal Word Embeddings to Understand the Evolution of Cyberattacks
Yun Shen, Gianluca Stringhini
http://arxiv.org/abs/1905.12590v1
• [cs.CR]Automatically Dismantling Online Dating Fraud
Guillermo Suarez-Tangil, Matthew Edwards, Claudia Peersman, Gianluca Stringhini, Awais Rashid, Monica Whitty
http://arxiv.org/abs/1905.12593v1
• [cs.CR]On Evaluating the Effectiveness of the HoneyBot: A Case Study
Celine Irvene, David Formby, Raheem Beyah
http://arxiv.org/abs/1905.12061v1
• [cs.CV]Blocksworld Revisited: Learning and Reasoning to Generate Event-Sequences from Image Pairs
Tejas Gokhale, Shailaja Sampat, Zhiyuan Fang, Yezhou Yang, Chitta Baral
http://arxiv.org/abs/1905.12042v1
• [cs.CV]CGaP: Continuous Growth and Pruning for Efficient Deep Learning
Xiaocong Du, Zheng Li, Yu Cao
http://arxiv.org/abs/1905.11533v2
• [cs.CV]Closed-Loop Adaptation for Weakly-Supervised Semantic Segmentation
Zhengqiang Zhang, Shujian Yu, Shi Yin, Qinmu Peng, Xinge You
http://arxiv.org/abs/1905.12190v1
• [cs.CV]Coherent Semantic Attention for Image Inpainting
Hongyu Liu, Bin Jiang, Yi Xiao, Chao Yang
http://arxiv.org/abs/1905.12384v1
• [cs.CV]Compositional Convolutional Networks For Robust Object Classification under Occlusion
Adam Kortylewski, Qing Liu, Huiyu Wang, Zhishuai Zhang, Alan Yuille
http://arxiv.org/abs/1905.11826v2
• [cs.CV]Disentangling Monocular 3D Object Detection
Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, Peter Kontschieder
http://arxiv.org/abs/1905.12365v1
• [cs.CV]Flat2Layout: Flat Representation for Estimating Layout of General Room Types
Chi-Wei Hsiao, Cheng Sun, Min Sun, Hwann-Tzong Chen
http://arxiv.org/abs/1905.12571v1
• [cs.CV]GlyphGAN: Style-Consistent Font Generation Based on Generative Adversarial Networks
Hideaki Hayashi, Kohtaro Abe, Seiichi Uchida
http://arxiv.org/abs/1905.12502v1
• [cs.CV]Hierarchical Feature Aggregation Networks for Video Action Recognition
Swathikiran Sudhakaran, Sergio Escalera, Oswald Lanz
http://arxiv.org/abs/1905.12462v1
• [cs.CV]Image Alignment in Unseen Domains via Domain Deep Generalization
Thanh-Dat Truong, Khoa Luu, Chi-Nhanh Duong, Ngan Le, Minh-Triet Tran
http://arxiv.org/abs/1905.12028v1
• [cs.CV]Image-to-Image Translation with Multi-Path Consistency Regularization
Jianxin Lin, Yingce Xia, Yijun Wang, Tao Qin, Zhibo Chen
http://arxiv.org/abs/1905.12498v1
• [cs.CV]Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images
Henrik Grønholt Jensen, François Lauze, Sune Darkner
http://arxiv.org/abs/1905.12056v1
• [cs.CV]Instance-Aware Representation Learning and Association for Online Multi-Person Tracking
Hefeng Wu, Yafei Hu, Keze Wang, Hanhui Li, Lin Nie, Hui Cheng
http://arxiv.org/abs/1905.12409v1
• [cs.CV]KG-GAN: Knowledge-Guided Generative Adversarial Networks
Che-Han Chang, Chun-Hsien Yu, Szu-Ying Chen, Edward Y. Chang
http://arxiv.org/abs/1905.12261v1
• [cs.CV]Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification
Zhao Zhang, Lei Jia, Mingbo Zhao, Guangcan Liu, Meng Wang, Shuicheng Yan
http://arxiv.org/abs/1905.12236v1
• [cs.CV]Leveraging Medical Visual Question Answering with Supporting Facts
Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade, Alexis Asseman, Ahmet S. Ozcan
http://arxiv.org/abs/1905.12008v1
• [cs.CV]Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition
Dylan C. Tannugi, Alceu S. Britto Jr., Alessandro L. Koerich
http://arxiv.org/abs/1905.12082v1
• [cs.CV]NPTC-net: Narrow-Band Parallel Transport Convolutional Neural Network on Point Clouds
Pengfei Jin, Tianhao Lai, Rongjie Lai, Bin Dong
http://arxiv.org/abs/1905.12218v1
• [cs.CV]OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks
Jiashi Li, Qi Qi, Jingyu Wang, Ce Ge, Yujian Li, Zhangzhang Yue, Haifeng Sun
http://arxiv.org/abs/1905.11664v2
• [cs.CV]Probabilistic Category-Level Pose Estimation via Segmentation and Predicted-Shape Priors
Benjamin Burchfiel, George Konidaris
http://arxiv.org/abs/1905.12079v1
• [cs.CV]Smooth Shells: Multi-Scale Shape Registration with Functional Maps
Marvin Eisenberger, Zorah Lähner, Daniel Cremers
http://arxiv.org/abs/1905.12512v1
• [cs.CV]Super Interaction Neural Network
Yang Yao, Xu Zhang, Baile Xu, Furao Shen, Jian Zhao
http://arxiv.org/abs/1905.12349v1
• [cs.CV]Texture CNN for Histopathological Image Classification
Jonathan de Matos, Alceu de S. Britto Jr., Luiz E. S. de Oliveira, Alessandro L. Koerich
http://arxiv.org/abs/1905.12005v1
• [cs.CV]Texture CNN for Thermoelectric Metal Pipe Image Classification
Daniel Vriesman, Alessandro Zimmer, Alceu S. Britto Jr., Alessandro L. Koerich
http://arxiv.org/abs/1905.12003v1
• [cs.CV]Video-to-Video Translation for Visual Speech Synthesis
Michail C. Doukas, Viktoriia Sharmanska, Stefanos Zafeiriou
http://arxiv.org/abs/1905.12043v1
• [cs.CV]Vision-to-Language Tasks Based on Attributes and Attention Mechanism
Xuelong Li, Aihong Yuan, Xiaoqiang Lu
http://arxiv.org/abs/1905.12243v1
• [cs.CV]Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello
http://arxiv.org/abs/1905.12162v1
• [cs.CY]A Platform to Collect, Unify, and Distribute Inertial Labeled Signals for Human Activity Recognition
Anna Ferrari, Daniela Micucci, Marco Mobilio, Paolo Napoletano
http://arxiv.org/abs/1905.12555v1
• [cs.CY]Algorithmic Bias and the Biases of the Bias Catchers
David Rozado
http://arxiv.org/abs/1905.11985v1
• [cs.CY]Complexity Analysis of Approaching Clinical Psychiatry with Predictive Analytics and Neural Networks
Soaad Hossain
http://arxiv.org/abs/1905.12471v1
• [cs.CY]Data Breach e-Crime, A Case Study and Legal Analysis
Samuel Cris Ayo
http://arxiv.org/abs/1905.12472v1
• [cs.CY]Exploiting Cognitive Structure for Adaptive Learning
Qi Liu, Shiwei Tong, Chuanren Liu, Hongke Zhao, Enhong Chen, Haiping Ma, Shijin Wang
http://arxiv.org/abs/1905.12470v1
• [cs.CY]Food for thought: Ethical considerations of user trust in computer vision
Kaylen J. Pfisterer, Jennifer Boger, Alexander Wong
http://arxiv.org/abs/1905.12487v1
• [cs.CY]Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter
Francois Modave, Yunpeng Zhao, Janice Krieger, Zhe He, Yi Guo, Jinhai Huo, Mattia Prosperi, Jiang Bian
http://arxiv.org/abs/1905.12469v1
• [cs.DB]Designing and Implementing Data Warehouse for Agricultural Big Data
Vuong M. Ngo, Nhien-An Le-Khac, M-Tahar Kechadi
http://arxiv.org/abs/1905.12411v1
• [cs.DC]Energy Efficiency Features of the Intel Skylake-SP Processor and Their Impact on Performance
Robert Schöne, Thomas Ilsche, Mario Bielert, Andreas Gocht, Daniel Hackenberg
http://arxiv.org/abs/1905.12468v1
• [cs.DC]Putting Strong Linearizability in Context: Preserving Hyperproperties in Programs that Use Concurrent Objects
Hagit Attiya, Constantin Enea
http://arxiv.org/abs/1905.12063v1
• [cs.DC]Read-Uncommitted Transactions for Smart Contract Performance
Victor Cook, Zachary Painter, Christina Peterson, Damian Dechev
http://arxiv.org/abs/1905.12351v1
• [cs.DC]The Impact of RDMA on Agreement
Marcos K. Aguilera, Naama Ben-David, Rachid Guerraoui, Virendra Marathe, Igor Zablotchi
http://arxiv.org/abs/1905.12143v1
• [cs.DL]Using Micro-collections in Social Media to Generate Seeds for Web Archive Collections
Alexander C. Nwala, Michele C. Weigle, Michael L. Nelson
http://arxiv.org/abs/1905.12220v1
• [cs.GT]Heuristics in Multi-Winner Approval Voting
Jaelle Scheuerman, Jason L. Harman, Nicholas Mattei, K. Brent Venable
http://arxiv.org/abs/1905.12104v1
• [cs.HC]Minimizing Time-to-Rank: A Learning and Recommendation Approach
Haoming Li, Sujoy Sikdar, Rohit Vaish, Junming Wang, Lirong Xia, Chaonan Ye
http://arxiv.org/abs/1905.11984v1
• [cs.IR]Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction
Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang
http://arxiv.org/abs/1905.11900v2
• [cs.IR]NRPA: Neural Recommendation with Personalized Attention
Hongtao Liu, Fangzhao Wu, Wenjun Wang, Xianchen Wang, Pengfei Jiao, Chuhan Wu, Xing Xie
http://arxiv.org/abs/1905.12480v1
• [cs.IR]Predicting next shopping stage using Google Analytics data for E-commerce applications
Mihai Cristian Pîrvu, Alexandra Anghel
http://arxiv.org/abs/1905.12595v1
• [cs.IT]A Method of Expressing the Magnitude of Merit of Being Able to Access a Side Information During Encoding
Kiminori Iriyama
http://arxiv.org/abs/1905.12315v1
• [cs.LG]A Control-Model-Based Approach for Reinforcement Learning
Yingdong Lu, Mark S. Squillante, Chai Wah Wu
http://arxiv.org/abs/1905.12009v1
• [cs.LG]A Heuristic for Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Nicholas Watters, Loic Matthey, Christopher P. Burgess, Alexander Lerchner, Irina Higgins
http://arxiv.org/abs/1905.12614v1
• [cs.LG]A New Distribution on the Simplex with Auto-Encoding Applications
Andrew Stirn, Tony Jebara, David A Knowles
http://arxiv.org/abs/1905.12052v1
• [cs.LG]A Simple Saliency Method That Passes the Sanity Checks
Arushi Gupta, Sanjeev Arora
http://arxiv.org/abs/1905.12152v1
• [cs.LG]A Study of BFLOAT16 for Deep Learning Training
Dhiraj Kalamkar, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, Nataraj Jammalamadaka, Jianyu Huang, Hector Yuen, Jiyan Yang, Jongsoo Park, Alexander Heinecke, Evangelos Georganas, Sudarshan Srinivasan, Abhisek Kundu, Misha Smelyanskiy, Bharat Kaul, Pradeep Dubey
http://arxiv.org/abs/1905.12322v1
• [cs.LG]A Topology Layer for Machine Learning
Rickard Brüel-Gabrielsson, Bradley J. Nelson, Anjan Dwaraknath, Primoz Skraba, Leonidas J. Guibas, Gunnar Carlsson
http://arxiv.org/abs/1905.12200v1
• [cs.LG]Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
Yufei Cui, Wuguannan Yao, Qiao Li, Antoni B. Chan, Chun Jason Xue
http://arxiv.org/abs/1905.12194v1
• [cs.LG]Adaptive Deep Kernel Learning
Prudencio Tossou, Basile Dura, Francois Laviolette, Mario Marchand, Alexandre Lacoste
http://arxiv.org/abs/1905.12131v1
• [cs.LG]Address Instance-level Label Prediction in Multiple Instance Learning
Minlong Peng, Qi Zhang
http://arxiv.org/abs/1905.12226v1
• [cs.LG]Adversarial Imitation Learning from Incomplete Demonstrations
Mingfei Sun, Xiaojuan Ma
http://arxiv.org/abs/1905.12310v1
• [cs.LG]An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network
Thanh Tung Khuat, Fang Chen, Bogdan Gabrys
http://arxiv.org/abs/1905.12170v1
• [cs.LG]An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient
Pan Xu, Felicia Gao, Quanquan Gu
http://arxiv.org/abs/1905.12615v1
• [cs.LG]An Inertial Newton Algorithm for Deep Learning
Camille Castera, Jérôme Bolte, Cédric Févotte, Edouard Pauwels
http://arxiv.org/abs/1905.12278v1
• [cs.LG]An Investigation of Data Poisoning Defenses for Online Learning
Yizhen Wang, Kamalika Chaudhuri
http://arxiv.org/abs/1905.12121v1
• [cs.LG]Analyzing the Interpretability Robustness of Self-Explaining Models
Haizhong Zheng, Earlence Fernandes, Atul Prakash
http://arxiv.org/abs/1905.12429v1
• [cs.LG]Approximate Guarantees for Dictionary Learning
Aditya Bhaskara, Wai Ming Tai
http://arxiv.org/abs/1905.12091v1
• [cs.LG]Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber, Olivier Bachem
http://arxiv.org/abs/1905.12506v1
• [cs.LG]Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting
Adriana-Simona Mihaita, Zheyuan Liu, Chen Cai, Marian-Andrei Rizoiu
http://arxiv.org/abs/1905.12254v1
• [cs.LG]Brain-inspired reverse adversarial examples
Shaokai Ye, Sia Huat Tan, Kaidi Xu, Yanzhi Wang, Chenglong Bao, Kaisheng Ma
http://arxiv.org/abs/1905.12171v1
• [cs.LG]Calibrated Surrogate Maximization of Linear-fractional Utility in Binary Classification
Han Bao, Masashi Sugiyama
http://arxiv.org/abs/1905.12511v1
• [cs.LG]Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model
Chang Li, Maarten de Rijke
http://arxiv.org/abs/1905.12370v1
• [cs.LG]Certifiably Robust Interpretation in Deep Learning
Alexander Levine, Sahil Singla, Soheil Feizi
http://arxiv.org/abs/1905.12105v1
• [cs.LG]Complex-valued neural networks for machine learning on non-stationary physical data
Jesper Sören Dramsch, Mikael Lüthje, Anders Nymark Christensen
http://arxiv.org/abs/1905.12321v1
• [cs.LG]Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning
Shariq Iqbal, Fei Sha
http://arxiv.org/abs/1905.12127v1
• [cs.LG]Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, Eric Mjolsness
http://arxiv.org/abs/1905.12122v1
• [cs.LG]Differential Privacy Has Disparate Impact on Model Accuracy
Eugene Bagdasaryan, Vitaly Shmatikov
http://arxiv.org/abs/1905.12101v1
• [cs.LG]Differential Privacy for Multi-armed Bandits: What Is It and What Is Its Cost?
Debabrota Basu, Christos Dimitrakakis, Aristide Tossou
http://arxiv.org/abs/1905.12298v1
• [cs.LG]Dimension Reduction Approach for Interpretability of Sequence to Sequence Recurrent Neural Networks
Kun Su, Eli Shlizerman
http://arxiv.org/abs/1905.12176v1
• [cs.LG]EM Converges for a Mixture of Many Linear Regressions
Jeongyeol Kwon, Constantine Caramanis
http://arxiv.org/abs/1905.12106v1
• [cs.LG]Efficient EM-Variational Inference for Hawkes Process
Feng Zhou
http://arxiv.org/abs/1905.12251v1
• [cs.LG]Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness
Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans
http://arxiv.org/abs/1905.12202v1
• [cs.LG]Exploring Interpretable LSTM Neural Networks over Multi-Variable Data
Tian Guo, Tao Lin, Nino Antulov-Fantulin
http://arxiv.org/abs/1905.12034v1
• [cs.LG]Fast and Robust Rank Aggregation against Model Misspecification
Yuangang Pan, Weijie Chen, Gang Niu, Ivor W. Tsang, Masashi Sugiyama
http://arxiv.org/abs/1905.12341v1
• [cs.LG]Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks
Pu Zhao, Siyue Wang, Cheng Gongye, Yanzhi Wang, Yunsi Fei, Xue Lin
http://arxiv.org/abs/1905.12032v1
• [cs.LG]Flexible Mining of Prefix Sequences from Time-Series Traces
Antonio Anastasio Bruto da Costa, Goran Frehse, Pallab Dasgupta
http://arxiv.org/abs/1905.12262v1
• [cs.LG]From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone
Pekka Siirtola, Heli Koskimäki, Juha Röning
http://arxiv.org/abs/1905.12285v1
• [cs.LG]Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones
Guichen Zhang, Pedram Ghamisi, Xiao Xiang Zhu
http://arxiv.org/abs/1905.12305v1
• [cs.LG]G2R Bound: A Generalization Bound for Supervised Learning from GAN-Synthetic Data
Fu-Chieh Chang, Hao-Jen Wang, Chun-Nan Chou, Edward Y. Chang
http://arxiv.org/abs/1905.12313v1
• [cs.LG]GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany, Yves Moreau
http://arxiv.org/abs/1905.12374v1
• [cs.LG]Generation of Policy-Level Explanations for Reinforcement Learning
Nicholay Topin, Manuela Veloso
http://arxiv.org/abs/1905.12044v1
• [cs.LG]Generative Parameter Sampler For Scalable Uncertainty Quantification
Minsuk Shin, Young Lee, Jun S. Liu
http://arxiv.org/abs/1905.12440v1
• [cs.LG]Graph Convolutional Modules for Traffic Forecasting
Kyungeun Lee, Wonjong Rhee
http://arxiv.org/abs/1905.12256v1
• [cs.LG]Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
Liwei Wu, Hsiang-Fu Yu, Nikhil Rao, James Sharpnack, Cho-Jui Hsieh
http://arxiv.org/abs/1905.12217v1
• [cs.LG]Harnessing Slow Dynamics in Neuromorphic Computation
Tianlin Liu
http://arxiv.org/abs/1905.12116v1
• [cs.LG]Improved Generalisation Bounds for Deep Learning Through $L^\infty$ Covering Numbers
Antoine Ledent, Yunwen Lei, Marius Kloft
http://arxiv.org/abs/1905.12430v1
• [cs.LG]Instant Quantization of Neural Networks using Monte Carlo Methods
Gonçalo Mordido, Matthijs Van Keirsbilck, Alexander Keller
http://arxiv.org/abs/1905.12253v1
• [cs.LG]Learning Bayesian Networks with Low Rank Conditional Probability Tables
Adarsh Barik, Jean Honorio
http://arxiv.org/abs/1905.12552v1
• [cs.LG]Learning Portable Representations for High-Level Planning
Steven James, Benjamin Rosman, George Konidaris
http://arxiv.org/abs/1905.12006v1
• [cs.LG]Learning the Non-linearity in Convolutional Neural Networks
Gavneet Singh Chadha, Andreas Schwung
http://arxiv.org/abs/1905.12337v1
• [cs.LG]Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings
Angel Daruna, Weiyu Liu, Zsolt Kira, Sonia Chernova
http://arxiv.org/abs/1905.12181v1
• [cs.LG]Limitations of the Empirical Fisher Approximation
Frederik Kunstner, Lukas Balles, Philipp Hennig
http://arxiv.org/abs/1905.12558v1
• [cs.LG]Meta-Learning Representations for Continual Learning
Khurram Javed, Martha White
http://arxiv.org/abs/1905.12588v1
• [cs.LG]Minimizing approximately submodular functions
Marwa El Halabi, Stefanie Jegelka
http://arxiv.org/abs/1905.12145v1
• [cs.LG]Misleading Authorship Attribution of Source Code using Adversarial Learning
Erwin Quiring, Alwin Maier, Konrad Rieck
http://arxiv.org/abs/1905.12386v1
• [cs.LG]Mixed Precision Training With 8-bit Floating Point
Naveen Mellempudi, Sudarshan Srinivasan, Dipankar Das, Bharat Kaul
http://arxiv.org/abs/1905.12334v1
• [cs.LG]Model Similarity Mitigates Test Set Overuse
Horia Mania, John Miller, Ludwig Schmidt, Moritz Hardt, Benjamin Recht
http://arxiv.org/abs/1905.12580v1
• [cs.LG]Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities
Aristide Tossou, Debabrota Basu, Christos Dimitrakakis
http://arxiv.org/abs/1905.12425v1
• [cs.LG]Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
Giancarlo Kerg, Kyle Goyette, Maximilian Puelma Touzel, Gauthier Gidel, Eugene Vorontsov, Yoshua Bengio, Guillaume Lajoie
http://arxiv.org/abs/1905.12080v1
• [cs.LG]Nyström landmark sampling and regularized Christoffel functions
Michaël Fanuel, Joachim Schreurs, Johan A. K. Suykens
http://arxiv.org/abs/1905.12346v1
• [cs.LG]On the Expected Dynamics of Nonlinear TD Learning
David Brandfonbrener, Joan Bruna
http://arxiv.org/abs/1905.12185v1
• [cs.LG]On the Expressive Power of Deep Polynomial Neural Networks
Joe Kileel, Matthew Trager, Joan Bruna
http://arxiv.org/abs/1905.12207v1
• [cs.LG]On the equivalence between graph isomorphism testing and function approximation with GNNs
Zhengdao Chen, Soledad Villar, Lei Chen, Joan Bruna
http://arxiv.org/abs/1905.12560v1
• [cs.LG]Pre-training Graph Neural Networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec
http://arxiv.org/abs/1905.12265v1
• [cs.LG]Privacy Amplification by Mixing and Diffusion Mechanisms
Borja Balle, Gilles Barthe, Marco Gaboardi, Joseph Geumlek
http://arxiv.org/abs/1905.12264v1
• [cs.LG]Private Causal Inference using Propensity Scores
Si Kai Lee, Luigi Gresele, Mijung Park, Krikamol Muandet
http://arxiv.org/abs/1905.12592v1
• [cs.LG]Probabilistic Decoupling of Labels in Classification
Jeppe Nørregaard, Lars Kai Hansen
http://arxiv.org/abs/1905.12403v1
• [cs.LG]Reinforcement Learning with Policy Mixture Model for Temporal Point Processes Clustering
Weichang Wu, Junchi Yan, Xiaokang Yang, Hongyuan Zha
http://arxiv.org/abs/1905.12345v1
• [cs.LG]Rethinking Full Connectivity in Recurrent Neural Networks
Matthijs Van Keirsbilck, Alexander Keller, Xiaodong Yang
http://arxiv.org/abs/1905.12340v1
• [cs.LG]SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver
Po-Wei Wang, Priya L. Donti, Bryan Wilder, Zico Kolter
http://arxiv.org/abs/1905.12149v1
• [cs.LG]SECRET: Semantically Enhanced Classification of Real-world Tasks
Ayten Ozge Akmandor, Jorge Ortiz, Irene Manotas, Bongjun Ko, Niraj K. Jha
http://arxiv.org/abs/1905.12356v1
• [cs.LG]Scalable and transferable learning of algorithms via graph embedding for multi-robot reward collection
Hyunwook Kang, Aydar Mynbay, James R. Morrison, Jinkyoo Park
http://arxiv.org/abs/1905.12204v1
• [cs.LG]Single neuron-based neural networks are as efficient as dense deep neural networks in binary and multi-class recognition problems
Yassin Khalifa, Justin Hawks, Ervin Sejdic
http://arxiv.org/abs/1905.12135v1
• [cs.LG]Size-free generalization bounds for convolutional neural networks
Philip M. Long, Hanie Sedghi
http://arxiv.org/abs/1905.12600v1
• [cs.LG]Solving graph compression via optimal transport
Vikas K. Garg, Tommi Jaakkola
http://arxiv.org/abs/1905.12158v1
• [cs.LG]SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
http://arxiv.org/abs/1905.12107v1
• [cs.LG]Stabilizing GANs with Octave Convolutions
Ricard Durall, Franz-Josef Pfreundt, Janis Keuper
http://arxiv.org/abs/1905.12534v1
• [cs.LG]Strategic Prediction with Latent Aggregative Games
Vikas K. Garg, Tommi Jaakkola
http://arxiv.org/abs/1905.12169v1
• [cs.LG]Targeted Attacks on Deep Reinforcement Learning Agents through Adversarial Observations
Léonard Hussenot, Matthieu Geist, Olivier Pietquin
http://arxiv.org/abs/1905.12282v1
• [cs.LG]Understanding Generalization of Deep Neural Networks Trained with Noisy Labels
Wei Hu, Zhiyuan Li, Dingli Yu
http://arxiv.org/abs/1905.11368v2
• [cs.LG]Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition
Martin Mundt, Sagnik Majumder, Iuliia Pliushch, Visvanathan Ramesh
http://arxiv.org/abs/1905.12019v1
• [cs.LG]Using Ontologies To Improve Performance In Massively Multi-label Prediction Models
Ethan Steinberg, Peter J. Liu
http://arxiv.org/abs/1905.12126v1
• [cs.LG]Weakly-paired Cross-Modal Hashing
Xuanwu Liu, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang
http://arxiv.org/abs/1905.12203v1
• [cs.LG]Where is the Information in a Deep Neural Network?
Alessandro Achille, Stefano Soatto
http://arxiv.org/abs/1905.12213v1
• [cs.MA]Robo-Taxi service fleet sizing: assessing the impact of user trust and willingness-to-use
Reza Vosooghi, Joseph Kamel, Jakob Puchinger, Vincent Leblond, Marija Jankovic
http://arxiv.org/abs/1905.12267v1
• [cs.NE]Attention Based Pruning for Shift Networks
Ghouthi Boukli Hacene, Carlos Lassance, Vincent Gripon, Matthieu Courbariaux, Yoshua Bengio
http://arxiv.org/abs/1905.12300v1
• [cs.NE]Composing Neural Algorithms with Fugu
James B Aimone, William Severa, Craig M Vineyard
http://arxiv.org/abs/1905.12130v1
• [cs.PL]Categorization of Program Regions for Agile Compilation using Machine Learning and Hardware Support
Sanket Tavarageri
http://arxiv.org/abs/1905.12292v1
• [cs.RO]CARE: Cooperative Autonomy for Resilience and Efficiency of Robot Teams for Complete Coverage of Unknown Environments under Robot Failures
Junnan Song, Shalabh Gupta
http://arxiv.org/abs/1905.12191v1
• [cs.RO]LeTS-Drive: Driving in a Crowd by Learning from Tree Search
Panpan Cai, Yuanfu Luo, Aseem Saxena, David Hsu, Wee Sun Lee
http://arxiv.org/abs/1905.12197v1
• [cs.RO]Learning Navigation Subroutines by Watching Videos
Ashish Kumar, Saurabh Gupta, Jitendra Malik
http://arxiv.org/abs/1905.12612v1
• [cs.RO]ORangE: Operational Range Estimation for Mobile Robot Exploration on a Single Discharge Cycle
Kshitij Tiwari, Xuesu Xiao, Ville Kyrki, Nak Young Chong
http://arxiv.org/abs/1905.12559v1
• [cs.RO]Planning with State Abstractions for Non-Markovian Task Specifications
Yoonseon Oh, Roma Patel, Thao Nguyen, Baichuan Huang, Ellie Pavlick, Stefanie Tellex
http://arxiv.org/abs/1905.12096v1
• [cs.RO]Safety-related Tasks within the Set-Based Task-Priority Inverse Kinematics Framework
Paolo Di Lillo, Filippo Arrichiello, Gianluca Antonelli, Stefano Chiaverini
http://arxiv.org/abs/1905.12459v1
• [cs.SI]Understanding the Effectiveness of Data Reduction in Public Transportation Networks
Thomas Bläsius, Philipp Fischbeck, Tobias Friedrich, Martin Schirneck
http://arxiv.org/abs/1905.12477v1
• [cs.SY]Research on fuzzy PID Shared control method of small brain-controlled uav
Na Dong, Wen-qi Zhang, Zhong-ke Gao
http://arxiv.org/abs/1905.12240v1
• [econ.EM]Centered and non-centered variance inflation factor
Román Salmerón Gómez, Catalina García García y José García Pérez
http://arxiv.org/abs/1905.12293v1
• [econ.EM]Matching on What Matters: A Pseudo-Metric Learning Approach to Matching Estimation in High Dimensions
Gentry Johnson, Brian Quistorff, Matt Goldman
http://arxiv.org/abs/1905.12020v1
• [eess.AS]Deep-Learning-Based Audio-Visual Speech Enhancement in Presence of Lombard Effect
Daniel Michelsanti, Zheng-Hua Tan, Sigurdur Sigurdsson, Jesper Jensen
http://arxiv.org/abs/1905.12605v1
• [eess.IV]Application of Different Simulated Spectral Data and Machine Learning to Estimate the Chlorophyll $a$ Concentration of Several Inland Waters
Philipp M. Maier, Sina Keller
http://arxiv.org/abs/1905.12563v1
• [eess.IV]Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
Ali Hatamizadeh, Hamid Hosseini, Zhengyuan Liu, Steven D. Schwartz, Demetri Terzopoulos
http://arxiv.org/abs/1905.12120v1
• [eess.IV]Image Denoising with Graph-Convolutional Neural Networks
Diego Valsesia, Giulia Fracastoro, Enrico Magli
http://arxiv.org/abs/1905.12281v1
• [eess.IV]Segmentation of blood vessels in retinal fundus images
Michiel Straat, Jorrit Oosterhof
http://arxiv.org/abs/1905.12596v1
• [eess.IV]Towards Real Scene Super-Resolution with Raw Images
Xiangyu Xu, Yongrui Ma, Wenxiu Sun
http://arxiv.org/abs/1905.12156v1
• [eess.SP]Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
Nicolas Scheiner, Stefan Haag, Nils Appenrodt, Bharanidhar Duraisamy, Jürgen Dickmann, Martin Fritzsche, Bernhard Sick
http://arxiv.org/abs/1905.11987v1
• [eess.SP]Statistical Modeling of the FSO Fronthaul Channel for UAV-based Networks
Marzieh Najafi, Hedieh Ajam, Vahid Jamali, Panagiotis D. Diamantoulakis, George K. Karagiannidis, Robert Schober
http://arxiv.org/abs/1905.12424v1
• [eess.SP]The Meta Distributions of the SIR/SNR and Data Rate in Coexisting Sub-6GHz and Millimeter-wave Cellular Networks
Hazem Ibrahim, Hina Tabassum, Uyen T. Nguyen
http://arxiv.org/abs/1905.12002v1
• [math.AC]On the generalized Hamming weights of certain Reed-Muller-type codes
Manuel Gonzalez-Sarabia, Delio Jaramillo, Rafael H. Villarreal
http://arxiv.org/abs/1905.12136v1
• [math.OC]A Quaternion-based Certifiably Optimal Solution to the Wahba Problem with Outliers
Heng Yang, Luca Carlone
http://arxiv.org/abs/1905.12536v1
• [math.OC]A unified variance-reduced accelerated gradient method for convex optimization
Guanghui Lan, Zhize Li, Yi Zhou
http://arxiv.org/abs/1905.12412v1
• [math.OC]Accelerated Sparsified SGD with Error Feedback
Tomoya Murata, Taiji Suzuki
http://arxiv.org/abs/1905.12224v1
• [math.OC]Global Guarantees for Blind Demodulation with Generative Priors
Paul Hand, Babhru Joshi
http://arxiv.org/abs/1905.12576v1
• [math.ST]Array-RQMC for option pricing under stochastic volatility models
Amal Ben Abdellah, Pierre L’Ecuyer, Florian Puchhammer
http://arxiv.org/abs/1905.12062v1
• [math.ST]Multivariate Distributionally Robust Convex Regression under Absolute Error Loss
Jose Blanchet, Peter W. Glynn, Jun Yan, Zhengqing Zhou
http://arxiv.org/abs/1905.12231v1
• [math.ST]On Some Resampling Procedures with the Empirical Beta Copula
Anna Kiriliouk, Johan Segers, Hideatsu Tsukahara
http://arxiv.org/abs/1905.12466v1
• [math.ST]Rank-one Multi-Reference Factor Analysis
Yariv Aizenbud, Boris Landa, Yoel Shkolnisky
http://arxiv.org/abs/1905.12442v1
• [math.ST]The cost-free nature of optimally tuning Tikhonov regularizers and other ordered smoothers
Pierre C Bellec, Dana Yang
http://arxiv.org/abs/1905.12517v1
• [math.ST]The spiked matrix model with generative priors
Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
http://arxiv.org/abs/1905.12385v1
• [math.ST]Tight Recovery Guarantees for Orthogonal Matching Pursuit Under Gaussian Noise
Chen Amiraz, Robert Krauthgamer, Boaz Nadler
http://arxiv.org/abs/1905.12347v1
• [q-bio.NC]NeoGuard: a public, online learning platform for neonatal seizures
Amir Hossein Ansari, Perumpillichira Joseph Cherian, Alexander Caicedo, Anneleen Dereymaeker, Katrien Jansen, Leen De Wispelaere, Charlotte Dielman, Jan Vervisch, Paul Govaert, Maarten De Vos, Gunnar Naulaers, Sabine Van Huffel
http://arxiv.org/abs/1905.12382v1
• [q-bio.NC]Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias Hennig
http://arxiv.org/abs/1905.12375v1
• [q-bio.NC]Using local plasticity rules to train recurrent neural networks
Owen Marschall, Kyunghyun Cho, Cristina Savin
http://arxiv.org/abs/1905.12100v1
• [stat.AP]Calculating the Expected Value of Sample Information in Practice: Considerations from Three Case Studies
Anna Heath, Natalia R. Kunst, Christopher Jackson, Mark Strong, Fernando Alarid-Escudero, Jeremy D. Goldhaber-Fiebert, Gianluca Baio, Nicolas A. Menzies, Hawre Jalal
http://arxiv.org/abs/1905.12013v1
• [stat.CO]Gradients do grow on trees: a linear-time ${\cal O}\hspace{-0.2em}\left( N \right)$-dimensional gradient for statistical phylogenetics
Xiang Ji, Zhenyu Zhang, Andrew Holbrook, Akihiko Nishimura, Guy Baele, Andrew Rambaut, Philippe Lemey, Marc A. Suchard
http://arxiv.org/abs/1905.12146v1
• [stat.ME]Bayesian Dynamic Fused LASSO
Kaoru Irie
http://arxiv.org/abs/1905.12275v1
• [stat.ME]Bayesian Inference for Polya Inverse Gamma Models
Christopher Glynn, Jingyu He, Nicholas G. Polson, Jianeng Xu
http://arxiv.org/abs/1905.12141v1
• [stat.ME]Topological Techniques in Model Selection
Shaoxiong Hu, Hugo Maruri-Aguliar, Zixiang Ma
http://arxiv.org/abs/1905.12269v1
• [stat.ML]AdaOja: Adaptive Learning Rates for Streaming PCA
Amelia Henriksen, Rachel Ward
http://arxiv.org/abs/1905.12115v1
• [stat.ML]Bayesian Anomaly Detection Using Extreme Value Theory
Sreelekha Guggilam, S. M. Arshad Zaidi, Varun Chandola, Abani Patra
http://arxiv.org/abs/1905.12150v1
• [stat.ML]Bayesian Nonparametric Federated Learning of Neural Networks
Mikhail Yurochkin, Mayank Agarwal, Soumya Ghosh, Kristjan Greenewald, Trong Nghia Hoang, Yasaman Khazaeni
http://arxiv.org/abs/1905.12022v1
• [stat.ML]Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean Foster, Tim Januschowski
http://arxiv.org/abs/1905.12417v1
• [stat.ML]Deep Generalized Method of Moments for Instrumental Variable Analysis
Andrew Bennett, Nathan Kallus, Tobias Schnabel
http://arxiv.org/abs/1905.12495v1
• [stat.ML]Discovering Conditionally Salient Features with Statistical Guarantees
Jaime Roquero Gimenez, James Zou
http://arxiv.org/abs/1905.12177v1
• [stat.ML]Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds
http://arxiv.org/abs/1905.12090v1
• [stat.ML]Extra-gradient with player sampling for provable fast convergence in n-player games
Samy Jelassi, Carles Domingo Enrich, Damien Scieur, Arthur Mensch, Joan Bruna
http://arxiv.org/abs/1905.12363v1
• [stat.ML]Fast mixing of Metropolized Hamiltonian Monte Carlo: Benefits of multi-step gradients
Yuansi Chen, Raaz Dwivedi, Martin J. Wainwright, Bin Yu
http://arxiv.org/abs/1905.12247v1
• [stat.ML]Hijacking Malaria Simulators with Probabilistic Programming
Bradley Gram-Hansen, Christian Schröder de Witt, Tom Rainforth, Philip H. S. Torr, Yee Whye Teh, Atılım Güneş Baydin
http://arxiv.org/abs/1905.12432v1
• [stat.ML]How to iron out rough landscapes and get optimal performances: Replicated Gradient Descent and its application to tensor PCA
Giulio Biroli, Chiara Cammarota, Federico Ricci-Tersenghi
http://arxiv.org/abs/1905.12294v1
• [stat.ML]Lifelong Bayesian Optimization
Yao Zhang, James Jordon, Ahmed M. Alaa, Mihaela van der Schaar
http://arxiv.org/abs/1905.12280v1
• [stat.ML]Multi-task Learning in Deep Gaussian Processes with Multi-kernel Layers
Ayman Boustati, Richard S. Savage
http://arxiv.org/abs/1905.12407v1
• [stat.ML]On the Inductive Bias of Neural Tangent Kernels
Alberto Bietti, Julien Mairal
http://arxiv.org/abs/1905.12173v1
• [stat.ML]Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets
Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang
http://arxiv.org/abs/1905.12569v1
• [stat.ML]Semi-Supervised Learning, Causality and the Conditional Cluster Assumption
Julius von Kügelgen, Marco Loog, Alexander Mey, Bernhard Schölkopf
http://arxiv.org/abs/1905.12081v1
• [stat.ML]Switching Linear Dynamics for Variational Bayes Filtering
Philip Becker-Ehmck, Jan Peters, Patrick van der Smagt
http://arxiv.org/abs/1905.12434v1
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