今日学术视野(2020.6.19)

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.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.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.RO - 机器人学 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.GN - 一般经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 eess.SP - 信号处理 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 physics.ed-ph - 物理教育 q-bio.NC - 神经元与认知 q-fin.CP -计算金融学 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

• [cs.AI]Aligning with Heterogeneous Preferences for Kidney Exchange
• [cs.AI]Conversational Neuro-Symbolic Commonsense Reasoning
• [cs.AI]Diverse Rule Sets
• [cs.AI]Logic, Probability and Action: A Situation Calculus Perspective
• [cs.CL]A Tweet-based Dataset for Company-Level Stock Return Prediction
• [cs.CL]An Exploratory Study of Argumentative Writing by Young Students: A Transformer-based Approach
• [cs.CL]Automatically Ranked Russian Paraphrase Corpus for Text Generation
• [cs.CL]Building Low-Resource NER Models Using Non-Speaker Annotation
• [cs.CL]Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
• [cs.CL]Cross-lingual Retrieval for Iterative Self-Supervised Training
• [cs.CL]EPIE Dataset: A Corpus For Possible Idiomatic Expressions
• [cs.CL]Exploiting Review Neighbors for Contextualized Helpfulness Prediction
• [cs.CL]Fine-grained Sentiment Controlled Text Generation
• [cs.CL]Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
• [cs.CL]Iterative Edit-Based Unsupervised Sentence Simplification
• [cs.CL]Modeling subjective assessments of guilt in newspaper crime narratives
• [cs.CL]On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms
• [cs.CL]Selective Question Answering under Domain Shift
• [cs.CL]The Role of Verb Semantics in Hungarian Verb-Object Order
• [cs.CR]De-Anonymizing Text by Fingerprinting Language Generation
• [cs.CR]Visor: Privacy-Preserving Video Analytics as a Cloud Service
• [cs.CV]3D Shape Reconstruction from Free-Hand Sketches
• [cs.CV]A Real-time Action Representation with Temporal Encoding and Deep Compression
• [cs.CV]A generalizable saliency map-based interpretation of model outcome
• [cs.CV]Adversarial Defense by Latent Style Transformations
• [cs.CV]Burst Photography for Learning to Enhance Extremely Dark Images
• [cs.CV]Contrastive Learning for Weakly Supervised Phrase Grounding
• [cs.CV]Cross-Correlated Attention Networks for Person Re-Identification
• [cs.CV]Deeply Learned Spectral Total Variation Decomposition
• [cs.CV]Evaluation of 3D CNN Semantic Mapping for Rover Navigation
• [cs.CV]Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
• [cs.CV]FISHING Net: Future Inference of Semantic Heatmaps In Grids
• [cs.CV]Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances
• [cs.CV]Implicit Neural Representations with Periodic Activation Functions
• [cs.CV]Intriguing generalization and simplicity of adversarially trained neural networks
• [cs.CV]LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
• [cs.CV]LSD-C: Linearly Separable Deep Clusters
• [cs.CV]Learning Sparse Masks for Efficient Image Super-Resolution
• [cs.CV]Learning Visual Commonsense for Robust Scene Graph Generation
• [cs.CV]Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction
• [cs.CV]Maximum Roaming Multi-Task Learning
• [cs.CV]MetaSDF: Meta-learning Signed Distance Functions
• [cs.CV]Mining Label Distribution Drift in Unsupervised Domain Adaptation
• [cs.CV]Mitosis Detection Under Limited Annotation: A Joint Learning Approach
• [cs.CV]Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
• [cs.CV]Multi-Subspace Neural Network for Image Recognition
• [cs.CV]Noise or Signal: The Role of Image Backgrounds in Object Recognition
• [cs.CV]On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
• [cs.CV]Probabilistic orientation estimation with matrix Fisher distributions
• [cs.CV]Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition
• [cs.CV]Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
• [cs.CV]Self-Supervised Representation Learning for Visual Anomaly Detection
• [cs.CV]Self-supervised Knowledge Distillation for Few-shot Learning
• [cs.CV]Semantic Visual Navigation by Watching YouTube Videos
• [cs.CV]Shallow Feature Based Dense Attention Network for Crowd Counting
• [cs.CV]Sketch-Guided Scenery Image Outpainting
• [cs.CV]Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• [cs.CV]Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
• [cs.CV]Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
• [cs.CV]Visual Chirality
• [cs.CV]When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous
• [cs.CV]WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild
• [cs.CY]A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data
• [cs.CY]Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context
• [cs.CY]On the environment-destructive probabilistic trends: a perceptual and behavioral study on video game players
• [cs.CY]Regulating algorithmic filtering on social media
• [cs.CY]Response by the Montreal AI Ethics Institute to the European Commission’s Whitepaper on AI
• [cs.CY]Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces
• [cs.DB]Incremental Lossless Graph Summarization
• [cs.DC]Probabilistic Models for the Execution Time in Stochastic Scheduling
• [cs.DC]Ranking and benchmarking framework for sampling algorithms on synthetic data streams
• [cs.DC]Triggerflow: Trigger-based Orchestration of Serverless Workflows
• [cs.GT]Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
• [cs.HC]$E^3$: Visual Exploration of Spatiotemporal Energy Demand
• [cs.HC]Factuality Checking in News Headlines with Eye Tracking
• [cs.IR]CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
• [cs.IR]Comparative Sentiment Analysis of App Reviews
• [cs.IR]Learning Colour Representations of Search Queries
• [cs.IT]3D Placement for Multi-UAV Relaying: An Iterative Gibbs-Sampling and Block Coordinate Descent Optimization Approach
• [cs.IT]Binary linear codes with few weights from Boolean functions
• [cs.IT]Dense and Sparse Coding: Theory and Architectures
• [cs.IT]High order low-bit Sigma-Delta quantization for fusion frames
• [cs.IT]Multilinear Algebra for Distributed Storage
• [cs.IT]Optimizing Information Freshness in Two-Way Relay Networks
• [cs.IT]Random sampling and reconstruction of concentrated signals in a reproducing kernel space
• [cs.LG]A Study of Compositional Generalization in Neural Models
• [cs.LG]AdvMind: Inferring Adversary Intent of Black-Box Attacks
• [cs.LG]Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
• [cs.LG]Analytical Probability Distributions and EM-Learning for Deep Generative Networks
• [cs.LG]Automatic Curriculum Learning through Value Disagreement
• [cs.LG]Bayesian active learning for production, a systematic study and a reusable library
• [cs.LG]Big Self-Supervised Models are Strong Semi-Supervised Learners
• [cs.LG]Categorical Normalizing Flows via Continuous Transformations
• [cs.LG]CoSE: Compositional Stroke Embeddings
• [cs.LG]Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
• [cs.LG]Constrained regret minimization for multi-criterion multi-armed bandits
• [cs.LG]Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
• [cs.LG]Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach
• [cs.LG]Decomposable Families of Itemsets
• [cs.LG]Delta Schema Network in Model-based Reinforcement Learning
• [cs.LG]Dynamic Tensor Rematerialization
• [cs.LG]Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization
• [cs.LG]Equilibrium Propagation for Complete Directed Neural Networks
• [cs.LG]Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
• [cs.LG]Feature Space Saturation during Training
• [cs.LG]FedCD: Improving Performance in non-IID Federated Learning
• [cs.LG]Fine-Grained Stochastic Architecture Search
• [cs.LG]Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations
• [cs.LG]GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
• [cs.LG]Generalising Recursive Neural Models by Tensor Decomposition
• [cs.LG]Geometry of Comparisons
• [cs.LG]Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control
• [cs.LG]How isotropic kernels learn simple invariants
• [cs.LG]Isometric Graph Neural Networks
• [cs.LG]Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
• [cs.LG]Learning What to Defer for Maximum Independent Sets
• [cs.LG]Linear Last-iterate Convergence for Matrix Games and Stochastic Games
• [cs.LG]Memory-Efficient Pipeline-Parallel DNN Training
• [cs.LG]Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
• [cs.LG]MixBoost: A Heterogeneous Boosting Machine
• [cs.LG]Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
• [cs.LG]Multipole Graph Neural Operator for Parametric Partial Differential Equations
• [cs.LG]NNC: Neural-Network Control of Dynamical Systems on Graphs
• [cs.LG]Network Diffusions via Neural Mean-Field Dynamics
• [cs.LG]Neural Anisotropy Directions
• [cs.LG]Neural Optimal Control for Representation Learning
• [cs.LG]Off-policy Bandits with Deficient Support
• [cs.LG]Opponent Modelling with Local Information Variational Autoencoders
• [cs.LG]Optimizing Grouped Convolutions on Edge Devices
• [cs.LG]Parameterized MDPs and Reinforcement Learning Problems — A Maximum Entropy Principle Based Framework
• [cs.LG]Partial Policy Iteration for L1-Robust Markov Decision Processes
• [cs.LG]Prior knowledge distillation based on financial time series
• [cs.LG]Real-Time Regression with Dividing Local Gaussian Processes
• [cs.LG]Region-based Energy Neural Network for Approximate Inference
• [cs.LG]Robust Meta-learning for Mixed Linear Regression with Small Batches
• [cs.LG]Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
• [cs.LG]Self-training Avoids Using Spurious Features Under Domain Shift
• [cs.LG]Solving Constrained CASH Problems with ADMM
• [cs.LG]Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning
• [cs.LG]StatAssist & GradBoost: A Study on Optimal INT8 Quantization-aware Training from Scratch
• [cs.LG]Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach
• [cs.LG]Task-agnostic Exploration in Reinforcement Learning
• [cs.LG]Tell Me Something I Don’t Know: Randomization Strategies for Iterative Data Mining
• [cs.LG]The Influence of Shape Constraints on the Thresholding Bandit Problem
• [cs.LG]Toward Theory of Applied Learning. What is Machine Learning?
• [cs.LG]Untangling tradeoffs between recurrence and self-attention in neural networks
• [cs.LG]Walk Message Passing Neural Networks and Second-Order Graph Neural Networks
• [cs.LG]Wasserstein Embedding for Graph Learning
• [cs.NE]Dynamic Vehicle Routing Problem: A Monte Carlo approach
• [cs.NE]Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
• [cs.NE]Learning to Learn with Feedback and Local Plasticity
• [cs.NE]On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron
• [cs.NE]Simplified Swarm Optimization for Bi-Objection Active Reliability Redundancy Allocation Problems
• [cs.NI]DCAF: A Dynamic Computation Allocation Framework for Online Serving System
• [cs.NI]Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique for Nodes Starvation Problem in Wireless Mesh Network
• [cs.NI]Leveraging the Power of Prediction: Predictive Service Placement for Latency-Sensitive Mobile Edge Computing
• [cs.RO]An Open-Source Scenario Architect for Autonomous Vehicles
• [cs.RO]Approximate Simulation for Template-Based Whole-Body Control
• [cs.RO]COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
• [cs.RO]Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
• [cs.RO]Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections
• [cs.RO]ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers
• [cs.SE]Quality Management of Machine Learning Systems
• [cs.SI]Did State-sponsored Trolls Shape the US Presidential Election Discourse? Quantifying Influence on Twitter
• [cs.SI]Scaling Choice Models of Relational Social Data
• [econ.GN]A demographic microsimulation model with an integrated household alignment method
• [eess.AS]Visual Attention for Musical Instrument Recognition
• [eess.IV]Deep Learning Meets SAR
• [eess.IV]High-Fidelity Generative Image Compression
• [eess.IV]Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling
• [eess.SP]Intelligent Protection & Classification of Transients in Two-Core Symmetric Phase Angle Regulating Transformers
• [eess.SP]Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
• [math.OC]Structured Stochastic Quasi-Newton Methods for Large-Scale Optimization Problems
• [math.OC]Variation diminishing linear time-invariant systems
• [math.PR]A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics
• [math.PR]Reverse Lebesgue and Gaussian isoperimetric inequalities for parallel sets with applications
• [math.ST]A Berry-Esseen theorem for sample quantiles under association
• [math.ST]Goodness-of-Fit Test for Self-Exciting Processes
• [math.ST]Logarithmic Voronoi cells
• [math.ST]Robust Persistence Diagrams using Reproducing Kernels
• [physics.ed-ph]Creating Experience value to build student satisfaction in higher education
• [q-bio.NC]Interpretable multimodal fusion networks reveal mechanisms of brain cognition
• [q-fin.CP]Consistent Recalibration Models and Deep Calibration
• [stat.AP]Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
• [stat.AP]CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
• [stat.AP]Using machine learning to identify nontraditional spatial dependence in occupancy data
• [stat.ME]An algorithm for non-parametric estimation in state-space models
• [stat.ME]Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes
• [stat.ME]Discussion of “On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning”
• [stat.ME]Efficient nonparametric statistical inference on population feature importance using Shapley values
• [stat.ME]Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes
• [stat.ME]Family of mean-mixtures of multivariate normal distributions: properties, inference and assessment of multivariate skewness
• [stat.ME]Multidimensional Bayesian IRT Model for Hierarchical Latent Structures
• [stat.ME]Shrinking the eigenvalues of M-estimators of covariance matrix
• [stat.ME]Wasserstein Regression
• [stat.ML]A Non-Asymptotic Analysis for Stein Variational Gradient Descent
• [stat.ML]Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
• [stat.ML]Approximate Gradient Coding with Optimal Decoding
• [stat.ML]Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
• [stat.ML]Deep Learning with Functional Inputs
• [stat.ML]Density Deconvolution with Normalizing Flows
• [stat.ML]Efficient Statistics for Sparse Graphical Models from Truncated Samples
• [stat.ML]FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated Features
• [stat.ML]GPIRT: A Gaussian Process Model for Item Response Theory
• [stat.ML]Image-on-Scalar Regression via Deep Neural Networks
• [stat.ML]Implicit regularization for convex regularizers
• [stat.ML]Interpolation and Learning with Scale Dependent Kernels
• [stat.ML]Kernel Alignment Risk Estimator: Risk Prediction from Training Data
• [stat.ML]LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
• [stat.ML]Longitudinal Variational Autoencoder
• [stat.ML]Occam’s Ghost
• [stat.ML]Regularized ERM on random subspaces
• [stat.ML]Robust compressed sensing of generative models
• [stat.ML]Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
• [stat.ML]Universally Quantized Neural Compression
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• [cs.AI]Aligning with Heterogeneous Preferences for Kidney Exchange
Rachel Freedman
http://arxiv.org/abs/2006.09519v1
• [cs.AI]Conversational Neuro-Symbolic Commonsense Reasoning
Forough Arabshahi, Jennifer Lee, Mikayla Gawarecki, Kathryn Mazaitis, Amos Azaria, Tom Mitchell
http://arxiv.org/abs/2006.10022v1
• [cs.AI]Diverse Rule Sets
Guangyi Zhang, Aristides Gionis
http://arxiv.org/abs/2006.09890v1
• [cs.AI]Logic, Probability and Action: A Situation Calculus Perspective
Vaishak Belle
http://arxiv.org/abs/2006.09868v1
• [cs.CL]A Tweet-based Dataset for Company-Level Stock Return Prediction
Karolina Sowinska, Pranava Madhyastha
http://arxiv.org/abs/2006.09723v1
• [cs.CL]An Exploratory Study of Argumentative Writing by Young Students: A Transformer-based Approach
Debanjan Ghosh, Beata Beigman Klebanov, Yi Song
http://arxiv.org/abs/2006.09873v1
• [cs.CL]Automatically Ranked Russian Paraphrase Corpus for Text Generation
Vadim Gudkov, Olga Mitrofanova, Elizaveta Filippskikh
http://arxiv.org/abs/2006.09719v1
• [cs.CL]Building Low-Resource NER Models Using Non-Speaker Annotation
Tatiana Tsygankova, Francesca Marini, Stephen Mayhew, Dan Roth
http://arxiv.org/abs/2006.09627v1
• [cs.CL]Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network
Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang
http://arxiv.org/abs/2006.09610v1
• [cs.CL]Cross-lingual Retrieval for Iterative Self-Supervised Training
Chau Tran, Yuqing Tang, Xian Li, Jiatao Gu
http://arxiv.org/abs/2006.09526v1
• [cs.CL]EPIE Dataset: A Corpus For Possible Idiomatic Expressions
Prateek Saxena, Soma Paul
http://arxiv.org/abs/2006.09479v1
• [cs.CL]Exploiting Review Neighbors for Contextualized Helpfulness Prediction
Jiahua Du, Jia Rong, Hua Wang, Yanchun Zhang
http://arxiv.org/abs/2006.09685v1
• [cs.CL]Fine-grained Sentiment Controlled Text Generation
Bidisha Samanta, Mohit Agarwal, Niloy Ganguly
http://arxiv.org/abs/2006.09891v1
• [cs.CL]Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey Nikolenko
http://arxiv.org/abs/2006.09766v1
• [cs.CL]Iterative Edit-Based Unsupervised Sentence Simplification
Dhruv Kumar, Lili Mou, Lukasz Golab, Olga Vechtomova
http://arxiv.org/abs/2006.09639v1
• [cs.CL]Modeling subjective assessments of guilt in newspaper crime narratives
Elisa Kreiss, Zijian Wang, Christopher Potts
http://arxiv.org/abs/2006.09589v1
• [cs.CL]On the Learnability of Concepts: With Applications to Comparing Word Embedding Algorithms
Adam Sutton, Nello Cristianini
http://arxiv.org/abs/2006.09896v1
• [cs.CL]Selective Question Answering under Domain Shift
Amita Kamath, Robin Jia, Percy Liang
http://arxiv.org/abs/2006.09462v1
• [cs.CL]The Role of Verb Semantics in Hungarian Verb-Object Order
Dorottya Demszky, László Kálmán, Dan Jurafsky, Beth Levin
http://arxiv.org/abs/2006.09432v1
• [cs.CR]De-Anonymizing Text by Fingerprinting Language Generation
Zhen Sun, Roei Schuster, Vitaly Shmatikov
http://arxiv.org/abs/2006.09615v1
• [cs.CR]Visor: Privacy-Preserving Video Analytics as a Cloud Service
Rishabh Poddar, Ganesh Ananthanarayanan, Srinath Setty, Stavros Volos, Raluca Ada Popa
http://arxiv.org/abs/2006.09628v1
• [cs.CV]3D Shape Reconstruction from Free-Hand Sketches
Jiayun Wang, Jierui Lin, Qian Yu, Runtao Liu, Yubei Chen, Stella X. Yu
http://arxiv.org/abs/2006.09694v1
• [cs.CV]A Real-time Action Representation with Temporal Encoding and Deep Compression
Kun Liu, Wu Liu, Huadong Ma, Mingkui Tan, Chuang Gan
http://arxiv.org/abs/2006.09675v1
• [cs.CV]A generalizable saliency map-based interpretation of model outcome
Shailja Thakur, Sebastian Fischmeister
http://arxiv.org/abs/2006.09504v1
• [cs.CV]Adversarial Defense by Latent Style Transformations
Shuo Wang, Surya Nepal, Marthie Grobler, Carsten Rudolph, Tianle Chen, Shangyu Chen
http://arxiv.org/abs/2006.09701v1
• [cs.CV]Burst Photography for Learning to Enhance Extremely Dark Images
Ahmet Serdar Karadeniz, Erkut Erdem, Aykut Erdem
http://arxiv.org/abs/2006.09845v1
• [cs.CV]Contrastive Learning for Weakly Supervised Phrase Grounding
Tanmay Gupta, Arash Vahdat, Gal Chechik, Xiaodong Yang, Jan Kautz, Derek Hoiem
http://arxiv.org/abs/2006.09920v1
• [cs.CV]Cross-Correlated Attention Networks for Person Re-Identification
Jieming Zhou, Soumava Kumar Roy, Pengfei Fang, Mehrtash Harandi, Lars Petersson
http://arxiv.org/abs/2006.09597v1
• [cs.CV]Deeply Learned Spectral Total Variation Decomposition
Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb
http://arxiv.org/abs/2006.10004v1
• [cs.CV]Evaluation of 3D CNN Semantic Mapping for Rover Navigation
Sebastiano Chiodini, Luca Torresin, Marco Pertile, Stefano Debei
http://arxiv.org/abs/2006.09761v1
• [cs.CV]Explanation-based Weakly-supervised Learning of Visual Relations with Graph Networks
Federico Baldassarre, Kevin Smith, Josephine Sullivan, Hossein Azizpour
http://arxiv.org/abs/2006.09562v1
• [cs.CV]FISHING Net: Future Inference of Semantic Heatmaps In Grids
Noureldin Hendy, Cooper Sloan, Feng Tian, Pengfei Duan, Nick Charchut, Yuesong Xie, Chuang Wang, James Philbin
http://arxiv.org/abs/2006.09917v1
• [cs.CV]Fast Object Classification and Meaningful Data Representation of Segmented Lidar Instances
Lukas Hahn, Frederik Hasecke, Anton Kummert
http://arxiv.org/abs/2006.10011v1
• [cs.CV]Implicit Neural Representations with Periodic Activation Functions
Vincent Sitzmann, J
3000
ulien N. P. Martel, Alexander W. Bergman, David B. Lindell, Gordon Wetzstein
http://arxiv.org/abs/2006.09661v1
• [cs.CV]Intriguing generalization and simplicity of adversarially trained neural networks
Chirag Agarwal, Peijie Chen, Anh Nguyen
http://arxiv.org/abs/2006.09373v1
• [cs.CV]LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB
Michael Fürst, Oliver Wasenmüller, Didier Stricker
http://arxiv.org/abs/2006.09738v1
• [cs.CV]LSD-C: Linearly Separable Deep Clusters
Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Kai Han, Andrea Vedaldi, Andrew Zisserman
http://arxiv.org/abs/2006.10039v1
• [cs.CV]Learning Sparse Masks for Efficient Image Super-Resolution
Longguang Wang, Xiaoyu Dong, Yingqian Wang, Xinyi Ying, Zaiping Lin, Wei An, Yulan Guo
http://arxiv.org/abs/2006.09603v1
• [cs.CV]Learning Visual Commonsense for Robust Scene Graph Generation
Alireza Zareian, Haoxuan You, Zhecan Wang, Shih-Fu Chang
http://arxiv.org/abs/2006.09623v1
• [cs.CV]Learning to Detect 3D Reflection Symmetry for Single-View Reconstruction
Yichao Zhou, Shichen Liu, Yi Ma
http://arxiv.org/abs/2006.10042v1
• [cs.CV]Maximum Roaming Multi-Task Learning
Lucas Pascal, Pietro Michiardi, Xavier Bost, Benoit Huet, Maria A. Zuluaga
http://arxiv.org/abs/2006.09762v1
• [cs.CV]MetaSDF: Meta-learning Signed Distance Functions
Vincent Sitzmann, Eric R. Chan, Richard Tucker, Noah Snavely, Gordon Wetzstein
http://arxiv.org/abs/2006.09662v1
• [cs.CV]Mining Label Distribution Drift in Unsupervised Domain Adaptation
Peizhao Li, Zhengming Ding, Hongfu Liu
http://arxiv.org/abs/2006.09565v1
• [cs.CV]Mitosis Detection Under Limited Annotation: A Joint Learning Approach
Pushpak Pati, Antonio Foncubierta-Rodriguez, Orcun Goksel, Maria Gabrani
http://arxiv.org/abs/2006.09772v1
• [cs.CV]Mucko: Multi-Layer Cross-Modal Knowledge Reasoning for Fact-based Visual Question Answering
Zihao Zhu, Jing Yu, Yujing Wang, Yajing Sun, Yue Hu, Qi Wu
http://arxiv.org/abs/2006.09073v2
• [cs.CV]Multi-Subspace Neural Network for Image Recognition
Chieh-Ning Fang, Chin-Teng Lin
http://arxiv.org/abs/2006.09618v1
• [cs.CV]Noise or Signal: The Role of Image Backgrounds in Object Recognition
Kai Xiao, Logan Engstrom, Andrew Ilyas, Aleksander Madry
http://arxiv.org/abs/2006.09994v1
• [cs.CV]On the Inference of Soft Biometrics from Typing Patterns Collected in a Multi-device Environment
Vishaal Udandarao, Mohit Agrawal, Rajesh Kumar, Rajiv Ratn Shah
http://arxiv.org/abs/2006.09501v1
• [cs.CV]Probabilistic orientation estimation with matrix Fisher distributions
D. Mohlin, G. Bianchi, J. Sullivan
http://arxiv.org/abs/2006.09740v1
• [cs.CV]Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognition
Zhaoqiang Xia, Wei Peng, Huai-Qian Khor, Xiaoyi Feng, Guoying Zhao
http://arxiv.org/abs/2006.09674v1
• [cs.CV]Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues
Jianrong Wang, Ge Zhang, Zhenyu Wu, XueWei Li, Li Liu
http://arxiv.org/abs/2006.09876v1
• [cs.CV]Self-Supervised Representation Learning for Visual Anomaly Detection
Rabia Ali, Muhammad Umar Karim Khan, Chong Min Kyung
http://arxiv.org/abs/2006.09654v1
• [cs.CV]Self-supervised Knowledge Distillation for Few-shot Learning
Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Mubarak Shah
http://arxiv.org/abs/2006.09785v1
• [cs.CV]Semantic Visual Navigation by Watching YouTube Videos
Matthew Chang, Arjun Gupta, Saurabh Gupta
http://arxiv.org/abs/2006.10034v1
• [cs.CV]Shallow Feature Based Dense Attention Network for Crowd Counting
Yunqi Miao, Zijia Lin, Guiguang Ding, Jungong Han
http://arxiv.org/abs/2006.09853v1
• [cs.CV]Sketch-Guided Scenery Image Outpainting
Yaxiong Wang, Yunchao Wei, Xueming Qian, Li Zhu, Yi Yang
http://arxiv.org/abs/2006.09788v1
• [cs.CV]Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, Armand Joulin
http://arxiv.org/abs/2006.09882v1
• [cs.CV]Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets
Roozbeh Yousefzadeh, Furong Huang
http://arxiv.org/abs/2006.09879v1
• [cs.CV]Vision-Aided Dynamic Blockage Prediction for 6G Wireless Communication Networks
Gouranga Charan, Muhammad Alrabeiah, Ahmed Alkhateeb
http://arxiv.org/abs/2006.09902v1
• [cs.CV]Visual Chirality
Zhiqiu Lin, Jin Sun, Abe Davis, Noah Snavely
http://arxiv.org/abs/2006.09512v1
• [cs.CV]When We First Met: Visual-Inertial Person Localization for Co-Robot Rendezvous
Xi Sun, Xinshuo Weng, Kris Kitani
http://arxiv.org/abs/2006.09959v1
• [cs.CV]WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild
Rita Pucci, Jitendra Shankaraiah, Devcharan Jathanna, Ullas Karanth, Kartic Subr
http://arxiv.org/abs/2006.09962v1
• [cs.CY]A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data
Mofeng Yang, Yixuan Pan, Aref Darzi, Sepehr Ghader, Chenfeng Xiong, Lei Zhang
http://arxiv.org/abs/2006.10036v1
• [cs.CY]Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context
Donald Martin Jr., Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, William S. Isaac
http://arxiv.org/abs/2006.09663v1
• [cs.CY]On the environment-destructive probabilistic trends: a perceptual and behavioral study on video game players
Quan-Hoang Vuong, Manh-Toan Ho, Minh-Hoang Nguyen, Thanh-Hang Pham, Hoang-Anh Ho, Thu-Trang Vuong, Viet-Phuong La
http://arxiv.org/abs/2006.09706v1
• [cs.CY]Regulating algorithmic filtering on social media
Sarah H. Cen, Devavrat Shah
http://arxiv.org/abs/2006.09647v1
• [cs.CY]Response by the Montreal AI Ethics Institute to the European Commission’s Whitepaper on AI
Abhishek Gupta, Camylle Lanteigne
http://arxiv.org/abs/2006.09428v1
• [cs.CY]Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces
Tapeesh Sood, Patrick Janssen, Clayton Miller
http://arxiv.org/abs/2006.09570v1
• [cs.DB]Incremental Lossless Graph Summarization
Jihoon Ko, Yunbum Kook, Kijung Shin
http://arxiv.org/abs/2006.09935v1
• [cs.DC]Probabilistic Models for the Execution Time in Stochastic Scheduling
Matheus Henrique Junqueira Saldanha
http://arxiv.org/abs/2006.09864v1
• [cs.DC]Ranking and benchmarking framework for sampling algorithms on synthetic data streams
József Dániel Gáspár, Martin Horváth, Győző Horváth, Zoltán Zvara
http://arxiv.org/abs/2006.09895v1
• [cs.DC]Triggerflow: Trigger-based Orchestration of Serverless Workflows
Pedro García-López, Aitor Arjona, Josep Sampe, Aleksander Slominski, Lionel Villard
http://arxiv.org/abs/2006.08654v2
• [cs.GT]Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
Rui Yan, Xiaoming Duan, Zongying Shi, Yisheng Zhong, Jason R. Marden, Francesco Bullo
http://arxiv.org/abs/2006.09585v1
• [cs.HC]$E^3$: Visual Exploration of Spatiotemporal Energy Demand
Junqi Wu, Zhibin Niu, Jing Wu, Xiufeng Liu, Jiawan Zhang
http://arxiv.org/abs/2006.09487v1
• [cs.HC]Factuality Checking in News Headlines with Eye Tracking
Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Birger Larsen, Stephen Alstrup, Christina Lioma
http://arxiv.org/abs/2006.09736v1
• [cs.IR]CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
Andre Esteva, Anuprit Kale, Romain Paulus, Kazuma Hashimoto, Wenpeng Yin, Dragomir Radev, Richard Socher
http://arxiv.org/abs/2006.09595v1
• [cs.IR]Comparative Sentiment Analysis of App Reviews
Sakshi Ranjan, Subhankar Mishra
http://arxiv.org/abs/2006.09739v1
• [cs.IR]Learning Colour Representations of Search Queries
Paridhi Maheshwari, Manoj Ghuhan, Vishwa Vinay
http://arxiv.org/abs/2006.09904v1
• [cs.IT]3D Placement for Multi-UAV Relaying: An Iterative Gibbs-Sampling and Block Coordinate Descent Optimization Approach
Zhenyu Kang, Changsheng You, Rui Zhang
http://arxiv.org/abs/2006.09658v1
• [cs.IT]Binary linear codes with few weights from Boolean functions
Xiaoqiang Wang, Dabin Zheng, Yan Zhang
http://arxiv.org/abs/2006.09591v1
• [cs.IT]Dense and Sparse Coding: Theory and Architectures
Abiy Tasissa, Emmanouil Theodosis, Bahareh Tolooshams, Demba Ba
http://arxiv.org/abs/2006.09534v1
• [cs.IT]High order low-bit Sigma-Delta quantization for fusion frames
Zhen Gao, Felix Krahmer, Alexander M. Powell
http://arxiv.org/abs/2006.09732v1
• [cs.IT]Multilinear Algebra for Distributed Storage
Iwan Duursma, Xiao Li, Hsin-Po Wang
http://arxiv.org/abs/2006.08911v1
• [cs.IT]Optimizing Information Freshness in Two-Way Relay Networks
Bohai Li, He Chen, Nikolaos Pappas, Yonghui Li
http://arxiv.org/abs/2006.09683v1
• [cs.IT]Random sampling and reconstruction of concentrated signals in a reproducing kernel space
Yaxu Li, Qiyu Sun, Jun Xian
http://arxiv.org/abs/2006.09609v1
• [cs.LG]A Study of Compositional Generalization in Neural Models
Tim Klinger, Dhaval Adjodah, Vincent Marois, Josh Joseph, Matthew Riemer, Alex ‘Sandy’ Pentland, Murray Campbell
http://arxiv.org/abs/2006.09437v1
• [cs.LG]AdvMind: Inferring Adversary Intent of Black-Box Attacks
Ren Pang, Xinyang Zhang, Shouling Ji, Xiapu Luo, Ting Wang
http://arxiv.org/abs/2006.09539v1
• [cs.LG]Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
Bartosz Wójcik, Paweł Morawiecki, Marek Śmieja, Tomasz Krzyżek, Przemysław Spurek, Jacek Tabor
http://arxiv.org/abs/2006.10013v1
• [cs.LG]Analytical Probability Distributions and EM-Learning for Deep Generative Networks
Randall Balestriero, Sebastien Paris, Richard G. Baraniuk
http://arxiv.org/abs/2006.10023v1
• [cs.LG]Automatic Curriculum Learning through Value Disagreement
Yunzhi Zhang, Pieter Abbeel, Lerrel Pinto
http://arxiv.org/abs/2006.09641v1
• [cs.LG]Bayesian active learning for production, a systematic study and a reusable library
Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste
http://arxiv.org/abs/2006.09916v1
• [cs.LG]Big Self-Supervised Models are Strong Semi-Supervised Learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton
http://arxiv.org/abs/2006.10029v1
• [cs.LG]Categorical Normalizing Flows via Continuous Transformations
Phillip Lippe, Efstratios Gavves
http://arxiv.org/abs/2006.09790v1
• [cs.LG]CoSE: Compositional Stroke Embeddings
Emre Aksan, Thomas Deselaers, Andrea Tagliasacchi, Otmar Hilliges
http://arxiv.org/abs/2006.09930v1
• [cs.LG]Communication-Efficient Robust Federated Learning Over Heterogeneous Datasets
Yanjie Dong, Georgios B. Giannakis, Tianyi Chen, Julian Cheng, Md. Jahangir Hossain, Victor C. M. Leung
http://arxiv.org/abs/2006.09992v1
• [cs.LG]Constrained regret minimization for multi-criterion multi-armed bandits
Anmol Kagrecha, Jayakrishnan Nair, Krishna Jagannathan
http://arxiv.org/abs/2006.09649v1
• [cs.LG]Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters
Kaiyi Ji, Jason D. Lee, Yingbin Liang, H. Vincent Poor
http://arxiv.org/abs/2006.09486v1
• [cs.LG]Data Driven Control with Learned Dynamics: Model-Based versus Model-Free Approach
Wenjian Hao, Yiqiang Han
http://arxiv.org/abs/2006.09543v1
• [cs.LG]Decomposable Families of Itemsets
Nikolaj Tatti, Hannes Heikinheimo
http://arxiv.org/abs/2006.09533v1
• [cs.LG]Delta Schema Network in Model-based Reinforcement Learning
Andrey Gorodetskiy, Alexandra Shlychkova, Aleksandr I. Panov
http://arxiv.org/abs/2006.09950v1
• [cs.LG]Dynamic Tensor Rematerialization
Marisa Kirisame, Steven Lyubomirsky, Altan Haan, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, Zachary Tatlock
http://arxiv.org/abs/2006.09616v1
• [cs.LG]Enhanced First and Zeroth Order Variance Reduced Algorithms for Min-Max Optimization
Tengyu Xu, Zhe Wang, Yingbin Liang, H. Vincent Poor
http://arxiv.org/abs/2006.09361v2
• [cs.LG]Equilibrium Propagation for Complete Directed Neural Networks
Matilde Tristany Farinha, Sérgio Pequito, Pedro A. Santos, Mário A. T. Figueiredo
http://arxiv.org/abs/2006.08798v2
• [cs.LG]Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions
Tom Yan, Christian Kroer, Alexander Peysakhovich
http://arxiv.org/abs/2006.09538v1
• [cs.LG]Feature Space Saturation during Training
Justin Shenk, Mats L. Richter, Wolf Byttner, Anders Arpteg, Mikael Huss
http://arxiv.org/abs/2006.08679v2
• [cs.LG]FedCD: Improving Performance in non-IID Federated Learning
Kavya Kopparapu, Eric Lin, Jessica Zhao
http://arxiv.org/abs/2006.09637v1
• [cs.LG]Fine-Grained Stochastic Architecture Search
Shraman Ray Chaudhuri, Elad Eban, Hanhan Li, Max Moroz, Yair Movshovitz-Attias
http://arxiv.org/abs/2006.09581v1
• [cs.LG]Forgetful Experience Replay in Hierarchical Reinforcement Learning from Demonstrations
Alexey Skrynnik, Aleksey Staroverov, Ermek Aitygulov, Kirill Aksenov, Vasilii Davydov, Aleksandr I. Panov
http://arxiv.org/abs/2006.09939v1
• [cs.LG]GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang
http://arxiv.org/abs/2006.09963v1
• [cs.LG]Generalising Recursive Neural Models by Tensor Decomposition
Daniele Castellana, Davide Bacciu
http://arxiv.org/abs/2006.10021v1
• [cs.LG]Geometry of Comparisons
Puoya Tabaghi, Ivan Dokmanić
http://arxiv.org/abs/2006.09858v1
• [cs.LG]Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control
Hua Zheng, Wei Xie, Mingbin Ben Feng
http://arxiv.org/abs/2006.09919v1
• [cs.LG]How isotropic kernels learn simple invariants
Jonas Paccolat, Stefano Spigler, Matthieu Wyart
http://arxiv.org/abs/2006.09754v1
• [cs.LG]Isometric Graph Neural Networks
Matthew Walker, Matthew Walker, Yiou Xiao, Yafei Wang, Ayan Acharya
http://arxiv.org/abs/2006.09554v1
• [cs.LG]Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haussmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
http://arxiv.org/abs/2006.09914v1
• [cs.LG]Learning What to Defer for Maximum Independent Sets
Sungsoo Ahn, Younggyo Seo, Jinwoo Shin
http://arxiv.org/abs/2006.09607v1
• [cs.LG]Linear Last-iterate Convergence for Matrix Games and Stochastic Games
Chung-Wei Lee, Haipeng Luo, Chen-Yu Wei, Mengxiao Zhang
http://arxiv.org/abs/2006.09517v1
• [cs.LG]Memory-Efficient Pipeline-Parallel DNN Training
Deepak Narayanan, Amar Phanishayee, Kaiyu Shi, Xie Chen, Matei Zaharia
http://arxiv.org/abs/2006.09503v1
• [cs.LG]Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
Seungeun Oh, Jihong Park, Eunjeong Jeong, Hyesung Kim, Mehdi Bennis, Seong-Lyun Kim
http://arxiv.org/abs/2006.09801v1
• [cs.LG]MixBoost: A Heterogeneous Boosting Machine
Thomas Parnell, Andreea Anghel, Malgorzata Lazuka, Nikolas Ioannou, Sebastian Kurella, Peshal Agarwal, Nikolaos Papandreou, Haralampos Pozidis
http://arxiv.org/abs/2006.09745v1
• [cs.LG]Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
Sam Snodgrass, Anurag Sarkar
http://arxiv.org/abs/2006.09807v1
• [cs.LG]Multipole Graph Neural Operator for Parametric Partial Differential Equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar
http://arxiv.org/abs/2006.09535v1
• [cs.LG]NNC: Neural-Network Control of Dynamical Systems on Graphs
Thomas Asikis, Lucas Böttcher, Nino Antulov-Fantulin
http://arxiv.org/abs/2006.09773v1
• [cs.LG]Network Diffusions via Neural Mean-Field Dynamics
Shushan He, Hongyuan Zha, Xiaojing Ye
http://arxiv.org/abs/2006.09449v1
• [cs.LG]Neural Anisotropy Directions
Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard
http://arxiv.org/abs/2006.09717v1
• [cs.LG]Neural Optimal Control for Representation Learning
Mathieu Chalvidal, Matthew Ricci, Rufin VanRullen, Thomas Serre
http://arxiv.org/abs/2006.09545v1
• [cs.LG]Off-policy Bandits with Deficient Support
Noveen Sachdeva, Yi Su, Thorsten Joachims
http://arxiv.org/abs/2006.09438v1
• [cs.LG]Opponent Modelling with Local Information Variational Autoencoders
Georgios Papoudakis, Filippos Christianos, Stefano V. Albrecht
http://arxiv.org/abs/2006.09447v1
• [cs.LG]Optimizing Grouped Convolutions on Edge Devices
Perry Gibson, José Cano, Jack Turner, Elliot J. Crowley, Michael O’Boyle, Amos Storkey
http://arxiv.org/abs/2006.09791v1
• [cs.LG]Parameterized MDPs and Reinforcement Learning Problems — A Maximum Entropy Principle Based Framework
Amber Srivastava, Srinivasa M Salapaka
http://arxiv.org/abs/2006.09646v1
• [cs.LG]Partial Policy Iteration for L1-Robust Markov Decision Processes
Chin Pang Ho, Marek Petrik, Wolfram Wiesemann
http://arxiv.org/abs/2006.09484v1
• [cs.LG]Prior knowledge distillation based on financial time series
Jie Fang, Jianwu Lin
http://arxiv.org/abs/2006.09247v2
• [cs.LG]Real-Time Regression with Dividing Local Gaussian Processes
Armin Lederer, Alejandro Jose Ordonez Conejo, Korbinian Maier, Wenxin Xiao, Sandra Hirche
http://arxiv.org/abs/2006.09446v1
• [cs.LG]Region-based Energy Neural Network for Approximate Inference
Dong Liu, Ragnar Thobaben, Lars K. Rasmussen
http://arxiv.org/abs/2006.09927v1
• [cs.LG]Robust Meta-learning for Mixed Linear Regression with Small Batches
Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh
http://arxiv.org/abs/2006.09702v1
• [cs.LG]Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes
Mike Gartrell, Insu Han, Elvis Dohmatob, Jennifer Gillenwater, Victor-Emmanuel Brunel
http://arxiv.org/abs/2006.09862v1
• [cs.LG]Self-training Avoids Using Spurious Features Under Domain Shift
Yining Chen, Colin Wei, Ananya Kumar, Tengyu Ma
http://arxiv.org/abs/2006.10032v1
• [cs.LG]Solving Constrained CASH Problems with ADMM
Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi, Dakuo Wang, Djallel Bouneffouf, Greg Bramble, Horst Samulowitz, Alexander G. Gray
http://arxiv.org/abs/2006.09635v1
• [cs.LG]Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning
Bram Cals, Yingqian Zhang, Remco Dijkman, Claudy van Dorst
http://arxiv.org/abs/2006.09507v1
• [cs.LG]StatAssist & GradBoost: A Study on Optimal INT8 Quantization-aware Training from Scratch
Taehoon Kim, Youngjoon Yoo, Jihoon Yang
http://arxiv.org/abs/2006.09679v1
• [cs.LG]Stochastic Network Utility Maximization with Unknown Utilities: Multi-Armed Bandits Approach
Arun Verma, Manjesh K. Hanawal
http://arxiv.org/abs/2006.09997v1
• [cs.LG]Task-agnostic Exploration in Reinforcement Learning
Xuezhou Zhang, Yuzhe ma, Adish Singla
http://arxiv.org/abs/2006.09497v1
• [cs.LG]Tell Me Something I Don’t Know: Randomization Strategies for Iterative Data Mining
Sami Hanhijärvi, Markus Ojala, Niko Vuokko, Kai Puolamäki, Nikolaj Tatti, Heikki Mannila
http://arxiv.org/abs/2006.09467v1
• [cs.LG]The Influence of Shape Constraints on the Thresholding Bandit Problem
James Cheshire, Pierre Menard, Alexandra Carpentier
http://arxiv.org/abs/2006.10006v1
• [cs.LG]Toward Theory of Applied Learning. What is Machine Learning?
Marina Sapir
http://arxiv.org/abs/2006.09500v1
• [cs.LG]Untangling tradeoffs between recurrence and self-attention in neural networks
Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie
http://arxiv.org/abs/2006.09471v1
• [cs.LG]Walk Message Passing Neural Networks and Second-Order Graph Neural Networks
Floris Geerts
http://arxiv.org/abs/2006.09499v1
• [cs.LG]Wasserstein Embedding for Graph Learning
Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann
http://arxiv.org/abs/2006.09430v1
• [cs.NE]Dynamic Vehicle Routing Problem: A Monte Carlo approach
Michał Okulewicz, Jacek Mańdziuk
http://arxiv.org/abs/2006.09996v1
• [cs.NE]Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
Anja Jankovic, Carola Doerr
http://arxiv.org/abs/2006.09855v1
• [cs.NE]Learning to Learn with Feedback and Local Plasticity
Jack Lindsey, Ashok Litwin-Kumar
http://arxiv.org/abs/2006.09549v1
• [cs.NE]On sparse connectivity, adversarial robustness, and a novel model of the artificial neuron
Sergey Bochkanov
http://arxiv.org/abs/2006.09510v1
• [cs.NE]Simplified Swarm Optimization for Bi-Objection Active Reliability Redundancy Allocation Problems
Wei-Chang Yeh
http://arxiv.org/abs/2006.09844v1
• [cs.NI]DCAF: A Dynamic Computation Allocation Framework for Online Serving System
Biye Jiang, Pengye Zhang, Rihan Chen, Binding Dai, Xinchen Luo, Yin Yang, Guan Wang, Guorui Zhou, Xiaoqiang Zhu, Kun Gai
http://arxiv.org/abs/2006.09684v1
• [cs.NI]Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique for Nodes Starvation Problem in Wireless Mesh Network
Fuad A. Ghaleb, Bander Ali Saleh Al-rimy, Maznah Kamat, Mohd. Foad Rohani, Shukor Abd Razak
http://arxiv.org/abs/2006.09655v1
• [cs.NI]Leveraging the Power of Prediction: Predictive Service Placement for Latency-Sensitive Mobile Edge Computing
Huirong Ma, Zhi Zhou, Xu Chen
http://arxiv.org/abs/2006.09710v1
• [cs.RO]An Open-Source Scenario Architect for Autonomous Vehicles
Tim Stahl, Johannes Betz
http://arxiv.org/abs/2006.09731v1
• [cs.RO]Approximate Simulation for Template-Based Whole-Body Control
Vince Kurtz, Patrick M. Wensing, Michael D. Lemmon, Hai Lin
http://arxiv.org/abs/2006.09921v1
• [cs.RO]COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Eivind Meyer, Amalie Heiberg, Adil Rasheed, Omer San
http://arxiv.org/abs/2006.09540v1
• [cs.RO]Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Simen Theie Havenstrøm, Adil Rasheed, Omer San
http://arxiv.org/abs/2006.09792v1
• [cs.RO]Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections
Carl-Johan Hoel, Tommy Tram, Jonas Sjöberg
http://arxiv.org/abs/2006.09786v1
• [cs.RO]ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers
James Ferlez, Mahmoud Elnaggar, Yasser Shoukry, Cody Fleming
http://arxiv.org/abs/2006.09564v1
• [cs.SE]Quality Management of Machine Learning Systems
P. Santhanam
http://arxiv.org/abs/2006.09529v1
• [cs.SI]Did State-sponsored Trolls Shape the US Presidential Election Discourse? Quantifying Influence on Twitter
Nikos Salamanos, Michael J. Jensen, Xinlei He, Yang Chen, Costas Iordanou, Michael Sirivianos
http://arxiv.org/abs/2006.09938v1
• [cs.SI]Scaling Choice Models of Relational Social Data
Jan Overgoor, George Pakapol Supaniratisai, Johan Ugander
http://arxiv.org/abs/2006.10003v1
• [econ.GN]A demographic microsimulation model with an integrated household alignment method
Amarin Siripanich, Taha Rashidi
http://arxiv.org/abs/2006.09474v1
• [eess.AS]Visual Attention for Musical Instrument Recognition
Karn Watcharasupat, Siddharth Gururani, Alexander Lerch
http://arxiv.org/abs/2006.09640v1
• [eess.IV]Deep Learning Meets SAR
Xiao Xiang Zhu, Sina Montazeri, Mohsin Ali, Yuansheng Hua, Yuanyuan Wang, Lichao Mou, Yilei Shi, Feng Xu, Richard Bamler
http://arxiv.org/abs/2006.10027v1
• [eess.IV]High-Fidelity Generative Image Compression
Fabian Mentzer, George Toderici, Michael Tschannen, Eirikur Agustsson
http://arxiv.org/abs/2006.09965v1
• [eess.IV]Noise2Inpaint: Learning Referenceless Denoising by Inpainting Unrolling
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akçakaya
http://arxiv.org/abs/2006.09450v1
• [eess.SP]Intelligent Protection & Classification of Transients in Two-Core Symmetric Phase Angle Regulating Transformers
Pallav Kumar Bera, Can Isik
http://arxiv.org/abs/2006.09865v1
• [eess.SP]Wireless 3D Point Cloud Delivery Using Deep Graph Neural Networks
Takuya Fujihashi, Toshiaki Koike-Akino, Siheng Chen, Takashi Watanabe
http://arxiv.org/abs/2006.09835v1
• [math.OC]Structured Stochastic Quasi-Newton Methods for Large-Scale Optimization Problems
Minghan Yang, Dong Xu, Yongfeng Li, Zaiwen Wen, Mengyun Chen
http://arxiv.org/abs/2006.09606v1
• [math.OC]Variation diminishing linear time-invariant systems
Christian Grussler, Rodolphe Sepulchre
http://arxiv.org/abs/2006.10030v1
• [math.PR]A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics
Cosme Louart, Romain Couillet
http://arxiv.org/abs/2006.09728v1
• [math.PR]Reverse Lebesgue and Gaussian isoperimetric inequalities for parallel sets with applications
Varun Jog
http://arxiv.org/abs/2006.09568v1
• [math.ST]A Berry-Esseen theorem for sample quantiles under association
L. Douge
http://arxiv.org/abs/2006.09770v1
• [math.ST]Goodness-of-Fit Test for Self-Exciting Processes
Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie
http://arxiv.org/abs/2006.09439v1
• [math.ST]Logarithmic Voronoi cells
Yulia Alexandr, Alexander Heaton
http://arxiv.org/abs/2006.09431v1
• [math.ST]Robust Persistence Diagrams using Reproducing Kernels
Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath Sriperumbudur
http://arxiv.org/abs/2006.10012v1
• [physics.ed-ph]Creating Experience value to build student satisfaction in higher education
Muji Gunarto, Ratih Hurriyati
http://arxiv.org/abs/2006.09846v1
• [q-bio.NC]Interpretable multimodal fusion networks reveal mechanisms of brain cognition
Wenxing Hu, Xianghe Meng, Yuntong Bai, Aiying Zhang, Biao Cai, Gemeng Zhang, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang
http://arxiv.org/abs/2006.09454v1
• [q-fin.CP]Consistent Recalibration Models and Deep Calibration
Matteo Gambara, Josef Teichmann
http://arxiv.org/abs/2006.09455v1
• [stat.AP]Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
Zenan Wang, Xuan Yin, Tianbo Li, Liangjie Hong
http://arxiv.org/abs/2006.09666v1
• [stat.AP]CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
Paul Freulon, Jérémie Bigot, Boris P. Hejblum
http://arxiv.org/abs/2006.09003v2
• [stat.AP]Using machine learning to identify nontraditional spatial dependence in occupancy data
Narmadha M. Mohankumar, Trevor J. Hefley
http://arxiv.org/abs/2006.09983v1
• [stat.ME]An algorithm for non-parametric estimation in state-space models
Thi Tuyet Trang Chau, Pierre Ailliot, Valérie Monbet
http://arxiv.org/abs/2006.09525v1
• [stat.ME]Combining Experimental and Observational Data to Estimate Treatment Effects on Long Term Outcomes
Susan Athey, Raj Chetty, Guido Imbens
http://arxiv.org/abs/2006.09676v1
• [stat.ME]Discussion of “On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning”
Edward H. Kennedy, Sivaraman Balakrishnan, Larry A. Wasserman
http://arxiv.org/abs/2006.09613v1
• [stat.ME]Efficient nonparametric statistical inference on population feature importance using Shapley values
Brian D. Williamson, Jean Feng
http://arxiv.org/abs/2006.09481v1
• [stat.ME]Exact and computationally efficient Bayesian inference for generalized Markov modulated Poisson processes
Flavio B. Gonçalves, Livia M. Dutra, Roger W. C. Silva
http://arxiv.org/abs/2006.09949v1
• [stat.ME]Family of mean-mixtures of multivariate normal distributions: properties, inference and assessment of multivariate skewness
Mousa Abdi, Mohsen Madadi, N. Balakrishnan, Ahad Jamalizadeh
http://arxiv.org/abs/2006.10018v1
• [stat.ME]Multidimensional Bayesian IRT Model for Hierarchical Latent Structures
Juliane Venturelli S. L., Flavio B. Gonçalves, Dalton F. Andrade
http://arxiv.org/abs/2006.09966v1
• [stat.ME]Shrinking the eigenvalues of M-estimators of covariance matrix
Esa Ollila, Daniel P. Palomar, Frédéric Pascal
http://arxiv.org/abs/2006.10005v1
• [stat.ME]Wasserstein Regression
Yaqing Chen, Zhenhua Lin, Hans-Georg Müller
http://arxiv.org/abs/2006.09660v1
• [stat.ML]A Non-Asymptotic Analysis for Stein Variational Gradient Descent
Anna Korba, Adil Salim, Michael Arbel, Giulia Luise, Arthur Gretton
http://arxiv.org/abs/2006.09797v1
• [stat.ML]Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring
Taira Tsuchiya, Junya Honda, Masashi Sugiyama
http://arxiv.org/abs/2006.09668v1
• [stat.ML]Approximate Gradient Coding with Optimal Decoding
Margalit Glasgow, Mary Wootters
http://arxiv.org/abs/2006.09638v1
• [stat.ML]Causal inference of brain connectivity from fMRI with $ψ$-Learning Incorporated Linear non-Gaussian Acyclic Model ($ψ$-LiNGAM)
Aiying Zhang, Gemeng Zhang, Biao Cai, Wenxing Hu, Li Xiao, Tony W. Wilson, Julia M. Stephen, Vince D. Calhoun, Yu-Ping Wang
http://arxiv.org/abs/2006.09536v1
• [stat.ML]Deep Learning with Functional Inputs
Barinder Thind, Kevin Multani, Jiguo Cao
http://arxiv.org/abs/2006.09590v1
• [stat.ML]Density Deconvolution with Normalizing Flows
Tim Dockhorn, James A. Ritchie, Yaoliang Yu, Iain Murray
http://arxiv.org/abs/2006.09396v1
• [stat.ML]Efficient Statistics for Sparse Graphical Models from Truncated Samples
Arnab Bhattacharyya, Rathin Desai, Sai Ganesh Nagarajan, Ioannis Panageas
http://arxiv.org/abs/2006.09735v1
• [stat.ML]FREEtree: A Tree-based Approach for High Dimensional Longitudinal Data With Correlated Features
Yuancheng Xu, Athanasse Zafirov, R. Michael Alvarez, Dan Kojis, Min Tan, Christina M. Ramirez
http://arxiv.org/abs/2006.09693v1
• [stat.ML]GPIRT: A Gaussian Process Model for Item Response Theory
JBrandon Duck-Mayr, Roman Garnett, Jacob M. Montgomery
http://arxiv.org/abs/2006.09900v1
• [stat.ML]Image-on-Scalar Regression via Deep Neural Networks
Daiwei Zhang, Lexin Li, Chandra Sripada, Jian Kang
http://arxiv.org/abs/2006.09911v1
• [stat.ML]Implicit regularization for convex regularizers
Cesare Molinari, Mathurin Massias, Lorenzo Rosasco, Silvia Villa
http://arxiv.org/abs/2006.09859v1
• [stat.ML]Interpolation and Learning with Scale Dependent Kernels
Nicolò Pagliana, Alessandro Rudi, Ernesto De Vito, Lorenzo Rosasco
http://arxiv.org/abs/2006.09984v1
• [stat.ML]Kernel Alignment Risk Estimator: Risk Prediction from Training Data
Arthur Jacot, Berfin Şimşek, Francesco Spadaro, Clément Hongler, Franck Gabriel
http://arxiv.org/abs/2006.09796v1
• [stat.ML]LazyIter: A Fast Algorithm for Counting Markov Equivalent DAGs and Designing Experiments
Ali AhmadiTeshnizi, Saber Salehkaleybar, Negar Kiyavash
http://arxiv.org/abs/2006.09670v1
• [stat.ML]Longitudinal Variational Autoencoder
Siddharth Ramchandran, Gleb Tikhonov, Miika Koskinen, Harri Lähdesmäki
http://arxiv.org/abs/2006.09763v1
• [stat.ML]Occam’s Ghost
Peter Kövesarki
http://arxiv.org/abs/2006.09813v1
• [stat.ML]Regularized ERM on random subspaces
Andrea Della Vecchia, Jaouad Mourtada, Ernesto De Vito, Lorenzo Rosasco
http://arxiv.org/abs/2006.10016v1
• [stat.ML]Robust compressed sensing of generative models
Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis
http://arxiv.org/abs/2006.09461v1
• [stat.ML]Universal Lower-Bounds on Classification Error under Adversarial Attacks and Random Corruption
Elvis Dohmatob
http://arxiv.org/abs/2006.09989v1
• [stat.ML]Universally Quantized Neural Compression
Eirikur Agustsson, Lucas Theis
http://arxiv.org/abs/2006.09952v1