Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Grainger College of Engineering（格兰杰工程学院）；Center for Artificial Intelligence Innovation（人工智能创新中心）；National Center for Supercomputing Applications（国家超级计算应用中心）；Siebel School of Computing and Data Science（西贝尔计算与数据科学学院）
Ayush Khot, Xiwei Wang, Avik Roy, Volodymyr Kindratenko, Mark S. Neubauer
备注：
38 pages (including references) with 17 figures and 3 tables. Repository: https://github.com/FAIR4HEP/PFIN4UQAD . Submitted to Machine Learning: Science and Technology
Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on evidential deep learning (EDL) for deep neural network models designed to identify jets in high energy proton-proton collisions at the Large Hadron Collider and explore its utility in anomaly detection. EDL is a DL approach that treats learning as an evidence acquisition process designed to provide confidence (or epistemic uncertainty) about test data. Using publicly available datasets for jet classification benchmarking, we explore hyperparameter optimizations for EDL applied to the challenge of UQ for jet identification. We also investigate how the uncertainty is distributed for each jet class, how this method can be implemented for the detection of anomalies, how the uncertainty compares with Bayesian ensemble methods, and how the uncertainty maps onto latent spaces for the models. Our studies uncover some pitfalls of EDL applied to anomaly detection and a more effective way to quantify uncertainty from EDL as compared with the foundational EDL setup. These studies illustrate a methodological approach to interpreting EDL in jet classification models, providing new insights on how EDL quantifies uncertainty and detects out-of-distribution data which may lead to improved EDL methods for DL models applied to classification tasks.
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
Department of Computer Science, University of York, UK（英国约克大学计算机科学系）；Department of Elect. Eng., and Computer Science and Eng., Cyprus University of Technology, Cyprus（塞浦路斯技术大学电子工程与计算机科学系）
Evidential time-to-event prediction with calibrated uncertainty quantification
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学苏瑞福公共卫生学院）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）；Université de Technologie de Compiègne, CNRS, Heudiasyc（贡比涅技术大学、法国国家科研中心、Heudiasyc实验室）；Institut universitaire de France（法国大学研究院）
Uncertainty Quantification of Wind Gust Predictions in the Northeast United States: An Evidential Neural Network and Explainable Artificial Intelligence Approach
University of Connecticut（康涅狄格大学）；NSF National Center for Atmospheric Research（NSF国家大气研究中心）
Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification
State Key Laboratory of Advanced Rail Autonomous Operation（先进轨道交通自主运行国家重点实验室）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence（北京交通数据挖掘与具身智能重点实验室）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）；School of Automation and Intelligence, Beijing Jiaotong University（北京交通大学自动化与智能学院）；Beijing Key Laboratory of Security and Privacy in Intelligent Transportation（北京智能交通安全与隐私重点实验室）
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
National Center for Atmospheric Research（国家大气研究中心）；Cooperative Institute for Severe and High-Impact Weather Research and Operations（恶劣与高影响天气研究与合作研究所）；National Severe Storms Laboratory（国家强风暴实验室）；University Corporation for Atmospheric Research（大学大气研究公司）；University of Maryland（马里兰大学）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Colorado State University（科罗拉多州立大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
Equivariant Evidential Deep Learning for Interatomic Potentials
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
CalPro: Prior-Aware Evidential--Conformal Prediction with Structure-Aware Guarantees for Protein Structures
Department of Computer Science, Iowa State University, Ames, Iowa, USA；Department of Civil, Construction \& Environmental Engineering, Iowa State University, Ames, Iowa, USA
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
Gryphon Center for Artificial Intelligence and Theoretical Sciences, Singapore University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology（格雷芬人工智能与理论科学中心，新加坡大学匹兹堡大学，佩尔曼医学学院，放射肿瘤学系）；Georgia Institute of Technology（佐治亚理工学院）；University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology（宾夕法尼亚大学，佩尔曼医学学院，放射肿瘤学系）
Credal and Interval Deep Evidential Classifications
Department of Computer Science, The University of Manchester（曼彻斯特大学计算机科学系）；School of Engineering, Computing and Mathematics, Oxford Brookes University（奥克斯布里奇大学工程、计算与数学学院）
Risk-aware Classification via Uncertainty Quantification
Amazon Alexa AI（亚马逊Alexa人工智能）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；University College London（伦敦大学学院）；Fordham University（福特汉姆大学）；University of Brescia（布雷西亚大学）；Cardiff University（卡迪夫大学）
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy
Massachusetts Institute of Technology（麻省理工学院）；DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；Boston Dynamics AI Institute（波士顿动力人工智能研究所）；Northeastern University（东北大学）
Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning
University of the District of Columbia（哥伦比亚特区大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Virginia Tech（弗吉尼亚理工大学）；U.S. Army DEVCOM Army Research Laboratory（美国陆军DEVCOM陆军研究实验室）；University of Oslo（奥斯陆大学）；Bowie State University（鲍伊州立大学）
DICOM De-Identification via Hybrid AI and Rule-Based Framework for Scalable, Uncertainty-Aware Redaction
Rowan University（罗文大学）；Moffitt Cancer Center（莫菲特癌症中心）；University of South Florida（佛罗里达州立大学）；Impact Business Information Solutions, Inc（Impact商务信息解决方案公司）
