Evidential deep learning, built upon belief theory and subjective logic,
offers a principled and computationally efficient way to turn a deterministic
neural network uncertainty-aware. The resultant evidential models can quantify
fine-grained uncertainty using the learned evidence. To ensure theoretically
sound evidential models, the evidence needs to be non-negative, which requires
special activation functions for model training and inference. This constraint
often leads to inferior predictive performance compared to standard softmax
models, making it challenging to extend them to many large-scale datasets. To
unveil the real cause of this undesired behavior, we theoretically investigate
evidential models and identify a fundamental limitation that explains the
inferior performance: existing evidential activation functions create zero
evidence regions, which prevent the model to learn from training samples
falling into such regions. A deeper analysis of evidential activation functions
based on our theoretical underpinning inspires the design of a novel
regularizer that effectively alleviates this fundamental limitation. Extensive
experiments over many challenging real-world datasets and settings confirm our
theoretical findings and demonstrate the effectiveness of our proposed
approach.

该研究通过提出一种新颖的规则化方法，解决了 evidential models 训练和推理中产生的根本性困难，从而提高了预测性能。

学习累积所有训练样本的证据：理论与实践

Learn to Accumulate Evidence from All Training Samples: Theory and  Practice

An in-depth understanding of uncertainty is the first step to making
effective decisions under uncertainty. Deep/machine learning (ML/DL) has been
hugely leveraged to solve complex problems involved with processing
high-dimensional data. However, reasoning and quantifying different types of
uncertainties to achieve effective decision-making have been much less explored
in ML/DL than in other Artificial Intelligence (AI) domains. In particular,
belief/evidence theories have been studied in KRR since the 1960s to reason and
measure uncertainties to enhance decision-making effectiveness. We found that
only a few studies have leveraged the mature uncertainty research in
belief/evidence theories in ML/DL to tackle complex problems under different
types of uncertainty. In this survey paper, we discuss several popular belief
theories and their core ideas dealing with uncertainty causes and types and
quantifying them, along with the discussions of their applicability in ML/DL.
In addition, we discuss three main approaches that leverage belief theories in
Deep Neural Networks (DNNs), including Evidential DNNs, Fuzzy DNNs, and Rough
DNNs, in terms of their uncertainty causes, types, and quantification methods
along with their applicability in diverse problem domains. Based on our
in-depth survey, we discuss insights, lessons learned, limitations of the
current state-of-the-art bridging belief theories and ML/DL, and finally,
future research directions.

本论文调查了信念理论在深度学习中的应用及其局限性，并提出了未来的研究方向。