cs.AI - 人工智能 cs.AR - 硬件体系结构 cs.CG - 计算几何学 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.HC - 人机接口 cs.IR - 信息检索 cs.IT - 信息论 cs.LG - 自动学习 cs.LO - 计算逻辑 cs.NE - 神经与进化计算 cs.NI - 网络和互联网体系结构 cs.PL - 编程语言 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.CA - 古典分析与常微分方程 math.PR - 概率 math.ST - 统计理论 physics.data-an - 数据分析、 统计和概率 q-bio.GN - 基因组学 q-bio.QM - 定量方法 quant-ph - 量子物理 stat.ME - 统计方法论 stat.AP - 应用统计 stat.ML - (统计)机器学习 stat.OT - 其他统计学
• [cs.AI]How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
• [cs.AR]Cain: Automatic Code Generation for Simultaneous Convolutional Kernels on Focal-plane Sensor-processors
• [cs.CG]Geometric Moment Invariants to Motion Blur
• [cs.CL]Adv-OLM: Generating Textual Adversaries via OLM
• [cs.CL]Content Selection Network for Document-grounded Retrieval-based Chatbots
• [cs.CL]Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
• [cs.CL]Multi-sense embeddings through a word sense disambiguation process
• [cs.CL]ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
• [cs.CL]Validating Label Consistency in NER Data Annotation
• [cs.CL]Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
• [cs.CR]Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?
• [cs.CR]Introducing the Unitychain Structure: A novel blockchain-like structure that enables greater parallel processing, security, and performance for networks that leverage distributed key generation and classical consensus protocols
• [cs.CV]A two-stage data association approach for 3D Multi-object Tracking
• [cs.CV]Activity Graph Transformer for Temporal Action Localization
• [cs.CV]Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion
• [cs.CV]All-Day Object Tracking for Unmanned Aerial Vehicle
• [cs.CV]An Effective Data Augmentation for Person Re-identification
• [cs.CV]Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
• [cs.CV]CM-NAS: Rethinking Cross-Modality Neural Architectures for Visible-Infrared Person Re-Identification
• [cs.CV]COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos
• [cs.CV]DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition
• [cs.CV]Discovering Multi-Label Actor-Action Association in a Weakly Supervised Setting
• [cs.CV]FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation
• [cs.CV]Fast and Robust Certifiable Estimation of the Relative Pose Between Two Calibrated Cameras
• [cs.CV]Finger Vein Recognition by Generating Code
• [cs.CV]Fire Threat Detection From Videos with Q-Rough Sets
• [cs.CV]Generative Zero-shot Network Quantization
• [cs.CV]Hierarchical Graph-RNNs for Action Detection of Multiple Activities
• [cs.CV]Image-to-Image Translation: Methods and Applications
• [cs.CV]Learn to Dance with AIST++: Music Conditioned 3D Dance Generation
• [cs.CV]MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes
• [cs.CV]MoG-QSM: Model-based Generative Adversarial Deep Learning Network for Quantitative Susceptibility Mapping
• [cs.CV]Nonparametric clustering for image segmentation
• [cs.CV]Pre-training without Natural Images
• [cs.CV]Progressive Co-Attention Network for Fine-grained Visual Classification
• [cs.CV]Regularization via deep generative models: an analysis point of view
• [cs.CV]Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection
• [cs.CV]Segmenting Transparent Object in the Wild with Transformer
• [cs.CV]TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images
• [cs.CV]Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network
• [cs.CV]Video Summarization: Study of various techniques
• [cs.CY]”This Whole Thing Smacks of Gender”: Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis
• [cs.CY]Allocating Opportunities in a Dynami
8000
c Model of Intergenerational Mobility
• [cs.CY]MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes
• [cs.CY]The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information
• [cs.DC]Clairvoyant Prefetching for Distributed Machine Learning I/O
• [cs.DC]GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering
• [cs.DL]What is all this new MeSH about? Exploring the semantic provenance of new descriptors in the MeSH thesaurus
• [cs.HC]Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
• [cs.HC]Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization
• [cs.HC]Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation
• [cs.IR]Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
• [cs.IR]Fast Clustering of Short Text Streams Using Efficient Cluster Indexing and Dynamic Similarity Thresholds
• [cs.IR]Item Recommendation from Implicit Feedback
• [cs.IR]Joint Autoregressive and Graph Models for Software and Developer Social Networks
• [cs.IR]Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
• [cs.IR]Templates of generic geographic information for answering where-questions
• [cs.IT]Blocked and Hierarchical Disentangled Representation From Information Theory Perspective
• [cs.IT]Bounds on the Feedback Capacity of the #card=math&code=%28d%2C%5Cinfty%29)-RLL Input-Constrained Binary Erasure Channel
• [cs.IT]Dictionary-Sparse Recovery From Heavy-Tailed Measurements
• [cs.IT]Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems
• [cs.IT]Maddah-Ali-Niesen Scheme for Multi-access Coded Caching
• [cs.IT]Optimal Demand Private Coded Caching for Users with Small Buffers
• [cs.IT]Probabilistic Placement Optimization for Non-coherent and Coherent Joint Transmission in Cache-Enabled Cellular Networks
• [cs.IT]Rack-Aware Regenerating Codes with Fewer Helper Racks
• [cs.IT]Rate Region for Indirect Multiterminal Source Coding in Federated Learning
• [cs.IT]Robust spectral compressive sensing via vanilla gradient descent
• [cs.IT]Some punctured codes of several families of binary linear codes
• [cs.IT]Successive-Cancellation Decoding of Binary Polar Codes Based on Symmetric Parametrization
• [cs.IT]The Capacity of the Amplitude-Constrained Vector Gaussian Channel
• [cs.LG]A New Knowledge Gradient-based Method for Constrained Bayesian Optimization
• [cs.LG]A Note on Connectivity of Sublevel Sets in Deep Learning
• [cs.LG]An Information-Theoretic Analysis of the Impact of Task Similarity on Meta-Learning
• [cs.LG]An empirical evaluation of active inference in multi-armed bandits
• [cs.LG]Analysis of Information Flow Through U-Nets
• [cs.LG]Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
• [cs.LG]Boost then Convolve: Gradient Boosting Meets Graph Neural Networks
• [cs.LG]Can stable and accurate neural networks be computed? — On the barriers of deep learning and Smale’s 18th problem
• [cs.LG]Characterizing signal propagation to close the performance gap in unnormalized ResNets
• [cs.LG]Collaborative Teacher-Student Learning via Multiple Knowledge Transfer
• [cs.LG]Crossbreeding in Random Forest
• [cs.LG]Differential Euler: Designing a Neural Network approximator to solve the Chaotic Three Body Problem
• [cs.LG]Discussion of Ensemble Learning under the Era of Deep Learning
• [cs.LG]Distilling Interpretable Models into Human-Readable Code
• [cs.LG]Dive into Decision Trees and Forests: A Theoretical Demonstration
• [cs.LG]Do we need to go Deep? Knowledge Tracing with Big Data
• [cs.LG]Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction
• [cs.LG]Estimating Average Treatment Effects via Orthogonal Regularization
• [cs.LG]Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
• [cs.LG]From Local Pseudorandom Generators to Hardness of Learning
• [cs.LG]Invariance, encodings, and generalization: learning identity effects with neural networks
• [cs.LG]ItNet: iterative neural networks with tiny graphs for accurate and efficient anytime prediction
• [cs.LG]Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
• [cs.LG]Learning Massive Graph Embeddings on a Single Machine
• [cs.LG]Learning based signal detection for MIMO systems with unknown noise statistics
• [cs.LG]Non-Convex Compressed Sensing with Training Data
• [cs.LG]Orthogonal Least Squares Based Fast Feature Selection for Linear Classification
• [cs.LG]Out-of-Distribution Generalization Analysis via Influence Function
• [cs.LG]Overfitting for Fun and Profit: Instance-Adaptive Data Compression
• [cs.LG]Quadratic Residual Networks: A New Class of Neural Networks for Solving Forward and Inverse Problems in Physics Involving PDEs
• [cs.LG]Robust Reinforcement Learning on State Observations with Learned Optimal Adversary
• [cs.LG]Self-Adaptive Training: Bridging the Supervised and Self-Supervised Learning
• [cs.LG]Soft Genetic Programming Binary Classifiers
• [cs.LG]Stress Testing of Meta-learning Approaches for Few-shot Learning
• [cs.LG]Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment
• [cs.LO]Finite Model Theory of the Triguarded Fragment and Related Logics
• [cs.NE]Variable Division and Optimization for Constrained Multiobjective Portfolio Problems
• [cs.NI]Adversarial Machine Learning for Flooding Attacks on 5G Radio Access Network Slicing
• [cs.NI]Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
• [cs.PL]UNIT: Unifying Tensorized Instruction Compilation
• [cs.RO]Learning rich touch representations through cross-modal self-supervision
• [cs.RO]Model-based Policy Search for Partially Measurable Systems
• [cs.RO]Multi-robot energy autonomy with wind and constrained resources
• [cs.RO]Physical Reservoir Computing with Origami and its Application to Robotic Crawling
• [cs.SD]LEAF: A Learnable Frontend for Audio Classification
• [cs.SD]The Diagnosis of Asthma using Hilbert-Huang Transform and Deep Learning on Lung Sounds
• [cs.SE]Content-Based Textual File Type Detection at Scale
• [cs.SI]Density-based clustering of social networks
• [cs.SI]Synwalk — Community Detection via Random Walk Modelling
• [eess.AS]Arabic Speech Recognition by End-to-End, Modular Systems and Human
• [eess.IV]Chest X-ray lung and heart segmentation based on minimal training sets
• [eess.IV]Expectation-Maximization Regularized DeepLearning for Weakly Supervised Tumor Segmentation for Glioblastoma
• [eess.IV]GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
• [eess.IV]Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training
• [eess.IV]Weighted Fuzzy-Based PSNR for Watermarking
• [eess.SY]Data-driven sparse polynomial chaos expansion for models with dependent inputs
• [eess.SY]Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation
• [math.CA]HMC, an example of Functional Analysis applied to Algorithms in Data Mining. The convergence in
• [math.PR]A Topological Proof of Sklar’s Theorem in Arbitrary Dimensions
• [math.ST]Computation of quantile sets for bivariate data
• [math.ST]Information theoretic results for stationary time series and the Gaussian-generalized von Mises time series
• [math.ST]On detecting weak changes in the mean of CHARN models
• [math.ST]Optimal Full Ranking from Pairwise Comparisons
• [math.ST]Optimal convergence rates for the invariant density estimation of jump-diffusion processes
• [physics.data-an]Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy
• [physics.data-an]MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• [q-bio.GN]Motif Identification using CNN-based Pairwise Subsequence Alignment Score Prediction
• [q-bio.QM]Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
• [quant-ph]Enhancing Generative Models via Quantum Correlations
• [quant-ph]Noisy intermediate-scale quantum (NISQ) algorithms
• [stat
4e71
.ME]A unified method for multivariate mixed-type response regression
• [stat.AP]Correlated power time series of individual wind turbines: A data driven model approach
• [stat.AP]Customer Price Sensitivities in Competitive Automobile Insurance Markets
• [stat.AP]When the ends don’t justify the means: Learning a treatment strategy to prevent harmful indirect effects
• [stat.ME]A General Framework of Online Updating Variable Selection for Generalized Linear Models with Streaming Datasets
• [stat.ME]Bayesian Bandwidths in Semiparametric Modelling for Nonnegative Orthant Data with Diagnostics
• [stat.ME]Improving D-Optimality in Nonlinear Situations
• [stat.ME]Quantifying Uncertainty in Infectious Disease Mechanistic Models
• [stat.ME]Robust Differential Abundance Test in Compositional Data
• [stat.ML]Boosting in Univariate Nonparametric Maximum Likelihood Estimation
• [stat.ML]Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback
• [stat.ML]Influence Estimation for Generative Adversarial Networks
• [stat.OT]Lessons from the German Tank Problem
·····································
• [cs.AI]How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
Sérgio Jesus, Catarina Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama
http://arxiv.org/abs/2101.08758v1
• [cs.AR]Cain: Automatic Code Generation for Simultaneous Convolutional Kernels on Focal-plane Sensor-processors
Edward Stow, Riku Murai, Sajad Saeedi, Paul H. J. Kelly
http://arxiv.org/abs/2101.08715v1
• [cs.CG]Geometric Moment Invariants to Motion Blur
Hongxiang Hao, Hanlin Mo, Hua Li
http://arxiv.org/abs/2101.08647v1
• [cs.CL]Adv-OLM: Generating Textual Adversaries via OLM
Vijit Malik, Ashwani Bhat, Ashutosh Modi
http://arxiv.org/abs/2101.08523v1
• [cs.CL]Content Selection Network for Document-grounded Retrieval-based Chatbots
Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, Zhicheng Dou
http://arxiv.org/abs/2101.08426v1
• [cs.CL]Evaluating Multilingual Text Encoders for Unsupervised Cross-Lingual Retrieval
Robert Litschko, Ivan Vulić, Simone Paolo Ponzetto, Goran Glavaš
http://arxiv.org/abs/2101.08370v1
• [cs.CL]Multi-sense embeddings through a word sense disambiguation process
Terry Ruas, William Grosky, Aiko Aizawa
http://arxiv.org/abs/2101.08700v1
• [cs.CL]ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation
Qingxiu Dong, Xiaojun Wan, Yue Cao
http://arxiv.org/abs/2101.08382v1
• [cs.CL]Validating Label Consistency in NER Data Annotation
Qingkai Zeng, Mengxia Yu, Wenhao Yu, Tianwen Jiang, Tim Weninger, Meng Jiang
http://arxiv.org/abs/2101.08698v1
• [cs.CL]Zero-shot Generalization in Dialog State Tracking through Generative Question Answering
Shuyang Li, Jin Cao, Mukund Sridhar, Henghui Zhu, Shang-Wen Li, Wael Hamza, Julian McAuley
http://arxiv.org/abs/2101.08333v1
• [cs.CR]Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?
Jacson Rodrigues Correia-Silva, Rodrigo F. Berriel, Claudine Badue, Alberto F. De Souza, Thiago Oliveira-Santos
http://arxiv.org/abs/2101.08717v1
• [cs.CR]Introducing the Unitychain Structure: A novel blockchain-like structure that enables greater parallel processing, security, and performance for networks that leverage distributed key generation and classical consensus protocols
Joshua D. Tobkin
http://arxiv.org/abs/2101.08428v1
• [cs.CV]A two-stage data association approach for 3D Multi-object Tracking
Minh-Quan Dao, Vincent Frémont
http://arxiv.org/abs/2101.08684v1
• [cs.CV]Activity Graph Transformer for Temporal Action Localization
Megha Nawhal, Greg Mori
http://arxiv.org/abs/2101.08540v1
• [cs.CV]Aesthetics, Personalization and Recommendation: A survey on Deep Learning in Fashion
Wei Gong, Laila Khalid
http://arxiv.org/abs/2101.08301v1
• [cs.CV]All-Day Object Tracking for Unmanned Aerial Vehicle
Bowen Li, Changhon Fu, Fangqiang Ding, Junjie Ye, Fuling Lin
http://arxiv.org/abs/2101.08
af1
446v1
af1
446v1)
• [cs.CV]An Effective Data Augmentation for Person Re-identification
Yunpeng Gong, Zhiyong Zeng
http://arxiv.org/abs/2101.08533v1
• [cs.CV]Anti-UAV: A Large Multi-Modal Benchmark for UAV Tracking
Nan Jiang, Kuiran Wang, Xiaoke Peng, Xuehui Yu, Qiang Wang, Junliang Xing, Guorong Li, Guodong Guo, Jian Zhao, Zhenjun Han
http://arxiv.org/abs/2101.08466v1
• [cs.CV]CM-NAS: Rethinking Cross-Modality Neural Architectures for Visible-Infrared Person Re-Identification
Chaoyou Fu, Yibo Hu, Xiang Wu, Hailin Shi, Tao Mei, Ran He
http://arxiv.org/abs/2101.08467v1
• [cs.CV]COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos
Deesha Chavan, Dev Saad, Debarati B. Chakraborty
http://arxiv.org/abs/2101.08463v1
• [cs.CV]DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition
Edwin Arkel Rios, Wen-Huang Cheng, Bo-Cheng Lai
http://arxiv.org/abs/2101.08674v1
• [cs.CV]Discovering Multi-Label Actor-Action Association in a Weakly Supervised Setting
Sovan Biswas, Juergen Gall
http://arxiv.org/abs/2101.08567v1
• [cs.CV]FWB-Net:Front White Balance Network for Color Shift Correction in Single Image Dehazing via Atmospheric Light Estimation
Cong Wang, Yan Huang, Yuexian Zou, Yong Xu
http://arxiv.org/abs/2101.08465v1
• [cs.CV]Fast and Robust Certifiable Estimation of the Relative Pose Between Two Calibrated Cameras
Mercedes Garcia-Salguero, Javier Gonzalez-Jimenez
http://arxiv.org/abs/2101.08524v1
• [cs.CV]Finger Vein Recognition by Generating Code
Zhongxia Zhang, Mingwen Wang
http://arxiv.org/abs/2101.08415v1
• [cs.CV]Fire Threat Detection From Videos with Q-Rough Sets
Debarati B. Chakrabortya, Vinay Detania, Shah Parshv Jigneshkumar
http://arxiv.org/abs/2101.08459v1
• [cs.CV]Generative Zero-shot Network Quantization
Xiangyu He, Qinghao Hu, Peisong Wang, Jian Cheng
http://arxiv.org/abs/2101.08430v1
• [cs.CV]Hierarchical Graph-RNNs for Action Detection of Multiple Activities
Sovan Biswas, Yaser Souri, Juergen Gall
http://arxiv.org/abs/2101.08581v1
• [cs.CV]Image-to-Image Translation: Methods and Applications
Yingxue Pang, Jianxin Lin, Tao Qin, Zhibo Chen
http://arxiv.org/abs/2101.08629v1
• [cs.CV]Learn to Dance with AIST++: Music Conditioned 3D Dance Generation
Ruilong Li, Shan Yang, David A. Ross, Angjoo Kanazawa
http://arxiv.org/abs/2101.08779v1
• [cs.CV]MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes
Jinhai Yang, Hua Yang
http://arxiv.org/abs/2101.08609v1
• [cs.CV]MoG-QSM: Model-based Generative Adversarial Deep Learning Network for Quantitative Susceptibility Mapping
Ruimin Feng, Jiayi Zhao, He Wang, Baofeng Yang, Jie Feng, Yuting Shi, Ming Zhang, Chunlei Liu, Yuyao Zhang, Jie Zhuang, Hongjiang Wei
http://arxiv.org/abs/2101.08413v1
• [cs.CV]Nonparametric clustering for image segmentation
Giovanna Menardi
http://arxiv.org/abs/2101.08345v1
• [cs.CV]Pre-training without Natural Images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto, Eisuke Yamagata, Ryosuke Yamada, Nakamasa Inoue, Akio Nakamura, Yutaka Satoh
http://arxiv.org/abs/2101.08515v1
• [cs.CV]Progressive Co-Attention Network for Fine-grained Visual Classification
Tian Zhang, Dongliang Chang, Zhanyu Ma, Jun Guo
http://arxiv.org/abs/2101.08527v1
• [cs.CV]Regularization via deep generative models: an analysis point of view
Thomas Oberlin, Mathieu Verm
http://arxiv.org/abs/2101.08661v1
• [cs.CV]Rethinking Semantic Segmentation Evaluation for Explainability and Model Selection
Yuxiang Zhang, Sachin Mehta, Anat Caspi
http://arxiv.org/abs/2101.08418v1
• [cs.CV]Segmenting Transparent Object in the Wild with Transformer
Enze Xie, Wenjia Wang, Wenhai Wang, Peize Sun, Hang Xu, Ding Liang, Ping Luo
http://arxiv.org/abs/2101.08461v1
• [cs.CV]TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection in Chest X-Ray Images
Mustafa Hajij, Ghada Zamzmi, Fawwaz Batayneh
http://arxiv.org/abs/2101.08398v1
• [cs.CV]Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network
Berat Barakat, Ahmad Droby, Majeed Kassis, Jihad El-Sana
http://arxiv.org/abs/2101.08299v1
• [cs.CV]Video Summarization: Study of various techniques
Ravi Raj, Varad Bhatnagar, Aman Kumar Singh, Sneha Mane, Nilima Walde
http://arxiv.org/abs/2101.08434v1
• [cs.CY]“This Whole Thing Smacks of Gender”: Algorithmic Exclusion in Bioimpedance-based Body Composition Analysis
Kendra Albert, Maggie Delano
http://arxiv.org/abs/2101.08325v1
• [cs.CY]Allocating Opportunities in a Dynami
8000
c Model of Intergenerational Mobility
Hoda Heidari, Jon Kleinberg
http://arxiv.org/abs/2101.08451v1
• [cs.CY]MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes
Joseph Bae, Rohan Sukumaran, Sheshank Shankar, Saurish Srivastava, Rohan Iyer, Aryan Mahindra, Qamil Mirza, Maurizio Arseni, Anshuman Sharma, Saras Agrawal, Orna Mukhopadhyay, Colin Kang, Priyanshi Katiyar, Apurv Shekhar, Sifat Hasan, Krishnendu Dasgupta, Darshan Gandhi, Sethuramen TV, Parth Patwa, Ishaan Singh, Abhishek Singh, Ramesh Raskar
http://arxiv.org/abs/2101.07931v2
• [cs.CY]The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information
Max Aliapoulios, Antonis Papasavva, Cameron Ballard, Emiliano De Cristofaro, Gianluca Stringhini, Savvas Zannettou, Jeremy Blackburn
http://arxiv.org/abs/2101.08750v1
• [cs.DC]Clairvoyant Prefetching for Distributed Machine Learning I/O
Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
http://arxiv.org/abs/2101.08734v1
• [cs.DC]GPU-Accelerated Optimizer-Aware Evaluation of Submodular Exemplar Clustering
Philipp-Jan Honysz, Sebastian Buschjäger, Katharina Morik
http://arxiv.org/abs/2101.08763v1
• [cs.DL]What is all this new MeSH about? Exploring the semantic provenance of new descriptors in the MeSH thesaurus
Anastasios Nentidis, Anastasia Krithara, Grigorios Tsoumakas, Georgios Paliouras
http://arxiv.org/abs/2101.08293v1
• [cs.HC]Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
Prerna Juneja, Tanushree Mitra
http://arxiv.org/abs/2101.08419v1
• [cs.HC]Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization
Leonardo Christino, Martha D. Ferreira, Asal Jalilvand, Fernando V. Paulovich
http://arxiv.org/abs/2101.08655v1
• [cs.HC]Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation
Riku Arakawa, Hiromu Yakura
http://arxiv.org/abs/2101.08621v1
• [cs.IR]Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Luís Borges, Bruno Martins, Jamie Callan
http://arxiv.org/abs/2101.08705v1
• [cs.IR]Fast Clustering of Short Text Streams Using Efficient Cluster Indexing and Dynamic Similarity Thresholds
Md Rashadul Hasan Rakib, Muhammad Asaduzzaman
http://arxiv.org/abs/2101.08595v1
• [cs.IR]Item Recommendation from Implicit Feedback
Steffen Rendle
http://arxiv.org/abs/2101.08769v1
• [cs.IR]Joint Autoregressive and Graph Models for Software and Developer Social Networks
Rima Hazra, Hardik Aggarwal, Pawan Goyal, Animesh Mukherjee, Soumen Chakrabarti
http://arxiv.org/abs/2101.08729v1
• [cs.IR]Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
Luyu Gao, Zhuyun Dai, Jamie Callan
http://arxiv.org/abs/2101.08751v1
• [cs.IR]Templates of generic geographic information for answering where-questions
Ehsan Hamzei, Stephan Winter, Martin Tomko
http://arxiv.org/abs/2101.08394v1
• [cs.IT]Blocked and Hierarchical Disentangled Representation From Information Theory Perspective
Ziwen Liu, Mingqiang Li, Congying Han
http://arxiv.org/abs/2101.08408v1
• [cs.IT]Bounds on the Feedback Capacity of the #card=math&code=%28d%2C%5Cinfty%29)-RLL Input-Constrained Binary Erasure Channel
V. Arvind Rameshwar, Navin Kashyap
http://arxiv.org/abs/2101.08638v1
• [cs.IT]Dictionary-Sparse Recovery From Heavy-Tailed Measurements
Pedro Abdalla, Christian Kümmerle
http://arxiv.org/abs/2101.08298v1
• [cs.IT]Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems
Chong Han, Longfei Yan, Jinhong Yuan
http://arxiv.org/abs/2101.08469v1
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Pooja Nayak Muralidhar, Digvijay Katyal, B. Sundar Rajan
http://arxiv.org/abs/2101.08723v1
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K. K. Krishnan Namboodiri, B. Sundar Rajan
http://arxiv.org/abs/2101.08745v1
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Tianming Feng, Shuo Shi, Shushi Gu, Wei Xiang, Xuemai Gu
http://arxiv.org/abs/2101.08669v1
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Zhifang Zhang, Liyang Zhou
http://arxiv.org/abs/2101.08738v1
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Naifu Zhang, Meixia Tao, Jia Wang
http://arxiv.org/abs/2101.08696v1
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Xunmeng Wu, Zai Yang, Zongben Xu
http://arxiv.org/abs/2101.08547v1
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Xiaoqiang Wang, Dabin Zheng, Cunsheng Ding
http://arxiv.org/abs/2101.08425v1
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Jun Muramatsu
http://arxiv.org/abs/2101.08433v1
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Antonino Favano, Marco Ferrari, Maurizio Magarini, Luca Barletta
http://arxiv.org/abs/2101.08643v1
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Wenjie Chen, Shengcai Liu, Ke Tang
http://arxiv.org/abs/2101.08743v1
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Quynh Nguyen
http://arxiv.org/abs/2101.08576v1
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Sharu Theresa Jose, Osvaldo Simeone
http://arxiv.org/abs/2101.08390v1
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Dimitrije Markovic, Hrvoje Stojic, Sarah Schwoebel, Stefan J. Kiebel
http://arxiv.org/abs/2101.08699v1
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Suemin Lee, Ivan V. Bajić
http://arxiv.org/abs/2101.08427v1
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Mario Boley, Simon Teshuva, Pierre Le Bodic, Geoffrey I Webb
http://arxiv.org/abs/2101.08380v1
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Sergei Ivanov, Liudmila Prokhorenkova
http://arxiv.org/abs/2101.08543v1
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Vegard Antun, Matthew J. Colbrook, Anders C. Hansen
http://arxiv.org/abs/2101.08286v1
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Andrew Brock, Soham De, Samuel L. Smith
http://arxiv.org/abs/2101.08692v1
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Liyuan Sun, Jianping Gou, Lan Du, Dacheng Tao
http://arxiv.org/abs/2101.08471v1
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Abolfazl Nadi, Hadi Moradi, Khalil Taheri
http://arxiv.org/abs/2101.08585v1
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Pratyush Kumar, Aishwarya Das, Debayan Gupta
http://arxiv.org/abs/2101.08486v1
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Yongquan Yang, Haijun Lv
http://arxiv.org/abs/2101.08387v1
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Walker Ravina, Ethan Sterling, Olexiy Oryeshko, Nathan Bell, Honglei Zhuang, Xuanhui Wang, Yonghui Wu, Alexander Grushetsky
http://arxiv.org/abs/2101.08393v1
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Jinxiong Zhang
http://arxiv.org/abs/2101.08656v1
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Varun Mandalapu, Jiaqi Gong, Lujie Chen
http://arxiv.org/abs/2101.08349v1
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Gang Qu, Li Xiao, Wenxing Hu, Kun Zhang, Vince D. Calhoun, Yu-Ping Wang
http://arxiv.org/abs/2101.08316v1
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Tobias Hatt, Stefan Feuerriegel
http://arxiv.org/abs/2101.08490v1
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Zhaowei Cai, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Zhuowen Tu, Stefano Soatto
http://arxiv.org/abs/2101.08482v1
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Amit Daniely, Gal Vardi
http://arxiv.org/abs/2101.08303v1
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S. Brugiapaglia, M. Liu, P. Tupper
http://arxiv.org/abs/2101.08386v1
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Thomas Pfeil
http://arxiv.org/abs/2101.08685v1
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Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, Philip S. Yu
http://arxiv.org/abs/2101.08747v1
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Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
http://arxiv.org/abs/2101.08358v1
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Ke He, Le He, Lisheng Fan, Yansha Deng, George K. Karagiannidis, Arumugam Nallanathan
http://arxiv.org/abs/2101.08435v1
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G. Welper
http://arxiv.org/abs/2101.08310v1
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Sikai Zhang, Zi-Qiang Lang
http://arxiv.org/abs/2101.08539v1
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Haotian Ye, Chuanlong Xie, Yue Liu, Zhenguo Li
http://arxiv.org/abs/2101.08521v1
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Ties van Rozendaal, Iris A. M. Huijben, Taco S. Cohen
http://arxiv.org/abs/2101.08687v1
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Jie Bu, Anuj Karpatne
http://arxiv.org/abs/2101.08366v1
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Huan Zhang, Hongge Chen, Duane Boning, Cho-Jui Hsieh
http://arxiv.org/abs/2101.08452v1
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Lang Huang, Chao Zhang, Hongyang Zhang
http://arxiv.org/abs/2101.08732v1
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Ivan Gridin
http://arxiv.org/abs/2101.08742v1
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Aroof Aimen, Sahil Sidheekh, Vineet Madan, Narayanan C. Krishnan
http://arxiv.org/abs/2101.08587v1
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Thesath Nanayakkara, Gilles Clermont, Christopher James Langmead, David Swigon
http://arxiv.org/abs/2101.08477v1
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Emanuel Kieroński, Sebastian Rudolph
http://arxiv.org/abs/2101.08377v1
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Yi Chen, Aimin Zhou
http://arxiv.org/abs/2101.08552v1
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Yi Shi, Yalin E. Sagduyu
http://arxiv.org/abs/2101.08724v1
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Qiong Wu, Xu Chen, Zhi Zhou, Liang Chen, Junshan Zhang
http://arxiv.org/abs/2101.08391v1
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Jian Weng, Animesh Jain, Jie Wang, Leyuan Wang, Yida Wang, Tony Nowatzki
http://arxiv.org/abs/2101.08458v1
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Martina Zambelli, Yusuf Aytar, Francesco Visin, Yuxiang Zhou, Raia Hadsell
http://arxiv.org/abs/2101.08616v1
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Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski, Diego Romeres
http://arxiv.org/abs/2101.08740v1
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Hassan Fouad, Giovanni Beltrame
http://arxiv.org/abs/2101.08697v1
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Priyanka Bhovad, Suyi Li
http://arxiv.org/abs/2101.08348v1
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Neil Zeghidour, Olivier Teboul, Félix de Chaumont Quitry, Marco Tagliasacchi
http://arxiv.org/abs/2101.08596v1
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Gökhan Altan, Yakup Kutlu, Adnan Özhan Pekmezci, Serkan Nural
http://arxiv.org/abs/2101.08288v1
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Francesca Del Bonifro, Maurizio Gabbrielli, Stefano Zacchiroli
http://arxiv.org/abs/2101.08508v1
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Giovanna Menardi, Domenico De Stefano
http://arxiv.org/abs/2101.08334v1
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Christian Toth, Denis Helic, Bernhard C. Geiger
http://arxiv.org/abs/2101.08623v1
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Amir Hussein, Shinji Watanabe, Ahmed Ali
http://arxiv.org/abs/2101.08454v1
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Balázs Maga
http://arxiv.org/abs/2101.08309v1
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Chao Li, Wenjian Huang, Xi Chen, Yiran Wei, Stephen J. Price, Carola-Bibiane Schönlieb
http://arxiv.org/abs/2101.08757v1
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Ying Nie, Kai Han, Zhenhua Liu, An Xiao, Yiping Deng, Chunjing Xu, Yunhe Wang
http://arxiv.org/abs/2101.08525v1
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Lin Zhang, Tiziano Portenier, Orcun Goksel
http://arxiv.org/abs/2101.08339v1
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Maedeh Jamali, Nader Karimi, Shadrokh Samavi
http://arxiv.org/abs/2101.08502v1
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Zhanlin Liu, Youngjun Choe
http://arxiv.org/abs/2101.07997v2
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Jingxin Zhang, Donghua Zhou, Maoyin Chen
http://arxiv.org/abs/2101.08579v1
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Soumyadip Ghosh, Yingdong Lu, Tomasz Nowicki
http://arxiv.org/abs/2101.08688v1
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Fred Espen Benth, Giulia Di Nunno, Dennis Schroers
http://arxiv.org/abs/2101.08598v1
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Andreas H Hamel, Daniel Kostner
http://arxiv.org/abs/2101.08628v1
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Riccardo Gatto
http://arxiv.org/abs/2101.08529v1
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Joseph Ngatchou-Wandji, Marwa Ltaifa
http://arxiv.org/abs/2101.08597v1
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Pinhan Chen, Chao Gao, Anderson Y. Zhang
http://arxiv.org/abs/2101.08421v1
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Amorino Chiara, Nualart Eulalia
http://arxiv.org/abs/2101.08548v1
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Ayana Ghosh, Bobby G. Sumpter, Ondrej Dyck, Sergei V. Kalinin, Maxim Ziatdinov
http://arxiv.org/abs/2101.08449v1
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Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu
http://arxiv.org/abs/2101.08578v1
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Ethan Jacob Moyer, Anup Das
http://arxiv.org/abs/2101.08385v1
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Xinwei Yu, Matthew S. Creamer, Francesco Randi, Anuj K. Sharma, Scott W. Linderman, Andrew M. Leifer
http://arxiv.org/abs/2101.08211v1
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Xun Gao, Eric R. Anschuetz, Sheng-Tao Wang, J. Ignacio Cirac, Mikhail D. Lukin
http://arxiv.org/abs/2101.08354v1
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Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, Alán Aspuru-Guzik
http://arxiv.org/abs/2101.08448v1
• [stat
4e71
.ME]A unified method for multivariate mixed-type response regression
Karl Oskar Ekvall, Aaron J. Molstad
http://arxiv.org/abs/2101.08436v1
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Tobias Braun, Matthias Waechter, Joachim Peinke, Thomas Guhr
http://arxiv.org/abs/2101.08573v1
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Robert Matthijs Verschuren
http://arxiv.org/abs/2101.08551v1
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Kara E. Rudolph, Ivan Diaz
http://arxiv.org/abs/2101.08590v1
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Xiaoyu Ma, Lu Lin, Yujie Gai
http://arxiv.org/abs/2101.08639v1
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Célestin C. Kokonendji, Sobom M. Somé
http://arxiv.org/abs/2101.08365v1
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Hana Sulieman
http://arxiv.org/abs/2101.08608v1
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Lucy D’Agostino McGowan, Kyra H. Grantz, Eleanor Murray
http://arxiv.org/abs/2101.07329v2
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Shulei Wang
http://arxiv.org/abs/2101.08765v1
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YunPeng Li, ZhaoHui Ye
http://arxiv.org/abs/2101.08505v1
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Marc Jourdan, Mojmír Mutný, Johannes Kirschner, Andreas Krause
http://arxiv.org/abs/2101.08534v1
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Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, Takashi Kanemaru
http://arxiv.org/abs/2101.08367v1
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George Clark, Alex Gonye, Steven J Miller
http://arxiv.org/abs/2101.08162v2
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