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.

本文旨在研究如何衡量深度神经网络中不同的不确定性因素，进一步探究利用这些因素更有效地解决不同决策问题的方法。其中，作者提出了一个一般学习框架来定量不同根本原因导致的多种不确定性，并发现在对于深度神经网络的分类及早期时间点的事件检测中，对于实现最佳效果的不确定性因素分别为矛盾和真空。此外，本文还提出了一个改进 SSL 算法的框架，以抵御 OOD 对象的重要影响。