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 -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 -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