AutoBNN — compositional Bayesian neural networks for probabilistic time-series forecasting (open-source, Google Research)
Google Research (post by Urs Köster) presents AutoBNN, an open-source JAX package available within TensorFlow Probability that replaces Gaussian processes with compositional Bayesian neural networks to automate discovery of interpretable time-series forecasting models, produce uncertainty estimates, and scale more efficiently to large datasets. AutoBNN maps compositional GP kernels to BNN architectures and supports operators analogous to GP addition and multiplication while enabling GPU/TPU acceleration and possible hybrid architectures with deep BNN components.