Bayesian deep learning: A model-based interpretable approach

Abstract

Deep learning is considered to be a model-free, end-to-end, and black-box approach. It requires numerous data samples instead of expert knowledge on the target domain. Hence, it does not specify the mechanism and reasons for its decision making. This aspect is considered a critical limitation of deep learning. This paper introduces another viewpoint, namely Bayesian deep learning. Deep learning can be installed in any framework, such as Bayesian networks and reinforcement learning. Subsequently, an expert can implement the knowledge as the graph structure, accelerate learning, and obtain new knowledge on the target domain. The framework is termed as the deep generative model. Conversely, we can directly introduce the Bayesian modeling approach to deep learning. Subsequently, it is possible to explore deep learning with respect to the confidence of its decision making via uncertainty quantification of the output and detect wrong decision-making or anomalous inputs. Given the aforementioned approaches, it is possible to adjust the "brightness" of deep learning.