Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular approaches to estimate predictive uncertainty in deep learning are methods that combine predictions from multiple neural networks, such as Bayesian neural networks (BNNs) and deep ensembles. However their practicality in real-time, industrial-scale applications are limited due to the high memory and computational cost. Furthermore, ensembles and BNNs do not necessarily fix all the issues with the underlying member networks. In this work, we study principled approaches to improve uncertainty property of a single network, based on a single, deterministic representation. By formalizing the uncertainty quantification as a minimax learning problem, we first identify distance awareness, i.e., the model's ability to quantify the distance of a testing example from the training data, as a necessary condition for a DNN to achieve high-quality (i.e., minimax optimal) uncertainty estimation. We then propose Spectral-normalized Neural Gaussian Process (SNGP), a simple method that improves the distance-awareness ability of modern DNNs with two simple changes: (1) applying spectral normalization to hidden weights to enforce bi-Lipschitz smoothness in representations and (2) replacing the last output layer with a Gaussian process layer. On a suite of vision and language understanding benchmarks, SNGP outperforms other single-model approaches in prediction, calibration and out-of-domain detection. Furthermore, SNGP provides complementary benefits to popular techniques such as deep ensembles and data augmentation, making it a simple and scalable building block for probabilistic deep learning. Code is open-sourced at https://github.com/google/uncertainty-baselines
Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations
Graz University of Technology（格拉茨技术大学）；Stanford University（斯坦福大学）；Computer Algorithms for Médicine (Café) Laboratory（医学计算机算法（Café）实验室）；University Medicine Essen（埃森大学医学院）
A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
School of Automation and Information Engineering, Xi’an University of Technology（西安理工大学自动化与信息工程学院）；Concordia Institute of Aerospace Design and Innovation（康考迪亚航空航天设计与创新研究所）
Second-Order Uncertainty Quantification: A Distance-Based Approach
Institute of Informatics, LMU Munich（慕尼黑大学信息学研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心（MCML））；PRECISE Center, University of Pennsylvania（宾夕法尼亚大学PRECISE中心）
Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Georgia Tech Europe（佐治亚理工学院欧洲校区）；Czech Institute of Informatics, Robotics and Cybernetics（捷克信息学、机器人学与控制论研究所）；Czech Technical University in Prague（布拉格捷克理工大学）
Uncertainty Awareness on Unsupervised Domain Adaptation for Time Series Data
College of Computing and Data Science, Nanyang Technological University (NTU), Singapore（南洋理工大学计算与数据科学学院）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算机与人工智能学院）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（信息通信研究所（I2R），A*STAR，新加坡）；School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息科学技术大学人工智能学院）
Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness
University of Science and Technology of China（中国科学技术大学）；Baidu Talent Intelligence Center（百度人才智库中心）；Rutgers, The State University of New Jersey（罗格斯大学新泽西州立大学）
Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models
Ludwig Maximilians University Munich（慕尼黑路德维希-马克西米利安大学）；Siemens AG（西门子股份公司）；University Hospital Erlangen（埃尔朗根大学医院）
基于递归神经网络的解决方案越来越多地用于纵向电子健康记录数据的分析。然而，大多数工作都集中在预测准确性上，而忽视了预测的不确定性。我们提出了用于时间到事件预测任务的深度内核加速故障时间模型，通过递归神经网络和稀疏高斯过程的管道实现预测的不确定性意识。此外，基于深度度量学习的预训练步骤适于增强所提出的模型。在两个真实数据集上的实验中，我们的模型显示出比基于递归神经网络的基线更好的点估计性能。更重要的是，我们模型的预测方差可用于量化事件发生时间预测的不确定性估计：当我们的模型对其预测更有信心时，它会提供更好的性能。与相关方法（如Monte Carlo Dropout）相比，我们的模型通过利用分析解决方案提供了更好的不确定性估计，并且计算效率更高。
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
Deakin University（迪肯大学）；The University of Melbourne（墨尔本大学）；University of Cambridge（剑桥大学）；Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）
Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty
Department of Electrical and Computer Engineering of University of Maine（缅因大学电气与计算机工程系）；Electrical and Microelectronic Engineering Department of Rochester Institute of Technology（罗切斯特理工学院电气与微电子工程系）
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
German Aerospace Center (DLR)（德国航空航天中心）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；Technical University of Munich (TUM)（慕尼黑工业大学）；ETH Zürich (ETHZ)（苏黎世联邦理工学院）
Creating awareness about security and safety on highways to mitigate wildlife-vehicle collisions by detecting and recognizing wildlife fences using deep learning and drone technology
Rhodes University（罗德斯大学）；University of Ngaoundéré（恩冈代雷大学）
