Deep neural networks (DNNs) have received tremendous attention and achieved great success in various applications, such as image and video analysis, natural language processing, recommendation systems, and drug discovery. However, inherent uncertainties derived from different root causes have been realized as serious hurdles for DNNs to find robust and trustworthy solutions for real-world problems. A lack of consideration of such uncertainties may lead to unnecessary risk. For example, a self-driving autonomous car can misdetect a human on the road. A deep learning-based medical assistant may misdiagnose cancer as a benign tumor. In this work, we study how to measure different uncertainty causes for DNNs and use them to solve diverse decision-making problems more effectively. In the first part of this thesis, we develop a general learning framework to quantify multiple types of uncertainties caused by different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) and dissonance (i.e., uncertainty due to conflicting evidence), for graph neural networks. We provide a theoretical analysis of the relationships between different uncertainty types. We further demonstrate that dissonance is most effective for misclassification detection and vacuity is most effective for Out-of-Distribution (OOD) detection. In the second part of the thesis, we study the significant impact of OOD objects on semi-supervised learning (SSL) for DNNs and develop a novel framework to improve the robustness of existing SSL algorithms against OODs. In the last part of the thesis, we create a general learning framework to quantity multiple uncertainty types for multi-label temporal neural networks. We further develop novel uncertainty fusion operators to quantify the fused uncertainty of a subsequence for early event detection.

翻译：深度神经网络（DNN）在图像与视频分析、自然语言处理、推荐系统及药物发现等众多应用中备受关注并取得了巨大成功。然而，不同根本原因所导致的内在不确定性已被视为DNN在现实问题中寻求稳健且可信解的重大障碍。若忽视此类不确定性，可能引发不必要的风险。例如，一辆自动驾驶汽车可能误判道路上的行人；一个基于深度学习的医疗辅助系统可能将癌症误诊为良性肿瘤。本文旨在研究如何度量DNN中不同成因的不确定性，并将其用于更有效地解决多样化决策问题。在第一部分中，我们为图神经网络开发了一个通用学习框架，用以量化由不同根本原因（如空虚性，即因缺乏证据产生的不确定性；以及失调性，即因证据冲突产生的不确定性）导致的多种不确定性类型。我们提供了不同不确定性类型之间关系的理论分析，并进一步证明失调性对误分类检测最有效，而空虚性对分布外（OOD）检测最有效。在第二部分中，我们研究了OOD对象对DNN半监督学习（SSL）的显著影响，并开发了一个新框架以提升现有SSL算法针对OOD的鲁棒性。在最后一部分中，我们创建了一个通用学习框架，用以量化多标签时序神经网络的多种不确定性类型，并进一步开发了新颖的不确定性融合算子，用于量化子序列的融合不确定性，从而实现早期事件检测。