From deep learning to Bayesian neural network: 0 - Introduction

While deep learning models have seen significant success across various domains, their black-box learning nature and lack of interpretability affect their reliability in safety-critical applications. In response to these limitations, the Bayesian paradigm offers a promising alternative by incorporating uncertainty estimation into model predictions, enhancing transparency and decision-making.

Here, we present a series of tutorials to introduce fundamental concepts and recent advancements in deep learning and Bayesian neural networks.

• From deep learning to Bayesian neural network: 0 - Introduction

• From deep learning to Bayesian neural network: 1 - Deep learning and its theoretical framework

Reference

If you feel this tutorial is useful, I would appreciate it if you would cite the following paper:

@article{chen5009452interplay,
  title={Interplay between Bayesian Neural Networks and Deep Learning: A Survey},
  author={Chen, Yinsong and Yu, Samson S and Li, Zhong and Eshraghian, Jason K and Lim, Chee Peng},
  journal={Available at SSRN: https://ssrn.com/abstract=5100162 or http://dx.doi.org/10.2139/ssrn.5100162}
}