Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
IEEE

Efficient Uncertainty Quantification for 3-D MT Inversion via Variational Inference

Abstract: We present an efficient method for uncertainty quantification in 3-D magnetotelluric (MT) inversions based on variational inference principles and the variational autoencoder (VAE) framework ...
IEEE

Bayesian Deep Learning for Remaining Useful Life Estimation via Stein Variational Gradient Descent

Abstract: A crucial task in predictive maintenance is estimating the remaining useful life of physical systems. In the last decade, deep learning has improved considerably upon traditional model-based ...