astro-ph.EP - 地球与行星天体
    cs.AI - 人工智能
    cs.AR - 硬件体系结构
    cs.CE - 计算工程、 金融和科学
    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.DL - 数字图书馆
    cs.GT - 计算机科学与博弈论
    cs.HC - 人机接口
    cs.IR - 信息检索
    cs.IT - 信息论
    cs.LG - 自动学习
    cs.MA - 多代理系统
    cs.NE - 神经与进化计算
    cs.NI - 网络和互联网体系结构
    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
    eess.AS - 语音处理
    eess.IV - 图像与视频处理
    eess.SY - 系统和控制
    math.OC - 优化与控制
    math.ST - 统计理论
    q-bio.PE - 人口与发展
    stat.AP - 应用统计
    stat.ME - 统计方法论
    stat.ML - (统计)机器学习

    • [astro-ph.EP]Recovery of Meteorites Using an Autonomous Drone and Machine Learning
    • [cs.AI]DECORE: Deep Compression with Reinforcement Learning
    • [cs.AI]DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
    • [cs.AR]FPGA-based Near-Memory Acceleration of Modern Data-Intensive Applications
    • [cs.CE]Data-Driven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization
    • [cs.CL]A Discussion on Building Practical NLP Leaderboards: The Case of Machine Translation
    • [cs.CL]A comprehensive solution to retrieval-based chatbot construction
    • [cs.CL]Assessing Political Prudence of Open-domain Chatbots
    • [cs.CL]BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
    • [cs.CL]Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
    • [cs.CL]CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
    • [cs.CL]Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
    • [cs.CL]Dynamic Language Models for Continuously Evolving Content
    • [cs.CL]FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
    • [cs.CL]From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
    • [cs.CL]Graph Neural Networks for Natural Language Processing: A Survey
    • [cs.CL]How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus
    • [cs.CL]Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
    • [cs.CL]Local Explanation of Dialogue Response Generation
    • [cs.CL]N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses
    • [cs.CL]NAAQA: A Neural Architecture for Acoustic Question Answering
    • [cs.CL]Nested and Balanced Entity Recognition using Multi-Task Learning
    • [cs.CL]One Sense Per Translation
    • [cs.CL]Semi-Supervised and Unsupervised Sense Annotation via Translations
    • [cs.CL]Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings
    • [cs.CL]Sprachsynthese — State-of-the-Art in englischer und deutscher Sprache
    • [cs.CL]TellMeWhy: A Dataset for Answering Why-Questions in Narratives
    • [cs.CL]To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
    • [cs.CL]Towards User-Driven Neural Machine Translation
    • [cs.CL]Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
    • [cs.CL]Zero-Shot Controlled Generation with Encoder-Decoder Transformers
    • [cs.CR]Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
    • [cs.CV]A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks
    • [cs.CV]A deep learning approach to clustering visual arts
    • [cs.CV]An Image Forensic Technique Based on JPEG Ghosts
    • [cs.CV]Attention-based Partial Face Recognition
    • [cs.CV]AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation
    • [cs.CV]Bridge the Gap Between Model-based and Model-free Human Reconstruction
    • [cs.CV]Calibration and Auto-Refinement for Light Field Cameras
    • [cs.CV]Conterfactual Generative Zero-Shot Semantic Segmentation
    • [cs.CV]Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained Devices
    • [cs.CV]Instance-Level Task Parameters: A Robust Multi-task Weighting Framework
    • [cs.CV]K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
    • [cs.CV]Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
    • [cs.CV]Learning the Precise Feature for Cluster Assignment
    • [cs.CV]MlTr: Multi-label Classification with Transformer
    • [cs.CV]Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds
    • [cs.CV]Part-aware Panoptic Segmentation
    • [cs.CV]Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization
    • [cs.CV]Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification
    • [cs.CV]Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales
    • [cs.CV]Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos
    • [cs.CV]SimSwap: An Efficient Framework For High Fidelity Face Swapping
    • [cs.CV]Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario
    • [cs.CV]Spectral Unsupervised Domain Adaptation for Visual Recognition
    • [cs.CV]Step-Wise Hierarchical Alignment Network for Image-Text Matching
    • [cs.CV]Team RUC_AIM3 Technical Report at ActivityNet 2021: Entities Object Localization
    • [cs.CV]ViT-Inception-GAN for Image Colourising
    • [cs.CY]Identifying and Supporting Financially Vulnerable Consumers in a Privacy-Preserving Manner: A Use Case Using Decentralised Identifiers and Verifiable Credentials
    • [cs.DC]Bandwidth-Optimal Random Shuffling for GPUs
    • [cs.DC]IoT Virtualization with ML-based Information Extraction
    • [cs.DC]Stochastic modelling of blockchain consensus
    • [cs.DC]Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instance
    • [cs.DL]A dataset of mentorship in science with semantic and demographic estimations
    • [cs.GT]Multi-Receiver Online Bayesian Persuasion
    • [cs.HC]Smart textiles that teach: Fabric-based haptic device improves the rate of motor learning
    • [cs.HC]States of confusion: Eye and Head tracking reveal surgeons’ confusion during arthroscopic surgery
    • [cs.IR]A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
    • [cs.IR]DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
    • [cs.IR]GRASP: Graph Alignment through Spectral Signatures
    • [cs.IR]Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
    • [cs.IR]Predicting Knowledge Gain during Web Search based on Multimedia Resource Consumption
    • [cs.IT]AI Empowered Resource Management for Future Wireless Networks
    • [cs.IT]Cell-Free Symbiotic Radio: Channel Estimation Method and Achievable Rate Analysis
    • [cs.IT]Encoding of probability distributions for Asymmetric Numeral Systems
    • [cs.IT]Non-orthogonal Multiple Access for Multi-cell Indoor VLC
    • [cs.IT]On the Bound of Energy Consumption in Cellular IoT Networks
    • [cs.IT]Wireless Communication with Extremely Large-Scale Intelligent Reflecting Surface
    • [cs.LG]A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
    • [cs.LG]A Novel Approach to Lifelong Learning: The Plastic Support Structure
    • [cs.LG]Adversarial Option-Aware Hierarchical Imitation Learning
    • [cs.LG]Adversarial Robustness through the Lens of Causality
    • [cs.LG]Adversarial purification with Score-based generative models
    • [cs.LG]An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things
    • [cs.LG]Analyzing the Travel and Charging Behavior of Electric Vehicles — A Data-driven Approach
    • [cs.LG]Automatic Risk Adaptation in Distributional Reinforcement Learning
    • [cs.LG]Coded-InvNet for Resilient Prediction Serving Systems
    • [cs.LG]Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning
    • [cs.LG]DORO: Distributional and Outlier Robust Optimization
    • [cs.LG]Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
    • [cs.LG]Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
    • [cs.LG]Deep Conditional Gaussian Mixture Model for Constrained Clustering
    • [cs.LG]Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems
    • [cs.LG]Exploiting Record Similarity for Practical Vertical Federated Learning
    • [cs.LG]Feature Selection Tutorial with Python Examples
    • [cs.LG]GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
    • [cs.LG]Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation
    • [cs.LG]Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
    • [cs.LG]Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
    • [cs.LG]Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
    • [cs.LG]Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
    • [cs.LG]HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions
    • [cs.LG]HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
    • [cs.LG]Hybrid Generative-Contrastive Representation Learning
    • [cs.LG]Inter-domain Multi-relational Link Prediction
    • [cs.LG]Interpreting Expert Annotation Differences in Animal Behavior
    • [cs.LG]Invariant Information Bottleneck for Domain Generalization
    • [cs.LG]Is Homophily a Necessity for Graph Neural Networks?
    • [cs.LG]JKOnet: Proximal Optimal Transport Modeling of Population Dynamics
    • [cs.LG]Keyframe-Focused Visual Imitation Learning
    • [cs.LG]Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
    • [cs.LG]Label Noise SGD Provably Prefers Flat Global Minimizers
    • [cs.LG]Learning to Pool in Graph Neural Networks for Extrapolation
    • [cs.LG]Locally Sparse Networks for Interpretable Predictions
    • [cs.LG]LocoProp: Enhancing BackProp via Local Loss Optimization
    • [cs.LG]Meta-Adaptive Nonlinear Control: Theory and Algorithms
    • [cs.LG]Neural Symbolic Regression that Scales
    • [cs.LG]Nonmyopic Multifidelity Active Search
    • [cs.LG]Offline Reinforcement Learning as Anti-Exploration
    • [cs.LG]Online Continual Adaptation with Active Self-Training
    • [cs.LG]Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles
    • [cs.LG]Policy Gradient Bayesian Robust Optimization for Imitation Learning
    • [cs.LG]Preferential Temporal Difference Learning
    • [cs.LG]Probability Paths and the Structure of Predictions over Time
    • [cs.LG]Safe Reinforcement Learning with Linear Function Approximation
    • [cs.LG]Sparse Bayesian Learning via Stepwise Regression
    • [cs.LG]Survey of Image Based Graph Neural Networks
    • [cs.LG]TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
    • [cs.LG]Taylor Expansion of Discount Factors
    • [cs.LG]The Complexity of Sparse Tensor PCA
    • [cs.LG]The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
    • [cs.LG]Topological Detection of Trojaned Neural Networks
    • [cs.LG]Towards Understanding Generalization via Decomposing Excess Risk Dynamics
    • [cs.LG]TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning
    • [cs.LG]WAX-ML: A Python library for machine learning and feedback loops on streaming data
    • [cs.LG]What Can Knowledge Bring to Machine Learning? — A Survey of Low-shot Learning for Structured Data
    • [cs.MA]A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
    • [cs.NE]Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
    • [cs.NE]Generalized Moving Peaks Benchmark
    • [cs.NE]Problem-solving benefits of down-sampled lexicase selection
    • [cs.NE]PyGAD: An Intuitive Genetic Algorithm Python Library
    • [cs.NI]Acceleration-as-a-μService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs
    • [cs.NI]DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles
    • [cs.RO]Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control
    • [cs.RO]Autonomous Fire Fighting with a UAV-UGV Team at MBZIRC 2020
    • [cs.SD]Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
    • [cs.SD]HUI-Audio-Corpus-German: A high quality TTS dataset
    • [cs.SD]Spoken Style Learning with Multi-modal Hierarchical Context Encoding for Conversational Text-to-Speech Synthesis
    • [cs.SD]Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
    • [cs.SE]PSB2: The Second Program Synthesis Benchmark Suite
    • [cs.SE]TIRA: An OpenAPI Extension and Toolbox for GDPR Transparency in RESTful Architectures
    • [cs.SI]Deception Detection in Group Video Conversations using Dynamic Interaction Networks
    • [cs.SI]Maximizing Influence of Leaders in Social Networks
    • [cs.SI]Neural Higher-order Pattern (Motif) Prediction in Temporal Networks
    • [eess.AS]Improving RNN-T ASR Performance with Date-Time and Location Awareness
    • [eess.IV]KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation
    • [eess.SY]Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions
    • [math.OC]A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
    • [math.ST]Distributionally robust tail bounds based on Wasserstein distance and 今日学术视野(2021.6.15) - 图1-divergence
    • [math.ST]Neural Networks for Partially Linear Quantile Regression
    • [math.ST]New challenges in covariance estimation: multiple structures and coarse quantization
    • [q-bio.PE]Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling
    • [stat.AP]A Bayesian spatio-temporal error correction analysis of markets during the Finnish 1860s famine
    • [stat.AP]Statistical modeling of on-street parking lot occupancy in smart cities
    • [stat.ME]A new goodness of fit test for uniform distribution with censored observations
    • [stat.ME]Conformal Bayesian Computation
    • [stat.ME]DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm
    • [stat.ME]Parameter Estimation and Model-Based Clustering with Spherical Normal Distribution on the Unit Hypersphere
    • [stat.ME]Shall we count the living or the dead?
    • [stat.ML]A Unified Framework for Constructing Nonconvex Regularizations
    • [stat.ML]Continuous Herded Gibbs Sampling
    • [stat.ML]Learning the optimal regularizer for inverse problems
    • [stat.ML]Measuring the sensitivity of Gaussian processes to kernel choice
    • [stat.ML]Model Selection for Bayesian Autoencoders
    • [stat.ML]Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
    • [stat.ML]Neural Optimization Kernel: Towards Robust Deep Learning
    • [stat.ML]On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
    • [stat.ML]Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
    • [stat.ML]PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior
    • [stat.ML]Unsupervised Anomaly Detection Ensembles using Item Response Theory

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

    • [astro-ph.EP]Recovery of Meteorites Using an Autonomous Drone and Machine Learning
    Robert I. Citron, Peter Jenniskens, Christopher Watkins, Sravanthi Sinha, Amar Shah, Chedy Raissi, Hadrien Devillepoix, Jim Albers
    http://arxiv.org/abs/2106.06523v1

    • [cs.AI]DECORE: Deep Compression with Reinforcement Learning
    Manoj Alwani, Vashisht Madhavan, Yang Wang
    http://arxiv.org/abs/2106.06091v1

    • [cs.AI]DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
    Daochen Zha, Jingru Xie, Wenye Ma, Sheng Zhang, Xiangru Lian, Xia Hu, Ji Liu
    http://arxiv.org/abs/2106.06135v1

    • [cs.AR]FPGA-based Near-Memory Acceleration of Modern Data-Intensive Applications
    Gagandeep Singh, Mohammed Alser, Damla Senol Cali, Dionysios Diamantopoulos, Juan Gómez-Luna, Henk Corporaal, Onur Mutlu
    http://arxiv.org/abs/2106.06433v1

    • [cs.CE]Data-Driven Multiscale Design of Cellular Composites with Multiclass Microstructures for Natural Frequency Maximization
    Liwei Wang, Anton van Beek, Daicong Da, Yu-Chin Chan, Ping Zhu, Wei Chen
    http://arxiv.org/abs/2106.06478v1

    • [cs.CL]A Discussion on Building Practical NLP Leaderboards: The Case of Machine Translation
    Sebastin Santy, Prasanta Bhattacharya
    http://arxiv.org/abs/2106.06292v1

    • [cs.CL]A comprehensive solution to retrieval-based chatbot construction
    Kristen Moore, Shenjun Zhong, Zhen He, Torsten Rudolf, Nils Fisher, Brandon Victor, Neha Jindal
    http://arxiv.org/abs/2106.06139v1

    • [cs.CL]Assessing Political Prudence of Open-domain Chatbots
    Yejin Bang, Nayeon Lee, Etsuko Ishii, Andrea Madotto, Pascale Fung
    http://arxiv.org/abs/2106.06157v1

    • [cs.CL]BoB: BERT Over BERT for Training Persona-based Dialogue Models from Limited Personalized Data
    Haoyu Song, Yan Wang, Kaiyan Zhang, Wei-Nan Zhang, Ting Liu
    http://arxiv.org/abs/2106.06169v1

    • [cs.CL]Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation
    Xin Liu, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Min Zhang, Haiying Zhang, Jinsong Su
    http://arxiv.org/abs/2106.06125v1

    • [cs.CL]CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
    Henry Weld, Guanghao Huang, Jean Lee, Tongshu Zhang, Kunze Wang, Xinghong Guo, Siqu Long, Josiah Poon, Soyeon Caren Han
    http://arxiv.org/abs/2106.06213v1

    • [cs.CL]Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
    Matthew Finlayson, Aaron Mueller, Stuart Shieber, Sebastian Gehrmann, Tal Linzen, Yonatan Belinkov
    http://arxiv.org/abs/2106.06087v1

    • [cs.CL]Dynamic Language Models for Continuously Evolving Content
    Spurthi Amba Hombaiah, Tao Chen, Mingyang Zhang, Michael Bendersky, Marc Najork
    http://arxiv.org/abs/2106.06297v1

    • [cs.CL]FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
    Jean Lee, Hoyoul Luis Youn, Nicholas Stevens, Josiah Poon, Soyeon Caren Han
    http://arxiv.org/abs/2106.06247v1

    • [cs.CL]From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding
    Shan Wu, Bo Chen, Chunlei Xin, Xianpei Han, Le Sun, Weipeng Zhang, Jiansong Chen, Fan Yang, Xunliang Cai
    http://arxiv.org/abs/2106.06228v1

    • [cs.CL]Graph Neural Networks for Natural Language Processing: A Survey
    Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, Bo Long
    http://arxiv.org/abs/2106.06090v1

    • [cs.CL]How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue Corpus
    Felix Gervits, Antonio Roque, Gordon Briggs, Matthias Scheutz, Matthew Marge
    http://arxiv.org/abs/2106.06504v1

    • [cs.CL]Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment
    Zewen Chi, Li Dong, Bo Zheng, Shaohan Huang, Xian-Ling Mao, Heyan Huang, Furu Wei
    http://arxiv.org/abs/2106.06381v1

    • [cs.CL]Local Explanation of Dialogue Response Generation
    Yi-Lin Tuan, Connor Pryor, Wenhu Chen, Lise Getoor, William Yang Wang
    http://arxiv.org/abs/2106.06528v1

    • [cs.CL]N-Best ASR Transformer: Enhancing SLU Performance using Multiple ASR Hypotheses
    Karthik Ganesan, Pakhi Bamdev, Jaivarsan B, Amresh Venugopal, Abhinav Tushar
    http://arxiv.org/abs/2106.06519v1

    • [cs.CL]NAAQA: A Neural Architecture for Acoustic Question Answering
    Jerome Abdelnour, Jean Rouat, Giampiero Salvi
    http://arxiv.org/abs/2106.06147v1

    • [cs.CL]Nested and Balanced Entity Recognition using Multi-Task Learning
    Andreas Waldis, Luca Mazzola
    http://arxiv.org/abs/2106.06216v1

    • [cs.CL]One Sense Per Translation
    Bradley Hauer, Grzegorz Kondrak
    http://arxiv.org/abs/2106.06082v1

    • [cs.CL]Semi-Supervised and Unsupervised Sense Annotation via Translations
    Bradley Hauer, Grzegorz Kondrak, Yixing Luan, Arnob Mallik, Lili Mou
    http://arxiv.org/abs/2106.06462v1

    • [cs.CL]Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings
    Éric Le Ferrand, Steven Bird, Laurent Besacier
    http://arxiv.org/abs/2106.06160v1

    • [cs.CL]Sprachsynthese — State-of-the-Art in englischer und deutscher Sprache
    René Peinl
    http://arxiv.org/abs/2106.06230v1

    • [cs.CL]TellMeWhy: A Dataset for Answering Why-Questions in Narratives
    Yash Kumar Lal, Nathanael Chambers, Raymond Mooney, Niranjan Balasubramanian
    http://arxiv.org/abs/2106.06132v1

    • [cs.CL]To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
    Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano
    http://arxiv.org/abs/2106.06363v1

    • [cs.CL]Towards User-Driven Neural Machine Translation
    Huan Lin, Liang Yao, Baosong Yang, Dayiheng Liu, Haibo Zhang, Weihua Luo, Degen Huang, Jinsong Su
    http://arxiv.org/abs/2106.06200v1

    • [cs.CL]Turn the Combination Lock: Learnable Textual Backdoor Attacks via Word Substitution
    Fanchao Qi, Yuan Yao, Sophia Xu, Zhiyuan Liu, Maosong Sun
    http://arxiv.org/abs/2106.06361v1

    • [cs.CL]Zero-Shot Controlled Generation with Encoder-Decoder Transformers
    Devamanyu Hazarika, Mahdi Namazifar, Dilek Hakkani-Tür
    http://arxiv.org/abs/2106.06411v1

    • [cs.CR]Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
    Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher
    http://arxiv.org/abs/2106.06089v1

    • [cs.CV]A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks
    Karrar Al-Kaabi, Reza Monsefi, Davood Zabihzadeh
    http://arxiv.org/abs/2106.06420v1

    • [cs.CV]A deep learning approach to clustering visual arts
    Giovanna Castellano, Gennaro Vessio
    http://arxiv.org/abs/2106.06234v1

    • [cs.CV]An Image Forensic Technique Based on JPEG Ghosts
    Divakar Singh
    http://arxiv.org/abs/2106.06439v1

    • [cs.CV]Attention-based Partial Face Recognition
    Stefan Hörmann, Zeyuan Zhang, Martin Knoche, Torben Teepe, Gerhard Rigoll
    http://arxiv.org/abs/2106.06415v1

    • [cs.CV]AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation
    Mingxiang Chen, Zhanguo Chang, Haonan Lu, Bitao Yang, Zhuang Li, Liufang Guo, Zhecheng Wang
    http://arxiv.org/abs/2106.06250v1

    • [cs.CV]Bridge the Gap Between Model-based and Model-free Human Reconstruction
    Lixiang Lin, Jianke Zhu
    http://arxiv.org/abs/2106.06313v1

    • [cs.CV]Calibration and Auto-Refinement for Light Field Cameras
    Yuriy Anisimov, Gerd Reis, Didier Stricker
    http://arxiv.org/abs/2106.06181v1

    • [cs.CV]Conterfactual Generative Zero-Shot Semantic Segmentation
    Feihong Shen, Jun Liu, Ping Hu
    http://arxiv.org/abs/2106.06360v1

    • [cs.CV]Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained Devices
    R. Gallardo García, S. Jarquín Rodríguez, B. Beltrán Martínez, C. Hernández Gracidas, R. Martínez Torres
    http://arxiv.org/abs/2106.06505v1

    • [cs.CV]Instance-Level Task Parameters: A Robust Multi-task Weighting Framework
    Pavan Kumar Anasosalu Vasu, Shreyas Saxena, Oncel Tuzel
    http://arxiv.org/abs/2106.06129v1

    • [cs.CV]K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
    Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu
    http://arxiv.org/abs/2106.06442v1

    • [cs.CV]Learning Compositional Shape Priors for Few-Shot 3D Reconstruction
    Mateusz Michalkiewicz, Stavros Tsogkas, Sarah Parisot, Mahsa Baktashmotlagh, Anders Eriksson, Eugene Belilovsky
    http://arxiv.org/abs/2106.06440v1

    • [cs.CV]Learning the Precise Feature for Cluster Assignment
    Yanhai Gan, Xinghui Dong, Huiyu Zhou, Feng Gao, Junyu Dong
    http://arxiv.org/abs/2106.06159v1

    • [cs.CV]MlTr: Multi-label Classification with Transformer
    Xing Cheng, Hezheng Lin, Xiangyu Wu, Fan Yang, Dong Shen, Zhongyuan Wang, Nian Shi, Honglin Liu
    http://arxiv.org/abs/2106.06195v1

    • [cs.CV]Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds
    Emre Can Kaya, Ioan Tabus
    http://arxiv.org/abs/2106.06482v1

    • [cs.CV]Part-aware Panoptic Segmentation
    Daan de Geus, Panagiotis Meletis, Chenyang Lu, Xiaoxiao Wen, Gijs Dubbelman
    http://arxiv.org/abs/2106.06351v1

    • [cs.CV]Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localization
    Weichen Chen, Xinyi Yu, Linlin Ou
    http://arxiv.org/abs/2106.06485v1

    • [cs.CV]Refining Pseudo Labels with Clustering Consensus over Generations for Unsupervised Object Re-identification
    Xiao Zhang, Yixiao Ge, Yu Qiao, Hongsheng Li
    http://arxiv.org/abs/2106.06133v1

    • [cs.CV]Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales
    Ylva Jansson, Tony Lindeberg
    http://arxiv.org/abs/2106.06418v1

    • [cs.CV]Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos
    Gen-Bing Liong, John See, Lai-Kuan Wong
    http://arxiv.org/abs/2106.06489v1

    • [cs.CV]SimSwap: An Efficient Framework For High Fidelity Face Swapping
    Renwang Chen, Xuanhong Chen, Bingbing Ni, Yanhao Ge
    http://arxiv.org/abs/2106.06340v1

    • [cs.CV]Small Object Detection for Near Real-Time Egocentric Perception in a Manual Assembly Scenario
    Hooman Tavakoli, Snehal Walunj, Parsha Pahlevannejad, Christiane Plociennik, Martin Ruskowski
    http://arxiv.org/abs/2106.06403v1

    • [cs.CV]Spectral Unsupervised Domain Adaptation for Visual Recognition
    Jingyi Zhang, Jiaxing Huang, Shijian Lu
    http://arxiv.org/abs/2106.06112v1

    • [cs.CV]Step-Wise Hierarchical Alignment Network for Image-Text Matching
    Zhong Ji, Kexin Chen, Haoran Wang
    http://arxiv.org/abs/2106.06509v1

    • [cs.CV]Team RUC_AIM3 Technical Report at ActivityNet 2021: Entities Object Localization
    Ludan Ruan, Jieting Chen, Yuqing Song, Shizhe Chen, Qin Jin
    http://arxiv.org/abs/2106.06138v1

    • [cs.CV]ViT-Inception-GAN for Image Colourising
    Tejas Bana, Jatan Loya, Siddhant Kulkarni
    http://arxiv.org/abs/2106.06321v1

    • [cs.CY]Identifying and Supporting Financially Vulnerable Consumers in a Privacy-Preserving Manner: A Use Case Using Decentralised Identifiers and Verifiable Credentials
    Tasos Spiliotopoulos, Dave Horsfall, Magdalene Ng, Kovila Coopamootoo, Aad van Moorsel, Karen Elliott
    http://arxiv.org/abs/2106.06053v1

    • [cs.DC]Bandwidth-Optimal Random Shuffling for GPUs
    Rory Mitchell, Daniel Stokes, Eibe Frank, Geoffrey Holmes
    http://arxiv.org/abs/2106.06161v1

    • [cs.DC]IoT Virtualization with ML-based Information Extraction
    Martin Bauer
    http://arxiv.org/abs/2106.06022v1

    • [cs.DC]Stochastic modelling of blockchain consensus
    Claudio J. Tessone, Paolo Tasca, Flavio Iannelli
    http://arxiv.org/abs/2106.06465v1

    • [cs.DC]Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instance
    Roland Mathá, Dragi Kimovski, Anatoliy Zabrovskiy, Christian Timmerer, Radu Prodan
    http://arxiv.org/abs/2106.06242v1

    • [cs.DL]A dataset of mentorship in science with semantic and demographic estimations
    Qing Ke, Lizhen Liang, Ying Ding, Stephen V. David, Daniel E. Acuna
    http://arxiv.org/abs/2106.06487v1

    • [cs.GT]Multi-Receiver Online Bayesian Persuasion
    Matteo Castiglioni, Alberto Marchesi, Andrea Celli, Nicola Gatti
    http://arxiv.org/abs/2106.06480v1

    • [cs.HC]Smart textiles that teach: Fabric-based haptic device improves the rate of motor learning
    Vivek Ramachandran, Fabian Schilling, Amy Wu, Dario Floreano
    http://arxiv.org/abs/2106.06332v1

    • [cs.HC]States of confusion: Eye and Head tracking reveal surgeons’ confusion during arthroscopic surgery
    Benedikt Hosp, Myat Su Yin, peter Haddawy, Ratthapoom Watcharporas, paphon Sa-ngasoonsong, Enkelejda Kasneci
    http://arxiv.org/abs/2106.06261v1

    • [cs.IR]A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
    Bin Hao, Min Zhang, Weizhi Ma, Shaoyun Shi, Xinxing Yu, Houzhi Shan, Yiqun Liu, Shaoping Ma
    http://arxiv.org/abs/2106.06467v1

    • [cs.IR]DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
    Chuhan Wu, Fangzhao Wu, Yongfeng Huang
    http://arxiv.org/abs/2106.06258v1

    • [cs.IR]GRASP: Graph Alignment through Spectral Signatures
    Judith Hermanns, Anton Tsitsulin, Marina Munkhoeva, Alex Bronstein, Davide Mottin, Panagiotis Karras
    http://arxiv.org/abs/2106.05729v2

    • [cs.IR]Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation
    Ziwei Fan, Zhiwei Liu, Lei Zheng, Shen Wang, Philip S. Yu
    http://arxiv.org/abs/2106.06165v1

    • [cs.IR]Predicting Knowledge Gain during Web Search based on Multimedia Resource Consumption
    Christian Otto, Ran Yu, Georg Pardi, Johannes von Hoyer, Markus Rokicki, Anett Hoppe, Peter Holtz, Yvonne Kammerer, Stefan Dietze, Ralph Ewerth
    http://arxiv.org/abs/2106.06244v1

    • [cs.IT]AI Empowered Resource Management for Future Wireless Networks
    Yifei Shen, Jun Zhang, S. H. Song, Khaled B. Letaief
    http://arxiv.org/abs/2106.06178v1

    • [cs.IT]Cell-Free Symbiotic Radio: Channel Estimation Method and Achievable Rate Analysis
    Zhuoyin Dai, Ruoguang Li, Jingran Xu, Yong Zeng, Shi Jin
    http://arxiv.org/abs/2106.06148v1

    • [cs.IT]Encoding of probability distributions for Asymmetric Numeral Systems
    Jarek Duda
    http://arxiv.org/abs/2106.06438v1

    • [cs.IT]Non-orthogonal Multiple Access for Multi-cell Indoor VLC
    T. Uday, Abhinav Kumar, L. Natarajan
    http://arxiv.org/abs/2106.06187v1

    • [cs.IT]On the Bound of Energy Consumption in Cellular IoT Networks
    Bassel Al Homssi, Akram Al-Hourani, Sathyanarayanan Chandrasekharan, Karina Mabell Gomez, Sithamparanathan Kandeepan
    http://arxiv.org/abs/2106.06008v1

    • [cs.IT]Wireless Communication with Extremely Large-Scale Intelligent Reflecting Surface
    Chao Feng, Haiquan Lu, Yong Zeng, Shi Jin, Rui Zhang
    http://arxiv.org/abs/2106.06106v1

    • [cs.LG]A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
    Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger
    http://arxiv.org/abs/2106.06079v1

    • [cs.LG]A Novel Approach to Lifelong Learning: The Plastic Support Structure
    Georges Kanaan, Kai Wen Zheng, Lucas Fenaux
    http://arxiv.org/abs/2106.06298v1

    • [cs.LG]Adversarial Option-Aware Hierarchical Imitation Learning
    Mingxuan Jing, Wenbing Huang, Fuchun Sun, Xiaojian Ma, Tao Kong, Chuang Gan, Lei Li
    http://arxiv.org/abs/2106.05530v2

    • [cs.LG]Adversarial Robustness through the Lens of Causality
    Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang
    http://arxiv.org/abs/2106.06196v1

    • [cs.LG]Adversarial purification with Score-based generative models
    Jongmin Yoon, Sung Ju Hwang, Juho Lee
    http://arxiv.org/abs/2106.06041v1

    • [cs.LG]An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things
    Matteo Antonio Scrugli, Daniela Loi, Luigi Raffo, Paolo Meloni
    http://arxiv.org/abs/2106.06498v1

    • [cs.LG]Analyzing the Travel and Charging Behavior of Electric Vehicles — A Data-driven Approach
    Sina Baghali, Samiul Hasan, Zhaomiao Guo
    http://arxiv.org/abs/2106.06475v1

    • [cs.LG]Automatic Risk Adaptation in Distributional Reinforcement Learning
    Frederik Schubert, Theresa Eimer, Bodo Rosenhahn, Marius Lindauer
    http://arxiv.org/abs/2106.06317v1

    • [cs.LG]Coded-InvNet for Resilient Prediction Serving Systems
    Tuan Dinh, Kangwook Lee
    http://arxiv.org/abs/2106.06445v1

    • [cs.LG]Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning
    Shengchao Yan, Tim Welschehold, Daniel Büscher, Wolfram Burgard
    http://arxiv.org/abs/2106.06369v1

    • [cs.LG]DORO: Distributional and Outlier Robust Optimization
    Runtian Zhai, Chen Dan, J. Zico Kolter, Pradeep Ravikumar
    http://arxiv.org/abs/2106.06142v1

    • [cs.LG]Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
    Daniel J. B. Harrold, Jun Cao, Zhong Fan
    http://arxiv.org/abs/2106.06061v1

    • [cs.LG]Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
    Eugene Belilovsky, Louis Leconte, Lucas Caccia, Michael Eickenberg, Edouard Oyallon
    http://arxiv.org/abs/2106.06401v1

    • [cs.LG]Deep Conditional Gaussian Mixture Model for Constrained Clustering
    Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt
    http://arxiv.org/abs/2106.06385v1

    • [cs.LG]Dictionary and prior learning with unrolled algorithms for unsupervised inverse problems
    Benoît Malézieux, Thomas Moreau, Matthieu Kowalski
    http://arxiv.org/abs/2106.06338v1

    • [cs.LG]Exploiting Record Similarity for Practical Vertical Federated Learning
    Zhaomin Wu, Qinbin Li, Bingsheng He
    http://arxiv.org/abs/2106.06312v1

    • [cs.LG]Feature Selection Tutorial with Python Examples
    Padraig Cunningham, Bahavathy Kathirgamanathan, Sarah Jane Delany
    http://arxiv.org/abs/2106.06437v1

    • [cs.LG]GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
    Jiajun Fan, Changnan Xiao, Yue Huang
    http://arxiv.org/abs/2106.06232v1

    • [cs.LG]Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation
    Xuanli He, Islam Nassar, Jamie Kiros, Gholamreza Haffari, Mohammad Norouzi
    http://arxiv.org/abs/2106.06168v1

    • [cs.LG]Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
    Jialin Dong, Da Zheng, Lin F. Yang, Geroge Karypis
    http://arxiv.org/abs/2106.06150v1

    • [cs.LG]Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
    Kazuki Irie, Imanol Schlag, Róbert Csordás, Jürgen Schmidhuber
    http://arxiv.org/abs/2106.06295v1

    • [cs.LG]Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent
    Samira Abnar, Rianne van den Berg, Golnaz Ghiasi, Mostafa Dehghani, Nal Kalchbrenner, Hanie Sedghi
    http://arxiv.org/abs/2106.06080v1

    • [cs.LG]Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
    Seongjun Yun, Minbyul Jeong, Sungdong Yoo, Seunghun Lee, Sean S. Yi, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim
    http://arxiv.org/abs/2106.06218v1

    • [cs.LG]HIFI: Anomaly Detection for Multivariate Time Series with High-order Feature Interactions
    Liwei Deng, Xuanhao Chen, Yan Zhao, Kai Zheng
    http://arxiv.org/abs/2106.06167v1

    • [cs.LG]HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML
    Sebastian Pineda Arango, Hadi S. Jomaa, Martin Wistuba, Josif Grabocka
    http://arxiv.org/abs/2106.06257v1

    • [cs.LG]Hybrid Generative-Contrastive Representation Learning
    Saehoon Kim, Sungwoong Kim, Juho Lee
    http://arxiv.org/abs/2106.06162v1

    • [cs.LG]Inter-domain Multi-relational Link Prediction
    Luu Huu Phuc, Koh Takeuchi, Seiji Okajima, Arseny Tolmachev, Tomoyoshi Takebayashi, Koji Maruhashi, Hisashi Kashima
    http://arxiv.org/abs/2106.06171v1

    • [cs.LG]Interpreting Expert Annotation Differences in Animal Behavior
    Megan Tjandrasuwita, Jennifer J. Sun, Ann Kennedy, Swarat Chaudhuri, Yisong Yue
    http://arxiv.org/abs/2106.06114v1

    • [cs.LG]Invariant Information Bottleneck for Domain Generalization
    Bo Li, Yifei Shen, Yezhen Wang, Wenzhen Zhu, Colorado J. Reed, Tong Che, Jun Zhang, Dongsheng Li, Kurt Keutzer, Han Zhao
    http://arxiv.org/abs/2106.06333v1

    • [cs.LG]Is Homophily a Necessity for Graph Neural Networks?
    Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang
    http://arxiv.org/abs/2106.06134v1

    • [cs.LG]JKOnet: Proximal Optimal Transport Modeling of Population Dynamics
    Charlotte Bunne, Laetitia Meng-Papaxanthos, Andreas Krause, Marco Cuturi
    http://arxiv.org/abs/2106.06345v1

    • [cs.LG]Keyframe-Focused Visual Imitation Learning
    Chuan Wen, Jierui Lin, Jianing Qian, Yang Gao, Dinesh Jayaraman
    http://arxiv.org/abs/2106.06452v1

    • [cs.LG]Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks
    Nezihe Merve Gürel, Xiangyu Qi, Luka Rimanic, Ce Zhang, Bo Li
    http://arxiv.org/abs/2106.06235v1

    • [cs.LG]Label Noise SGD Provably Prefers Flat Global Minimizers
    Alex Damian, Tengyu Ma, Jason Lee
    http://arxiv.org/abs/2106.06530v1

    • [cs.LG]Learning to Pool in Graph Neural Networks for Extrapolation
    Jihoon Ko, Taehyung Kwon, Kijung Shin, Juho Lee
    http://arxiv.org/abs/2106.06210v1

    • [cs.LG]Locally Sparse Networks for Interpretable Predictions
    Junchen Yang, Ofir Lindenbaum, Yuval Kluger
    http://arxiv.org/abs/2106.06468v1

    • [cs.LG]LocoProp: Enhancing BackProp via Local Loss Optimization
    Ehsan Amid, Rohan Anil, Manfred K. Warmuth
    http://arxiv.org/abs/2106.06199v1

    • [cs.LG]Meta-Adaptive Nonlinear Control: Theory and Algorithms
    Guanya Shi, Kamyar Azizzadenesheli, Soon-Jo Chung, Yisong Yue
    http://arxiv.org/abs/2106.06098v1

    • [cs.LG]Neural Symbolic Regression that Scales
    Luca Biggio, Tommaso Bendinelli, Alexander Neitz, Aurelien Lucchi, Giambattista Parascandolo
    http://arxiv.org/abs/2106.06427v1

    • [cs.LG]Nonmyopic Multifidelity Active Search
    Quan Nguyen, Arghavan Modiri, Roman Garnett
    http://arxiv.org/abs/2106.06356v1

    • [cs.LG]Offline Reinforcement Learning as Anti-Exploration
    Shideh Rezaeifar, Robert Dadashi, Nino Vieillard, Léonard Hussenot, Olivier Bachem, Olivier Pietquin, Matthieu Geist
    http://arxiv.org/abs/2106.06431v1

    • [cs.LG]Online Continual Adaptation with Active Self-Training
    Shiji Zhou, Han Zhao, Shanghang Zhang, Lianzhe Wang, Heng Chang, Zhi Wang, Wenwu Zhu
    http://arxiv.org/abs/2106.06526v1

    • [cs.LG]Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles
    Sanath Kumar Krishnamurthy, Susan Athey
    http://arxiv.org/abs/2106.06483v1

    • [cs.LG]Policy Gradient Bayesian Robust Optimization for Imitation Learning
    Zaynah Javed, Daniel S. Brown, Satvik Sharma, Jerry Zhu, Ashwin Balakrishna, Marek Petrik, Anca D. Dragan, Ken Goldberg
    http://arxiv.org/abs/2106.06499v1

    • [cs.LG]Preferential Temporal Difference Learning
    Nishanth Anand, Doina Precup
    http://arxiv.org/abs/2106.06508v1

    • [cs.LG]Probability Paths and the Structure of Predictions over Time
    Zhiyuan Lin, Hao Sheng, Sharad Goel
    http://arxiv.org/abs/2106.06515v1

    • [cs.LG]Safe Reinforcement Learning with Linear Function Approximation
    Sanae Amani, Christos Thrampoulidis, Lin F. Yang
    http://arxiv.org/abs/2106.06239v1

    • [cs.LG]Sparse Bayesian Learning via Stepwise Regression
    Sebastian Ament, Carla Gomes
    http://arxiv.org/abs/2106.06095v1

    • [cs.LG]Survey of Image Based Graph Neural Networks
    Usman Nazir, He Wang, Murtaza Taj
    http://arxiv.org/abs/2106.06307v1

    • [cs.LG]TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
    Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok
    http://arxiv.org/abs/2106.06326v1

    • [cs.LG]Taylor Expansion of Discount Factors
    Yunhao Tang, Mark Rowland, Rémi Munos, Michal Valko
    http://arxiv.org/abs/2106.06170v1

    • [cs.LG]The Complexity of Sparse Tensor PCA
    Davin Choo, Tommaso d’Orsi
    http://arxiv.org/abs/2106.06308v1

    • [cs.LG]The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
    Geoff Pleiss, John P. Cunningham
    http://arxiv.org/abs/2106.06529v1

    • [cs.LG]Topological Detection of Trojaned Neural Networks
    Songzhu Zheng, Yikai Zhang, Hubert Wagner, Mayank Goswami, Chao Chen
    http://arxiv.org/abs/2106.06469v1

    • [cs.LG]Towards Understanding Generalization via Decomposing Excess Risk Dynamics
    Jiaye Teng, Jianhao Ma, Yang Yuan
    http://arxiv.org/abs/2106.06153v1

    • [cs.LG]TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning
    Xu Chen, Junshan Wang, Kunqing Xie
    http://arxiv.org/abs/2106.06273v1

    • [cs.LG]WAX-ML: A Python library for machine learning and feedback loops on streaming data
    Emmanuel Sérié
    http://arxiv.org/abs/2106.06524v1

    • [cs.LG]What Can Knowledge Bring to Machine Learning? — A Survey of Low-shot Learning for Structured Data
    Yang Hu, Adriane Chapman, Guihua Wen, Dame Wendy Hall
    http://arxiv.org/abs/2106.06410v1

    • [cs.MA]A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
    Chao Wen, Miao Xu, Zhilin Zhang, Zhenzhe Zheng, Yuhui Wang, Xiangyu Liu, Yu Rong, Dong Xie, Xiaoyang Tan, Chuan Yu, Jian Xu, Fan Wu, Guihai Chen, Xiaoqiang Zhu
    http://arxiv.org/abs/2106.06224v1

    • [cs.NE]Automated Configuration of Genetic Algorithms by Tuning for Anytime Performance
    Furong Ye, Carola Doerr, Hao Wang, Thomas Bäck
    http://arxiv.org/abs/2106.06304v1

    • [cs.NE]Generalized Moving Peaks Benchmark
    Danial Yazdani, Juergen Branke, Mohammad Nabi Omidvar, Changhe Li, Michalis Mavrovouniotis, Trung Thanh Nguyen, Shengxiang Yang, Xin Yao
    http://arxiv.org/abs/2106.06174v1

    • [cs.NE]Problem-solving benefits of down-sampled lexicase selection
    Thomas Helmuth, Lee Spector
    http://arxiv.org/abs/2106.06085v1

    • [cs.NE]PyGAD: An Intuitive Genetic Algorithm Python Library
    Ahmed Fawzy Gad
    http://arxiv.org/abs/2106.06158v1

    • [cs.NI]Acceleration-as-a-μService: A Cloud-native Monte-Carlo Option Pricing Engine on CPUs, GPUs and Disaggregated FPGAs
    Dionysios Diamantopoulos, Raphael Polig, Burkhard Ringlein, Mitra Purandare, Beat Weiss, Christoph Hagleitner, Mark Lantz, Francois Abel
    http://arxiv.org/abs/2106.06293v1

    • [cs.NI]DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles
    Anum Talpur, Mohan Gurusamy
    http://arxiv.org/abs/2106.06291v1

    • [cs.RO]Analyzing Neural Jacobian Methods in Applications of Visual Servoing and Kinematic Control
    Michael Przystupa, Masood Dehghan, Martin Jagersand, A. Rupam Mahmood
    http://arxiv.org/abs/2106.06083v1

    • [cs.RO]Autonomous Fire Fighting with a UAV-UGV Team at MBZIRC 2020
    Jan Quenzel, Malte Splietker, Dmytro Pavlichenko, Daniel Schleich, Christian Lenz, Max Schwarz, Michael Schreiber, Marius Beul, Sven Behnke
    http://arxiv.org/abs/2106.06444v1

    • [cs.SD]Catch-A-Waveform: Learning to Generate Audio from a Single Short Example
    Gal Greshler, Tamar Rott Shaham, Tomer Michaeli
    http://arxiv.org/abs/2106.06426v1

    • [cs.SD]HUI-Audio-Corpus-German: A high quality TTS dataset
    Pascal Puchtler, Johannes Wirth, René Peinl
    http://arxiv.org/abs/2106.06309v1

    • [cs.SD]Spoken Style Learning with Multi-modal Hierarchical Context Encoding for Conversational Text-to-Speech Synthesis
    Jingbei Li, Yi Meng, Chenyi Li, Zhiyong Wu, Helen Meng, Chao Weng, Dan Su
    http://arxiv.org/abs/2106.06233v1

    • [cs.SD]Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
    Tomi Kinnunen, Andreas Nautsch, Md Sahidullah, Nicholas Evans, Xin Wang, Massimiliano Todisco, Héctor Delgado, Junichi Yamagishi, Kong Aik Lee
    http://arxiv.org/abs/2106.06362v1

    • [cs.SE]PSB2: The Second Program Synthesis Benchmark Suite
    Thomas Helmuth, Peter Kelly
    http://arxiv.org/abs/2106.06086v1

    • [cs.SE]TIRA: An OpenAPI Extension and Toolbox for GDPR Transparency in RESTful Architectures
    Elias Grünewald, Paul Wille, Frank Pallas, Maria C. Borges, Max-R. Ulbricht
    http://arxiv.org/abs/2106.06001v1

    • [cs.SI]Deception Detection in Group Video Conversations using Dynamic Interaction Networks
    Srijan Kumar, Chongyang Bai, V. S. Subrahmanian, Jure Leskovec
    http://arxiv.org/abs/2106.06163v1

    • [cs.SI]Maximizing Influence of Leaders in Social Networks
    Xiaotian Zhou, Zhongzhi Zhang
    http://arxiv.org/abs/2106.06128v1

    • [cs.SI]Neural Higher-order Pattern (Motif) Prediction in Temporal Networks
    Yunyu Liu, Jianzhu Ma, Pan Li
    http://arxiv.org/abs/2106.06039v1

    • [eess.AS]Improving RNN-T ASR Performance with Date-Time and Location Awareness
    Swayambhu Nath Ray, Soumyajit Mitra, Raghavendra Bilgi, Sri Garimella
    http://arxiv.org/abs/2106.06183v1

    • [eess.IV]KRADA: Known-region-aware Domain Alignment for Open World Semantic Segmentation
    Chenhong Zhou, Feng Liu, Chen Gong, Tongliang Liu, Bo Han, William Cheung
    http://arxiv.org/abs/2106.06237v1

    • [eess.SY]Safety of Dynamical Systems with Multiple Non-Convex Unsafe Sets Using Control Barrier Functions
    Gennaro Notomista, Matteo Saveriano
    http://arxiv.org/abs/2106.06330v1

    • [math.OC]A Decentralized Adaptive Momentum Method for Solving a Class of Min-Max Optimization Problems
    Babak Barazandeh, Tianjian Huang, George Michailidis
    http://arxiv.org/abs/2106.06075v1

    • [math.ST]Distributionally robust tail bounds based on Wasserstein distance and 今日学术视野(2021.6.15) - 图2-divergence
    Corina Birghila, Maximilian Aigner, Sebastian Engelke
    http://arxiv.org/abs/2106.06266v1

    • [math.ST]Neural Networks for Partially Linear Quantile Regression
    Qixian Zhong, Jane-Ling Wang
    http://arxiv.org/abs/2106.06225v1

    • [math.ST]New challenges in covariance estimation: multiple structures and coarse quantization
    Johannes Maly, Tianyu Yang, Sjoerd Dirksen, Holger Rauhut, Giuseppe Caire
    http://arxiv.org/abs/2106.06190v1

    • [q-bio.PE]Impact of the mesoscale structure of a bipartite ecological interaction network on its robustness through a probabilistic modeling
    Saint-Clair Chabert-Liddell, Pierre Barbillon, Sophie Donnet
    http://arxiv.org/abs/2106.06348v1

    • [stat.AP]A Bayesian spatio-temporal error correction analysis of markets during the Finnish 1860s famine
    Tiia-Maria Pasanen, Miikka Voutilainen, Jouni Helske, Harri Högmander
    http://arxiv.org/abs/2106.06268v1

    • [stat.AP]Statistical modeling of on-street parking lot occupancy in smart cities
    Marc Schneble, Göran Kauermann
    http://arxiv.org/abs/2106.06197v1

    • [stat.ME]A new goodness of fit test for uniform distribution with censored observations
    Sudheesh K. Kattumannil, Sreedevi E. P
    http://arxiv.org/abs/2106.06368v1

    • [stat.ME]Conformal Bayesian Computation
    Edwin Fong, Chris Holmes
    http://arxiv.org/abs/2106.06137v1

    • [stat.ME]DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm
    Vincent Plassier, Maxime Vono, Alain Durmus, Eric Moulines
    http://arxiv.org/abs/2106.06300v1

    • [stat.ME]Parameter Estimation and Model-Based Clustering with Spherical Normal Distribution on the Unit Hypersphere
    Kisung You
    http://arxiv.org/abs/2106.06375v1

    • [stat.ME]Shall we count the living or the dead?
    Anders Huitfeldt, Matthew P. Fox, Rhian M. Daniel, Asbjørn Hróbjartsson, Eleanor J. Murray
    http://arxiv.org/abs/2106.06316v1

    • [stat.ML]A Unified Framework for Constructing Nonconvex Regularizations
    Zhiyong Zhou
    http://arxiv.org/abs/2106.06123v1

    • [stat.ML]Continuous Herded Gibbs Sampling
    Laura M. Wolf, Marcus Baum
    http://arxiv.org/abs/2106.06430v1

    • [stat.ML]Learning the optimal regularizer for inverse problems
    Giovanni S. Alberti, Ernesto De Vito, Matti Lassas, Luca Ratti, Matteo Santacesaria
    http://arxiv.org/abs/2106.06513v1

    • [stat.ML]Measuring the sensitivity of Gaussian processes to kernel choice
    William T. Stephenson, Soumya Ghosh, Tin D. Nguyen, Mikhail Yurochkin, Sameer K. Deshpande, Tamara Broderick
    http://arxiv.org/abs/2106.06510v1

    • [stat.ML]Model Selection for Bayesian Autoencoders
    Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone
    http://arxiv.org/abs/2106.06245v1

    • [stat.ML]Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall
    Tadashi Kozuno, Pierre Ménard, Rémi Munos, Michal Valko
    http://arxiv.org/abs/2106.06279v1

    • [stat.ML]Neural Optimization Kernel: Towards Robust Deep Learning
    Yueming Lyu, Ivor Tsang
    http://arxiv.org/abs/2106.06097v1

    • [stat.ML]On Learnability via Gradient Method for Two-Layer ReLU Neural Networks in Teacher-Student Setting
    Shunta Akiyama, Taiji Suzuki
    http://arxiv.org/abs/2106.06251v1

    • [stat.ML]Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation
    Xiaohui Chen, Xu Han, Jiajing Hu, Francisco J. R. Ruiz, Liping Liu
    http://arxiv.org/abs/2106.06189v1

    • [stat.ML]PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior
    Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu
    http://arxiv.org/abs/2106.06406v1

    • [stat.ML]Unsupervised Anomaly Detection Ensembles using Item Response Theory
    Sevvandi Kandanaarachchi
    http://arxiv.org/abs/2106.06243v1