cs.AI - 人工智能 cs.CL - 计算与语言 cs.CR - 加密与安全 cs.CV - 机器视觉与模式识别 cs.CY - 计算与社会 cs.DC - 分布式、并行与集群计算 cs.DS - 数据结构与算法 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.RO - 机器人学 cs.SD - 声音处理 cs.SE - 软件工程 cs.SI - 社交网络与信息网络 econ.EM - 计量经济学 eess.AS - 语音处理 eess.IV - 图像与视频处理 math.OC - 优化与控制 math.PR - 概率 math.ST - 统计理论 quant-ph - 量子物理 stat.AP - 应用统计 stat.ME - 统计方法论 stat.ML - (统计)机器学习

    • [cs.AI]Intelligent Software Web Agents: A Gap Analysis
    • [cs.AI]VitrAI — Applying Explainable AI in the Real World
    • [cs.CL]A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
    • [cs.CL]A reproduction of Apple’s bi-directional LSTM models for language identification in short strings
    • [cs.CL]Continuous Learning in Neural Machine Translation using Bilingual Dictionaries
    • [cs.CL]Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
    • [cs.CL]Emoji-Based Transfer Learning for Sentiment Tasks
    • [cs.CL]Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders
    • [cs.CL]Neural Inverse Text Normalization
    • [cs.CL]Optimizing Inference Performance of Transformers on CPUs
    • [cs.CL]Speech-language Pre-training for End-to-end Spoken Language Understanding
    • [cs.CL]Transformer Language Models with LSTM-based Cross-utterance Information Representation
    • [cs.CL]Two Training Strategies for Improving Relation Extraction over Universal Graph
    • [cs.CL]Unsupervised Extractive Summarization using Pointwise Mutual Information
    • [cs.CR]A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones
    • [cs.CR]Deep Reinforcement Learning for Backup Strategies against Adversaries
    • [cs.CV]A Parameterised Quantum Circuit Approach to Point Set Matching
    • [cs.CV]A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
    • [cs.CV]Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
    • [cs.CV]Analysis of Interpolation based Image In-painting Approaches
    • [cs.CV]Annotation Cleaning for the MSR-Video to Text Dataset
    • [cs.CV]Densely Deformable Efficient Salient Object Detection Network
    • [cs.CV]Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
    • [cs.CV]End-to-end Audio-visual Speech Recognition with Conformers
    • [cs.CV]Improving Object Detection in Art Images Using Only Style Transfer
    • [cs.CV]K-Hairstyle: A Large-scale Korean hairstyle dataset for virtual hair editing and hairstyle classification
    • [cs.CV]Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots
    • [cs.CV]Outdoor inverse rendering from a single image using multiview self-supervision
    • [cs.CV]ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation
    • [cs.CV]Reviving Iterative Training with Mask Guidance for Interactive Segmentation
    • [cs.CV]Robust White Matter Hyperintensity Segmentation on Unseen Domain
    • [cs.CV]Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
    • [cs.CY]A Decentralized Approach Towards Responsible AI in Social Ecosystems
    • [cs.CY]When no news is bad news — Detection of negative events from news media content
    • [cs.DC]Deep Reinforcement Agent for Scheduling in HPC
    • [cs.DC]TerraWatt: Sustaining Sustainable Computing of Containers in Containers
    • [cs.DS]Adaptive Sampling for Fast Constrained Maximization of Submodular Function
    • [cs.DS]Computing Betweenness Centrality in Link Streams
    • [cs.HC]Multiversal views on language models
    • [cs.HC]Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking
    • [cs.IR]An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration
    • [cs.IR]Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia
    • [cs.IR]Destination similarity based on implicit user interest
    • [cs.IR]Personalized Visualization Recommendation
    • [cs.IR]SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
    • [cs.IT]Complete Power Reallocation for MU-MIMO under Per-Antenna Power Constraint
    • [cs.IT]Distributed Source Coding with Encryption Using Correlated Keys
    • [cs.IT]Multi-access Coded Caching Scheme with Linear Sub-packetization using PDAs
    • [cs.IT]On Graph Matching Using Generalized Seed Side-Information
    • [cs.IT]On the Application of BAC-NOMA to 6G umMTC
    • [cs.IT]Rate-Splitting Multiple Access to Mitigate the Curse of Mobility in (Massive) MIMO Networks
    • [cs.IT]Uncertainty-of-Information Scheduling: A Restless Multi-armed Bandit Framework
    • [cs.LG]A Computability Perspective on (Verified) Machine Learning
    • [cs.LG]A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models
    • [cs.LG]A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes
    • [cs.LG]A model for traffic incident prediction using emergency braking data
    • [cs.LG]Bayesian Quadrature on Riemannian Data Manifolds
    • [cs.LG]Bias-Free Scalable Gaussian Processes via Randomized Truncations
    • [cs.LG]Bootstrapped Representation Learning on Graphs
    • [cs.LG]Broad-UNet: Multi-scale feature learning for nowcasting tasks
    • [cs.LG]Certified Defenses: Why Tighter Relaxations May Hurt Training?
    • [cs.LG]Cockpit: A Practical Debugging Tool for Training Deep Neural Networks
    • [cs.LG]Confounding Tradeoffs for Neural Network Quantization
    • [cs.LG]Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
    • [cs.LG]DeepGLEAM: an hybrid mechanistic and deep learning model for COVID-19 forecasting
    • [cs.LG]Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
    • [cs.LG]Disturbing Reinforcement Learning Agents with Corrupted Rewards
    • [cs.LG]Do-calculus enables causal reasoning with latent variable models
    • [cs.LG]Dynamic Precision Analog Computing for Neural Networks
    • [cs.LG]Efficient Algorithms for Federated Saddle Point Optimization
    • [cs.LG]End-to-End Intelligent Framework for Rockfall Detection
    • [cs.LG]Explaining Neural Scaling Laws
    • [cs.LG]Exploiting Spline Models for the Training of Fully Connected Layers in Neural Network
    • [cs.LG]How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?
    • [cs.LG]Interpretable Predictive Maintenance for Hard Drives
    • [cs.LG]Jacobian Determinant of Normalizing Flows
    • [cs.LG]MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
    • [cs.LG]Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
    • [cs.LG]Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
    • [cs.LG]Neural Architecture Search as Program Transformation Exploration
    • [cs.LG]Online Graph Dictionary Learning
    • [cs.LG]PAC-BUS: Meta-Learning Bounds via PAC-Bayes and Uniform Stability
    • [cs.LG]Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
    • [cs.LG]Projected Wasserstein gradient descent for high-dimensional Bayesian inference
    • [cs.LG]Proximal and Federated Random Reshuffling
    • [cs.LG]ReLU Neural Networks for Exact Maximum Flow Computation
    • [cs.LG]Regret, stability, and fairness in matching markets with bandit learners
    • [cs.LG]SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs
    • [cs.LG]Sample-Optimal PAC Learning of Halfspaces with Malicious Noise
    • [cs.LG]Scalable Bayesian Inverse Reinforcement Learning
    • [cs.LG]Semantically-Conditioned Negative Samples for Efficient Contrastive Learning
    • [cs.LG]Sparse-Push: Communication- & Energy-Efficient Decentralized Distributed Learning over Directed & Time-Varying Graphs with non-IID Datasets
    • [cs.LG]Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
    • [cs.LG]Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
    • [cs.LG]Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
    • [cs.LG]The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
    • [cs.LG]The Symmetry between Bandits and Knapsacks: A Primal-Dual LP-based Approach
    • [cs.LG]Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
    • [cs.LG]Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
    • [cs.LG]What does LIME really see in images?
    • [cs.NE]Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
    • [cs.NE]Min-Max-Plus Neural Networks
    • [cs.NE]Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
    • [cs.RO]Customizable Stochastic High Fidelity Model of the Sensors and Camera onboard a Low SWaP Fixed Wing Autonomous Aircraft
    • [cs.RO]Fair Robust Assignment using Redundancy
    • [cs.RO]Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach
    • [cs.RO]Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
    • [cs.RO]Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
    • [cs.RO]Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approach
    • [cs.RO]Speculative Path Planning
    • [cs.RO]kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
    • [cs.SD]Content-Aware Speaker Embeddings for Speaker Diarisation
    • [cs.SD]Contrastive Unsupervised Learning for Speech Emotion Recognition
    • [cs.SD]Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL Sound Field Dataset
    • [cs.SD]VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention
    • [cs.SE]Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services
    • [cs.SE]On Automatic Parsing of Log Records
    • [cs.SI]A Tale of Two Countries: A Longitudinal Cross-Country Study of Mobile Users’ Reactions to the COVID-19 Pandemic Through the Lens of App Popularity
    • [cs.SI]How do climate change skeptics engage with opposing views? Understanding mechanisms of social identity and cognitive dissonance in an online forum
    • [cs.SI]Leveraging Artificial Intelligence to Analyze Citizens’ Opinions on Urban Green Space
    • [cs.SI]Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern based on Socio-economic Determinants
    • [cs.SI]Mutually exciting point process graphs for modelling dynamic networks
    • [econ.EM]Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
    • [eess.AS]Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
    • [eess.AS]Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier
    • [eess.IV]A Generative Model for Hallucinating Diverse Versions of Super Resolution Images
    • [eess.IV]Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
    • [eess.IV]COVID-19 detection from scarce chest x-ray image data using deep learning
    • [eess.IV]Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization
    • [eess.IV]Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data
    • [math.OC]Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
    • [math.PR]Some Hoeffding- and Bernstein-type Concentration Inequalities
    • [math.PR]Urn models with random multiple drawing and random addition
    • [math.ST]Central Limit Theorems for General Transportation Costs
    • [math.ST]Two-sample Test with Kernel Projected Wasserstein Distance
    • [quant-ph]Theoretical and Experimental Perspectives of Quantum Verification
    • [stat.AP]Automated Vehicle Crash Sequences: Patterns and Potential Uses in Safety Testing
    • [stat.AP]Designing group sequential clinical trials when a delayed effect is anticipated: A practical guidance
    • [stat.AP]Relaxing door-to-door matching reduces passenger waiting times: a workflow for the analysis of driver GPS traces in a stochastic carpooling service
    • [stat.ME]Equivalence class selection of categorical graphical models
    • [stat.ME]Explaining predictive models using Shapley values and non-parametric vine copulas
    • [stat.ME]Sparse Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation
    • [stat.ML]Bayesian Neural Network Priors Revisited
    • [stat.ML]Higher Order Generalization Error for First Order Discretization of Langevin Diffusion
    • [stat.ML]Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
    • [stat.ML]Leveraging Global Parameters for Flow-based Neural Posterior Estimation
    • [stat.ML]Pareto Optimal Model Selection in Linear Bandits
    • [stat.ML]Robust and integrative Bayesian neural networks for likelihood-free parameter inference
    • [stat.ML]Sequential Neural Posterior and Likelihood Approximation
    • [stat.ML]Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
    • [stat.ML]Unsupervised Ground Metric Learning using Wasserstein Eigenvectors

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

    • [cs.AI]Intelligent Software Web Agents: A Gap Analysis
    Sabrina Kirrane
    http://arxiv.org/abs/2102.06607v1

    • [cs.AI]VitrAI — Applying Explainable AI in the Real World
    Marc Hanussek, Falko Kötter, Maximilien Kintz, Jens Drawehn
    http://arxiv.org/abs/2102.06518v1

    • [cs.CL]A Little Pretraining Goes a Long Way: A Case Study on Dependency Parsing Task for Low-resource Morphologically Rich Languages
    Jivnesh Sandhan, Amrith Krishna, Ashim Gupta, Laxmidhar Behera, Pawan Goyal
    http://arxiv.org/abs/2102.06551v1

    • [cs.CL]A reproduction of Apple’s bi-directional LSTM models for language identification in short strings
    Mads Toftrup, Søren Asger Sørensen, Manuel R. Ciosici, Ira Assent
    http://arxiv.org/abs/2102.06282v1

    • [cs.CL]Continuous Learning in Neural Machine Translation using Bilingual Dictionaries
    Jan Niehues
    http://arxiv.org/abs/2102.06558v1

    • [cs.CL]Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
    Amila Silva, Ling Luo, Shanika Karunasekera, Christopher Leckie
    http://arxiv.org/abs/2102.06314v1

    • [cs.CL]Emoji-Based Transfer Learning for Sentiment Tasks
    Susann Boy, Dana Ruiter, Dietrich Klakow
    http://arxiv.org/abs/2102.06423v1

    • [cs.CL]Improving Zero-shot Neural Machine Translation on Language-specific Encoders-Decoders
    Junwei Liao, Yu Shi, Ming Gong, Linjun Shou, Hong Qu, Michael Zeng
    http://arxiv.org/abs/2102.06578v1

    • [cs.CL]Neural Inverse Text Normalization
    Monica Sunkara, Chaitanya Shivade, Sravan Bodapati, Katrin Kirchhoff
    http://arxiv.org/abs/2102.06380v1

    • [cs.CL]Optimizing Inference Performance of Transformers on CPUs
    Dave Dice, Alex Kogan
    http://arxiv.org/abs/2102.06621v1

    • [cs.CL]Speech-language Pre-training for End-to-end Spoken Language Understanding
    Yao Qian, Ximo Bian, Yu Shi, Naoyuki Kanda, Leo Shen, Zhen Xiao, Michael Zeng
    http://arxiv.org/abs/2102.06283v1

    • [cs.CL]Transformer Language Models with LSTM-based Cross-utterance Information Representation
    G. Sun, C. Zhang, P. C. Woodland
    http://arxiv.org/abs/2102.06474v1

    • [cs.CL]Two Training Strategies for Improving Relation Extraction over Universal Graph
    Qin Dai, Naoya Inoue, Ryo Takahashi, Kentaro Inui
    http://arxiv.org/abs/2102.06540v1

    • [cs.CL]Unsupervised Extractive Summarization using Pointwise Mutual Information
    Vishakh Padmakumar, He He
    http://arxiv.org/abs/2102.06272v1

    • [cs.CR]A Non-Intrusive Machine Learning Solution for Malware Detection and Data Theft Classification in Smartphones
    Sai Vishwanath Venkatesh, Prasanna D. Kumaran, Joish J Bosco, Pravin R. Kumaar, Vineeth Vijayaraghavan
    http://arxiv.org/abs/2102.06511v1

    • [cs.CR]Deep Reinforcement Learning for Backup Strategies against Adversaries
    Pascal Debus, Nicolas Müller, Konstantin Böttinger
    http://arxiv.org/abs/2102.06632v1

    • [cs.CV]A Parameterised Quantum Circuit Approach to Point Set Matching
    Mohammadreza Noormandipour, Hanchen Wang
    http://arxiv.org/abs/2102.06697v1

    • [cs.CV]A Too-Good-to-be-True Prior to Reduce Shortcut Reliance
    Nikolay Dagaev, Brett D. Roads, Xiaoliang Luo, Daniel N. Barry, Kaustubh R. Patil, Bradley C. Love
    http://arxiv.org/abs/2102.06406v1

    • [cs.CV]Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
    Luca Robbiano, Muhammad Rameez Ur Rahman, Fabio Galasso, Barbara Caputo, Fabio Maria Carlucci
    http://arxiv.org/abs/2102.06679v1

    • [cs.CV]Analysis of Interpolation based Image In-painting Approaches
    Mustafa Zor, Erkan Bostanci, Mehmet Serdar Guzel, Erinc Karatas
    http://arxiv.org/abs/2102.06564v1

    • [cs.CV]Annotation Cleaning for the MSR-Video to Text Dataset
    Haoran Chen, Jianmin Li, Simone Frintrop, Xiaolin Hu
    http://arxiv.org/abs/2102.06448v1

    • [cs.CV]Densely Deformable Efficient Salient Object Detection Network
    Tanveer Hussain, Saeed Anwar, Amin Ullah, Khan Muhammad, Sung Wook Baik
    http://arxiv.org/abs/2102.06407v1

    • [cs.CV]Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
    Mohamad Shahbazi, Zhiwu Huang, Danda Pani Paudel, Ajad Chhatkuli, Luc Van Gool
    http://arxiv.org/abs/2102.06696v1

    • [cs.CV]End-to-end Audio-visual Speech Recognition with Conformers
    Pingchuan Ma, Stavros Petridis, Maja Pantic
    http://arxiv.org/abs/2102.06657v1

    • [cs.CV]Improving Object Detection in Art Images Using Only Style Transfer
    David Kadish, Sebastian Risi, Anders Sundnes Løvlie
    http://arxiv.org/abs/2102.06529v1

    • [cs.CV]K-Hairstyle: A Large-scale Korean hairstyle dataset for virtual hair editing and hairstyle classification
    Taewoo Kim, Chaeyeon Chung, Sunghyun Park, Gyojung Gu, Keonmin Nam, Wonzo Choe, Jaesung Lee, Jaegul Choo
    http://arxiv.org/abs/2102.06288v1

    • [cs.CV]Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots
    Shigemichi Matsuzaki, Jun Miura, Hiroaki Masuzawa
    http://arxiv.org/abs/2102.06386v1

    • [cs.CV]Outdoor inverse rendering from a single image using multiview self-supervision
    Ye Yu, William A. P. Smith
    http://arxiv.org/abs/2102.06591v1

    • [cs.CV]ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation
    Trung Quang Tran, Mingu Kang, Daeyoung Kim
    http://arxiv.org/abs/2102.06328v1

    • [cs.CV]Reviving Iterative Training with Mask Guidance for Interactive Segmentation
    Konstantin Sofiiuk, Ilia A. Petrov, Anton Konushin
    http://arxiv.org/abs/2102.06583v1

    • [cs.CV]Robust White Matter Hyperintensity Segmentation on Unseen Domain
    Xingchen Zhao, Anthony Sicilia, Davneet Minhas, Erin O’Connor, Howard Aizenstein, William Klunk, Dana Tudorascu, Seong Jae Hwang
    http://arxiv.org/abs/2102.06650v1

    • [cs.CV]Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
    Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, Jiashi Feng
    http://arxiv.org/abs/2102.06605v1

    • [cs.CY]A Decentralized Approach Towards Responsible AI in Social Ecosystems
    Wenjing Chu
    http://arxiv.org/abs/2102.06362v1

    • [cs.CY]When no news is bad news — Detection of negative events from news media content
    Kristoffer L. Nielbo, Frida Haestrup, Kenneth C. Enevoldsen, Peter B. Vahlstrup, Rebekah B. Baglini, Andreas Roepstorff
    http://arxiv.org/abs/2102.06505v1

    • [cs.DC]Deep Reinforcement Agent for Scheduling in HPC
    Yuping Fan, Zhiling Lan, Taylor Childers, Paul Rich, William Allcock, Michael E. Papka
    http://arxiv.org/abs/2102.06243v1

    • [cs.DC]TerraWatt: Sustaining Sustainable Computing of Containers in Containers
    Jennifer Switzer, Rob McGuinness, Pat Pannuto, George Porter, Aaron Schulman, Barath Raghavan
    http://arxiv.org/abs/2102.06614v1

    • [cs.DS]Adaptive Sampling for Fast Constrained Maximization of Submodular Function
    Francesco Quinzan, Vanja Doskoč, Andreas Göbel, Tobias Friedrich
    http://arxiv.org/abs/2102.06486v1

    • [cs.DS]Computing Betweenness Centrality in Link Streams
    Frédéric Simard, Clémence Magnien, Matthieu Latapy
    http://arxiv.org/abs/2102.06543v1

    • [cs.HC]Multiversal views on language models
    Laria Reynolds, Kyle McDonell
    http://arxiv.org/abs/2102.06391v1

    • [cs.HC]Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking
    Youngjun Cho
    http://arxiv.org/abs/2102.06690v1

    • [cs.IR]An Overview of Recommender Systems and Machine Learning in Feature Modeling and Configuration
    Alexander Felfernig, Viet-Man Le, Andrei Popescu, Mathias Uta, Thi Ngoc Trang Tran, Müslüum Atas
    http://arxiv.org/abs/2102.06634v1

    • [cs.IR]Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia
    Yiping Jin, Vishakha Kadam, Dittaya Wanvarie
    http://arxiv.org/abs/2102.06429v1

    • [cs.IR]Destination similarity based on implicit user interest
    Hongliu Cao, Eoin Thomas
    http://arxiv.org/abs/2102.06687v1

    • [cs.IR]Personalized Visualization Recommendation
    Xin Qian, Ryan A. Rossi, Fan Du, Sungchul Kim, Eunyee Koh, Sana Malik, Tak Yeon Lee, Nesreen K. Ahmed
    http://arxiv.org/abs/2102.06343v1

    • [cs.IR]SceneRec: Scene-Based Graph Neural Networks for Recommender Systems
    Gang Wang, Ziyi Guo, Xiang Li, Dawei Yin, Shuai Ma
    http://arxiv.org/abs/2102.06401v1

    • [cs.IT]Complete Power Reallocation for MU-MIMO under Per-Antenna Power Constraint
    Sucheol Kim, Hyeongtaek Lee, Hwanjin Kim, Yongyun Choi, Junil Choi
    http://arxiv.org/abs/2102.06392v1

    • [cs.IT]Distributed Source Coding with Encryption Using Correlated Keys
    Yasutada Oohama, Bagus Santoso
    http://arxiv.org/abs/2102.06363v1

    • [cs.IT]Multi-access Coded Caching Scheme with Linear Sub-packetization using PDAs
    Shanuja Sasi, B. Sundar Rajan
    http://arxiv.org/abs/2102.06616v1

    • [cs.IT]On Graph Matching Using Generalized Seed Side-Information
    Mahshad Shariatnasab, Farhad Shirani, Siddharth Garg, Elza Erkip
    http://arxiv.org/abs/2102.06267v1

    • [cs.IT]On the Application of BAC-NOMA to 6G umMTC
    Zhiguo Ding, H. Vincent Poor
    http://arxiv.org/abs/2102.06584v1

    • [cs.IT]Rate-Splitting Multiple Access to Mitigate the Curse of Mobility in (Massive) MIMO Networks
    Onur Dizdar, Yijie Mao, Bruno Clerckx
    http://arxiv.org/abs/2102.06405v1

    • [cs.IT]Uncertainty-of-Information Scheduling: A Restless Multi-armed Bandit Framework
    Gongpu Chen, Soung Chang Liew, Yulin Shao
    http://arxiv.org/abs/2102.06384v1

    • [cs.LG]A Computability Perspective on (Verified) Machine Learning
    Tonicha Crook, Jay Morgan, Arno Pauly, Markus Roggenbach
    http://arxiv.org/abs/2102.06585v1

    • [cs.LG]A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models
    Severi Rissanen, Pekka Marttinen
    http://arxiv.org/abs/2102.06648v1

    • [cs.LG]A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes
    Zachary Nado, Justin M. Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl
    http://arxiv.org/abs/2102.06356v1

    • [cs.LG]A model for traffic incident prediction using emergency braking data
    Alexander Reichenbach, J. -Emeterio Navarro-B
    http://arxiv.org/abs/2102.06674v1

    • [cs.LG]Bayesian Quadrature on Riemannian Data Manifolds
    Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
    http://arxiv.org/abs/2102.06645v1

    • [cs.LG]Bias-Free Scalable Gaussian Processes via Randomized Truncations
    Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John P. Cunningham
    http://arxiv.org/abs/2102.06695v1

    • [cs.LG]Bootstrapped Representation Learning on Graphs
    Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Veličković, Michal Valko
    http://arxiv.org/abs/2102.06514v1

    • [cs.LG]Broad-UNet: Multi-scale feature learning for nowcasting tasks
    Jesus Garcia Fernandez, Siamak Mehrkanoon
    http://arxiv.org/abs/2102.06442v1

    • [cs.LG]Certified Defenses: Why Tighter Relaxations May Hurt Training?
    Nikola Jovanović, Mislav Balunović, Maximilian Baader, Martin Vechev
    http://arxiv.org/abs/2102.06700v1

    • [cs.LG]Cockpit: A Practical Debugging Tool for Training Deep Neural Networks
    Frank Schneider, Felix Dangel, Philipp Hennig
    http://arxiv.org/abs/2102.06604v1

    • [cs.LG]Confounding Tradeoffs for Neural Network Quantization
    Sahaj Garg, Anirudh Jain, Joe Lou, Mitchell Nahmias
    http://arxiv.org/abs/2102.06366v1

    • [cs.LG]Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices
    Yuhong Song, Weiwen Jiang, Bingbing Li, Panjie Qi, Qingfeng Zhuge, Edwin Hsing-Mean Sha, Sakyasingha Dasgupta, Yiyu Shi, Caiwen Ding
    http://arxiv.org/abs/2102.06336v1

    • [cs.LG]DeepGLEAM: an hybrid mechanistic and deep learning model for COVID-19 forecasting
    Dongxia Wu, Liyao Gao, Xinyue Xiong, Matteo Chinazzi, Alessandro Vespignani, Yian Ma, Rose Yu
    http://arxiv.org/abs/2102.06684v1

    • [cs.LG]Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
    Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb
    http://arxiv.org/abs/2102.06496v1

    • [cs.LG]Disturbing Reinforcement Learning Agents with Corrupted Rewards
    Rubén Majadas, Javier García, Fernando Fernández
    http://arxiv.org/abs/2102.06587v1

    • [cs.LG]Do-calculus enables causal reasoning with latent variable models
    Sara Mohammad-Taheri, Robert Ness, Jeremy Zucker, Olga Vitek
    http://arxiv.org/abs/2102.06626v1

    • [cs.LG]Dynamic Precision Analog Computing for Neural Networks
    Sahaj Garg, Joe Lou, Anirudh Jain, Mitchell Nahmias
    http://arxiv.org/abs/2102.06365v1

    • [cs.LG]Efficient Algorithms for Federated Saddle Point Optimization
    Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti, Sewoong Oh
    http://arxiv.org/abs/2102.06333v1

    • [cs.LG]End-to-End Intelligent Framework for Rockfall Detection
    Thanasis Zoumpekas, Anna Puig, Maria Salamó, David García-Sellés, Laura Blanco Nuñez, Marta Guinau
    http://arxiv.org/abs/2102.06491v1

    • [cs.LG]Explaining Neural Scaling Laws
    Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma
    http://arxiv.org/abs/2102.06701v1

    • [cs.LG]Exploiting Spline Models for the Training of Fully Connected Layers in Neural Network
    Kanya Mo, Shen Zheng, Xiwei Wang, Jinghua Wang, Klaus-Dieter Schewe
    http://arxiv.org/abs/2102.06554v1

    • [cs.LG]How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?
    Ming-Chang Lee, Jia-Chun Lin, Ernst Gunnar Gran
    http://arxiv.org/abs/2102.06560v1

    • [cs.LG]Interpretable Predictive Maintenance for Hard Drives
    Maxime Amram, Jack Dunn, Jeremy J. Toledano, Ying Daisy Zhuo
    http://arxiv.org/abs/2102.06509v1

    • [cs.LG]Jacobian Determinant of Normalizing Flows
    Huadong Liao, Jiawei He
    http://arxiv.org/abs/2102.06539v1

    • [cs.LG]MetaGrad: Adaptation using Multiple Learning Rates in Online Learning
    Tim van Erven, Wouter M. Koolen, Dirk van der Hoeven
    http://arxiv.org/abs/2102.06622v1

    • [cs.LG]Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
    Paramveer Dhillon, Sinan Aral
    http://arxiv.org/abs/2102.06602v1

    • [cs.LG]Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
    Hansheng Xue, Luwei Yang, Vaibhav Rajan, Wen Jiang, Yi Wei, Yu Lin
    http://arxiv.org/abs/2102.06371v1

    • [cs.LG]Neural Architecture Search as Program Transformation Exploration
    Jack Turner, Elliot J. Crowley, Michael O’Boyle
    http://arxiv.org/abs/2102.06599v1

    • [cs.LG]Online Graph Dictionary Learning
    Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary, Marco Corneli, Nicolas Courty
    http://arxiv.org/abs/2102.06555v1

    • [cs.LG]PAC-BUS: Meta-Learning Bounds via PAC-Bayes and Uniform Stability
    Alec Farid, Anirudha Majumdar
    http://arxiv.org/abs/2102.06589v1

    • [cs.LG]Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
    Laurent Pagnier, Michael Chertkov
    http://arxiv.org/abs/2102.06349v1

    • [cs.LG]Projected Wasserstein gradient descent for high-dimensional Bayesian inference
    Yifei Wang, Wuchen Li, Peng Chen
    http://arxiv.org/abs/2102.06350v1

    • [cs.LG]Proximal and Federated Random Reshuffling
    Konstantin Mishchenko, Ahmed Khaled, Peter Richtárik
    http://arxiv.org/abs/2102.06704v1

    • [cs.LG]ReLU Neural Networks for Exact Maximum Flow Computation
    Christoph Hertrich, Leon Sering
    http://arxiv.org/abs/2102.06635v1

    • [cs.LG]Regret, stability, and fairness in matching markets with bandit learners
    Sarah H. Cen, Devavrat Shah
    http://arxiv.org/abs/2102.06246v1

    • [cs.LG]SCOUT: Socially-COnsistent and UndersTandable Graph Attention Network for Trajectory Prediction of Vehicles and VRUs
    Sandra Carrasco, David Fernández Llorca, Miguel Ángel Sotelo
    http://arxiv.org/abs/2102.06361v1

    • [cs.LG]Sample-Optimal PAC Learning of Halfspaces with Malicious Noise
    Jie Shen
    http://arxiv.org/abs/2102.06247v1

    • [cs.LG]Scalable Bayesian Inverse Reinforcement Learning
    Alex J. Chan, Mihaela van der Schaar
    http://arxiv.org/abs/2102.06483v1

    • [cs.LG]Semantically-Conditioned Negative Samples for Efficient Contrastive Learning
    James O’ Neill, Danushka Bollegala
    http://arxiv.org/abs/2102.06603v1

    • [cs.LG]Sparse-Push: Communication- & Energy-Efficient Decentralized Distributed Learning over Directed & Time-Varying Graphs with non-IID Datasets
    Sai Aparna Aketi, Amandeep Singh, Jan Rabaey
    http://arxiv.org/abs/2102.05715v2

    • [cs.LG]Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
    Guojun Xiong, Gang Yan, Rahul Singh, Jian Li
    http://arxiv.org/abs/2102.06280v1

    • [cs.LG]Stragglers Are Not Disaster: A Hybrid Federated Learning Algorithm with Delayed Gradients
    Xingyu Li, Zhe Qu, Bo Tang, Zhuo Lu
    http://arxiv.org/abs/2102.06329v1

    • [cs.LG]Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors
    Julian Büchel, Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri, Dylan R. Muir
    http://arxiv.org/abs/2102.06408v1

    • [cs.LG]The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation
    Peter Kairouz, Ziyu Liu, Thomas Steinke
    http://arxiv.org/abs/2102.06387v1

    • [cs.LG]The Symmetry between Bandits and Knapsacks: A Primal-Dual LP-based Approach
    Xiaocheng Li, Chunlin Sun, Yinyu Ye
    http://arxiv.org/abs/2102.06385v1

    • [cs.LG]Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
    Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra
    http://arxiv.org/abs/2102.06462v1

    • [cs.LG]Universal Adversarial Perturbations Through the Lens of Deep Steganography: Towards A Fourier Perspective
    Chaoning Zhang, Philipp Benz, Adil Karjauv, In So Kweon
    http://arxiv.org/abs/2102.06479v1

    • [cs.LG]What does LIME really see in images?
    Damien Garreau, Dina Mardaoui
    http://arxiv.org/abs/2102.06307v1

    • [cs.NE]Leveraging Benchmarking Data for Informed One-Shot Dynamic Algorithm Selection
    Furong Ye, Carola Doerr, Thomas Bäck
    http://arxiv.org/abs/2102.06481v1

    • [cs.NE]Min-Max-Plus Neural Networks
    Ye Luo, Shiqing Fan
    http://arxiv.org/abs/2102.06358v1

    • [cs.NE]Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks
    Amine Aziz-Alaoui, Carola Doerr, Johann Dreo
    http://arxiv.org/abs/2102.06435v1

    • [cs.RO]Customizable Stochastic High Fidelity Model of the Sensors and Camera onboard a Low SWaP Fixed Wing Autonomous Aircraft
    Eduado Gallo
    http://arxiv.org/abs/2102.06492v1

    • [cs.RO]Fair Robust Assignment using Redundancy
    Matthew Malencia, Vijay Kumar, George Pappas, Amanda Prorok
    http://arxiv.org/abs/2102.06265v1

    • [cs.RO]Fast Fault Detection on a Quadrotor using Onboard Sensors and a Kalman Filter Approach
    Bram Strack van Schijndel, Sihao Sun, Coen de Visser
    http://arxiv.org/abs/2102.06439v1

    • [cs.RO]Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients
    Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro
    http://arxiv.org/abs/2102.06284v1

    • [cs.RO]Predicting and Attending to Damaging Collisions for Placing Everyday Objects in Photo-Realistic Simulations
    Aly Magassouba, Komei Sugiura, Angelica Nakayama, Tsubasa Hirakawa, Takayoshi Yamashita, Hironobu Fujiyoshi, Hisashi Kawai
    http://arxiv.org/abs/2102.06507v1

    • [cs.RO]Robotic Tool Tracking under Partially Visible Kinematic Chain: A Unified Approach
    Florian Richter, Jingpei Lu, Ryan K. Orosco, Michael C. Yip
    http://arxiv.org/abs/2102.06235v1

    • [cs.RO]Speculative Path Planning
    Mohammad Bakhshalipour, Mohamad Qadri, Dominic Guri
    http://arxiv.org/abs/2102.06261v1

    • [cs.RO]kPAM 2.0: Feedback Control for Category-Level Robotic Manipulation
    Wei Gao, Russ Tedrake
    http://arxiv.org/abs/2102.06279v1

    • [cs.SD]Content-Aware Speaker Embeddings for Speaker Diarisation
    G. Sun, D. Liu, C. Zhang, P. C. Woodland
    http://arxiv.org/abs/2102.06467v1

    • [cs.SD]Contrastive Unsupervised Learning for Speech Emotion Recognition
    Mao Li, Bo Yang, Joshua Levy, Andreas Stolcke, Viktor Rozgic, Spyros Matsoukas, Constantinos Papayiannis, Daniel Bone, Chao Wang
    http://arxiv.org/abs/2102.06357v1

    • [cs.SD]Deep Sound Field Reconstruction in Real Rooms: Introducing the ISOBEL Sound Field Dataset
    Miklas Strøm Kristoffersen, Martin Bo Møller, Pablo Martínez-Nuevo, Jan Østergaard
    http://arxiv.org/abs/2102.06455v1

    • [cs.SD]VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention
    Peng Liu, Yuewen Cao, Songxiang Liu, Na Hu, Guangzhi Li, Chao Weng, Dan Su
    http://arxiv.org/abs/2102.06431v1

    • [cs.SE]Data Analytics and Machine Learning Methods, Techniques and Tool for Model-Driven Engineering of Smart IoT Services
    Armin Moin
    http://arxiv.org/abs/2102.06445v1

    • [cs.SE]On Automatic Parsing of Log Records
    Jared Rand, Andriy Miranskyy
    http://arxiv.org/abs/2102.06320v1

    • [cs.SI]A Tale of Two Countries: A Longitudinal Cross-Country Study of Mobile Users’ Reactions to the COVID-19 Pandemic Through the Lens of App Popularity
    Liu Wang, Haoyu Wang, Yi Wang, Gareth Tyson
    http://arxiv.org/abs/2102.06528v1

    • [cs.SI]How do climate change skeptics engage with opposing views? Understanding mechanisms of social identity and cognitive dissonance in an online forum
    Lisa Oswald, Jonathan Bright
    http://arxiv.org/abs/2102.06516v1

    • [cs.SI]Leveraging Artificial Intelligence to Analyze Citizens’ Opinions on Urban Green Space
    Mohammadhossein Ghahramani, Nadina J. Galle, Fabio Duarte, Carlo Ratti, Francesco Pilla
    http://arxiv.org/abs/2102.06659v1

    • [cs.SI]Leveraging Artificial Intelligence to Analyze the COVID-19 Distribution Pattern based on Socio-economic Determinants
    Mohammadhossein Ghahramania, Francesco Pillaa
    http://arxiv.org/abs/2102.06656v1

    • [cs.SI]Mutually exciting point process graphs for modelling dynamic networks
    Francesco Sanna Passino, Nicholas A. Heard
    http://arxiv.org/abs/2102.06527v1

    • [econ.EM]Linear programming approach to nonparametric inference under shape restrictions: with an application to regression kink designs
    Harold D. Chiang, Kengo Kato, Yuya Sasaki, Takuya Ura
    http://arxiv.org/abs/2102.06586v1

    • [eess.AS]Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
    Jonah Casebeer, Vinjai Vale, Umut Isik, Jean-Marc Valin, Ritwik Giri, Arvindh Krishnaswamy
    http://arxiv.org/abs/2102.06610v1

    • [eess.AS]Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier
    Guillaume Carbajal, Julius Richter, Timo Gerkmann
    http://arxiv.org/abs/2102.06454v1

    • [eess.IV]A Generative Model for Hallucinating Diverse Versions of Super Resolution Images
    Mohamed Abderrahmen Abid, Ihsen Hedhli, Christian Gagné
    http://arxiv.org/abs/2102.06624v1

    • [eess.IV]Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
    Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock
    http://arxiv.org/abs/2102.06665v1

    • [eess.IV]COVID-19 detection from scarce chest x-ray image data using deep learning
    Shruti Jadon
    http://arxiv.org/abs/2102.06285v1

    • [eess.IV]Segmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization
    Mengwei Ren, Neel Dey, James Fishbaugh, Guido Gerig
    http://arxiv.org/abs/2102.06315v1

    • [eess.IV]Uncertainty-Aware Semi-supervised Method using Large Unlabelled and Limited Labeled COVID-19 Data
    Roohallah Alizadehsani, Danial Sharifrazi, Navid Hoseini Izadi, Javad Hassannataj Joloudari, Afshin Shoeibi, Juan M. Gorriz, Sadiq Hussain, Juan E. Arco, Zahra Alizadeh Sani, Fahime Khozeimeh, Abbas Khosravi, Saeid Nahavandi, Sheikh Mohammed Shariful Islam, U Rajendra Acharya
    http://arxiv.org/abs/2102.06388v1

    • [math.OC]Stability and Convergence of Stochastic Gradient Clipping: Beyond Lipschitz Continuity and Smoothness
    Vien V. Mai, Mikael Johansson
    http://arxiv.org/abs/2102.06489v1

    • [math.PR]Some Hoeffding- and Bernstein-type Concentration Inequalities
    Andreas Maurer, Massimiliano Pontil
    http://arxiv.org/abs/2102.06304v1

    • [math.PR]Urn models with random multiple drawing and random addition
    Irene Crimaldi, Pierre-Yves Louis, Ida Germana Minelli
    http://arxiv.org/abs/2102.06287v1

    • [math.ST]Central Limit Theorems for General Transportation Costs
    Eustasio del Barrio, Alberto González-Sanz, Jean-Michel Loubes
    http://arxiv.org/abs/2102.06379v1

    • [math.ST]Two-sample Test with Kernel Projected Wasserstein Distance
    Jie Wang, Rui Gao, Yao Xie
    http://arxiv.org/abs/2102.06449v1

    • [quant-ph]Theoretical and Experimental Perspectives of Quantum Verification
    Jose Carrasco, Andreas Elben, Christian Kokail, Barbara Kraus, Peter Zoller
    http://arxiv.org/abs/2102.05927v2

    • [stat.AP]Automated Vehicle Crash Sequences: Patterns and Potential Uses in Safety Testing
    Yu Song, Madhav V. Chitturi, David A. Noyce
    http://arxiv.org/abs/2102.06286v1

    • [stat.AP]Designing group sequential clinical trials when a delayed effect is anticipated: A practical guidance
    Dominic Magirr, José L. Jiménez
    http://arxiv.org/abs/2102.05535v2

    • [stat.AP]Relaxing door-to-door matching reduces passenger waiting times: a workflow for the analysis of driver GPS traces in a stochastic carpooling service
    Panayotis Papoutsis, Safa Fennia, Constant Bridon, Tarn Duong
    http://arxiv.org/abs/2102.06381v1

    • [stat.ME]Equivalence class selection of categorical graphical models
    Federico Castelletti, Stefano Peluso
    http://arxiv.org/abs/2102.06437v1

    • [stat.ME]Explaining predictive models using Shapley values and non-parametric vine copulas
    Kjersti Aas, Thomas Nagler, Martin Jullum, Anders Løland
    http://arxiv.org/abs/2102.06416v1

    • [stat.ME]Sparse Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation
    Alberto Caron, Gianluca Baio, Ioanna Manolopoulou
    http://arxiv.org/abs/2102.06573v1

    • [stat.ML]Bayesian Neural Network Priors Revisited
    Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard Turner, Mark van der Wilk, Laurence Aitchison
    http://arxiv.org/abs/2102.06571v1

    • [stat.ML]Higher Order Generalization Error for First Order Discretization of Langevin Diffusion
    Mufan Bill Li, Maxime Gazeau
    http://arxiv.org/abs/2102.06229v1

    • [stat.ML]Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations
    Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
    http://arxiv.org/abs/2102.06559v1

    • [stat.ML]Leveraging Global Parameters for Flow-based Neural Posterior Estimation
    Pedro L. C. Rodrigues, Thomas Moreau, Gilles Louppe, Alexandre Gramfort
    http://arxiv.org/abs/2102.06477v1

    • [stat.ML]Pareto Optimal Model Selection in Linear Bandits
    Yinglun Zhu, Robert Nowak
    http://arxiv.org/abs/2102.06593v1

    • [stat.ML]Robust and integrative Bayesian neural networks for likelihood-free parameter inference
    Fredrik Wrede, Robin Eriksson, Richard Jiang, Linda Petzold, Stefan Engblom, Andreas Hellander, Prashant Singh
    http://arxiv.org/abs/2102.06521v1

    • [stat.ML]Sequential Neural Posterior and Likelihood Approximation
    Samuel Wiqvist, Jes Frellsen, Umberto Picchini
    http://arxiv.org/abs/2102.06522v1

    • [stat.ML]Tightening the Dependence on Horizon in the Sample Complexity of Q-Learning
    Gen Li, Changxiao Cai, Yuxin Chen, Yuantao Gu, Yuting Wei, Yuejie Chi
    http://arxiv.org/abs/2102.06548v1

    • [stat.ML]Unsupervised Ground Metric Learning using Wasserstein Eigenvectors
    Geert-Jan Huizing, Laura Cantini, Gabriel Peyré
    http://arxiv.org/abs/2102.06278v16