Dynamic trust evaluation in large, rapidly evolving graphs demands models that capture changing relationships, express calibrated confidence, and resist adversarial manipulation. DGTEN (Deep Gaussian-Based Trust Evaluation Network) introduces a unified graph-based framework that does all three by combining uncertainty-aware message passing, expressive temporal modeling, and built-in defenses against trust-targeted attacks. It represents nodes and edges as Gaussian distributions so that both semantic signals and epistemic uncertainty propagate through the graph neural network, enabling risk-aware trust decisions rather than overconfident guesses. To track how trust evolves, it layers hybrid absolute-Gaussian-hourglass positional encoding with Kolmogorov-Arnold network-based unbiased multi-head attention, then applies an ordinary differential equation-based residual learning module to jointly model abrupt shifts and smooth trends. Robust adaptive ensemble coefficient analysis prunes or down-weights suspicious interactions using complementary cosine and Jaccard similarity, curbing reputation laundering, sabotage, and on-off attacks. On two signed Bitcoin trust networks, DGTEN delivers standout gains where it matters most: in single-timeslot prediction on Bitcoin-OTC, it improves MCC by +12.34% over the best dynamic baseline; in the cold-start scenario on Bitcoin-Alpha, it achieves a +25.00% MCC improvement, the largest across all tasks and datasets; while under adversarial on-off attacks, it surpasses the baseline by up to +10.23% MCC. These results endorse the unified DGTEN framework.
Beyond Uncertainty Quantification: Learning Uncertainty for Trust-Informed Neural Network Decisions - A Case Study in COVID-19 Classification
Faculty of Engineering & IT, University of Technology Sydney（技术悉尼大学工程与信息学院）；University Research and Innovation Center (EKIK), Óbuda University（奥布达大学研究与创新中心）
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm（法国国家健康与医学研究院）；Pixyl Research and Development Laboratory（Pixyl研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
School of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；University of Essex（埃塞克斯大学）；Institute of Mathematics（数学研究所）；Slovak Academy of Sciences（斯洛伐克科学院）
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Florida（佛罗里达大学）
Metropolis-Scale Resilient and Trustworthy Traffic Flow Inference Using Multi-Source Data
School of Transportation, Jilin University（吉林大学交通运输学院）；Department of Computer Science, City University of Hong Kong (Dongguan)（香港城市大学（东莞）计算机科学系）；Institute for Transport Planning and Systems, ETH Zurich（苏黎世联邦理工学院交通规划与系统研究所）；Institute of Intelligent Transportation Systems, College of Civil Engineering and Architecture, Zhejiang University（浙江大学智能交通系统研究所）；ZJU-UIUC Institute, Zhejiang University（浙江大学-UIUC研究院）
Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
Stockholm University Department of Physics（斯德哥尔摩大学物理系）；Department of Mathematical Sciences（数学科学系）；Chalmers university of technology & University of Gothenburg（楚德勒斯技术大学及哥德堡大学）；VERSES Research Lab（VERSES研究实验室）
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
University of Liverpool（利物浦大学）；University of Edinburgh（爱丁堡大学）；The University of Texas at Dallas（德州大学达拉斯分校）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Simon Fraser University（西蒙菲莎大学）；Kyoto University（京都大学）；Zhejiang University（浙江大学）；National Taiwan Normal University（国立台湾师范大学）；Indiana University（印第安纳大学）；Purdue University（普渡大学）；AppCubic（AppCubic公司）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The Hong Kong University of Science and Technology（香港科技大学）
Logit-Level Uncertainty Quantification in Vision-Language Models for Histopathology Image Analysis
Department of Electrical and Electronics Engineering, Izmir Bakircay University（电气与电子工程系，伊兹密尔巴克伊大学）；Department of Electrical Engineering and Computer Science, University of Stavanger（电气工程与计算机科学系，斯塔万格大学）；Batten College of Engineering and Technology, Old Dominion University（工程与技术学院，老奥布良大学）
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification
Institute of Theoretical Informatics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院理论信息学研究所）；Institute of Nanotechnology, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院纳米技术研究所）
A Critical Synthesis of Uncertainty Quantification and Foundation Models in Monocular Depth Estimation
Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology (KIT)（摄影测量与遥感研究所（卡尔斯鲁厄理工学院））；The Ohio State University（俄亥俄州立大学）
Knowing when to trust machine-learned interatomic potentials
Department of Chemistry, Mellon College of Science（化学系，梅隆科学学院）；Department of Materials Science and Engineering（材料科学与工程系）；Ray and Stephanie Lane Computational Biology Department, School of Computer Science（Ray和Stephanie Lane计算生物学部门，计算机科学学院）
On the Need to Align Intent and Implementation in Uncertainty Quantification for Machine Learning
Fermi National Accelerator Laboratory（费米国家加速器实验室）；Department of Astronomy and Astrophysics, University of Chicago（芝加哥大学天文学与天体物理学系）；Kavli Institute for Cosmological Physics, University of Chicago（芝加哥大学凯弗利宇宙物理研究所）
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Department of Mechanical Engineering, Iowa State University, Ames, IA, USA（机械工程系，爱荷华州立大学）；Pacific Northwest National Laboratory, Richland, WA, USA（太平洋西北国家实验室）；Department of Statistics, North Carolina State University, Raleigh, NC, USA（统计系，北卡罗来纳州立大学）
