LBBNN: An R Package for Sparse and Explainable Bayesian Deep Learning with Latent Binary Bayesian Neural Networks

The package LBBNN (latent binary Bayesian neural networks) provides a framework for doing sparse uncertainty aware and to a large degree interpretable Bayesian inference in neural network models, with potentially huge datasets, and highly overparametrized networks. Currently, most packages implementing Bayesian neural networks in R use Markov chain Monte Carlo based inference, without the possibility of GPU acceleration. Our package, using variational inference and the LibTorch backend (via the torch package), provides a more scalable and computationally efficient approach. The package integrates three different methodological works: Firstly, it takes into account the basic LBBNN model, where each weight in the network is associated with a Bernoulli latent inclusion indicator, allowing for incorporating model uncertainty and achieving substantial sparsification of the network while maintaining high predictive power. Secondly, using normalizing flows allows for modeling more complex and flexible variational distributions with statistical dependencies. And thirdly, by allowing the input variables to skip to any layer in the network, we can model different types of functions, e.g. we might only have linear connections. This allows for more explainable results. Lastly, local explanations at the prediction level with uncertainty are provided in the LBBNN package and are guaranteed to be exact for piecewise linear activations. To demonstrate how the package can be used in practice, we include experiments on both synthetic data and real-world datasets, covering both tabular and image based data.