Key updates:
* Classification Support for Full and Partial BNNs: While the initial focus was on regression with (P)BNNs - since most tasks in physical sciences deal with (quasi-)continuous variables - it was brought to my attention that some research domains can benefit from classification capabilities. So, the new update introduces classification support. To help you get started, I've provided two toy data examples, which can easily be generalized to real-world problems.
* Expanded SWA Options in JAX/Flax: This update enhances the Stochastic Weight Averaging options, providing more robust priors for both Full and Partial BNNs.
* Automatic Restart for HMC/NUTS: Now, HMC/NUTS for (P)BNNs can automatically restart in case of bad initializations, which helps during the autonomous exploration of parameter spaces in experiments and simulations.
* Additional Metrics for Active Learning and UQ: New metrics have been added to enhance the active learning and uncertainty quantification evaluation processes.
* Minor bug fixes, improved documentation, and more examples!
Looking ahead, the next major step will be expanding Partial BNNs beyond the current MLP and ConvNet architectures to include RNNs, GNNs, and Transformers.