Bayesian Deep Learning

Bayesian deep learning aims to combine the power of deep neural networks with the principled uncertainty quantification offered by Bayesian methods. Current research focuses on improving the scalability and accuracy of Bayesian inference in deep networks, exploring various architectures like deep Gaussian processes and employing algorithms such as variational inference, Laplace approximations, and Hamiltonian Monte Carlo. This field is significant because it addresses the limitations of traditional deep learning by providing well-calibrated uncertainty estimates, crucial for safety-critical applications and improving the reliability of predictions in diverse fields like healthcare, finance, and scientific modeling. The development of efficient and accurate Bayesian deep learning methods is driving progress in trustworthy AI.