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