Bayesian neural networks (BNNs) demonstrate promising success in improving the
robustness and uncertainty quantification of modern deep learning. However, they generally …

Compression and computational efficiency in deep learning have become a problem of
great significance. In this work, we argue that the most principled and effective way to attack …

We propose a general and scalable approximate sampling strategy for probabilistic models
with discrete variables. Our approach uses gradients of the likelihood function with respect …

The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

Abstract Bayesian Neural Networks (BNNs) have recently received increasing attention for
their ability to provide well-calibrated posterior uncertainties. However, model selection …

The promise of augmenting accurate predictions provided by modern neural networks with
well-calibrated predictive uncertainties has reinvigorated interest in Bayesian neural …

Proteins are responsible for the most diverse set of functions in biology. The ability to extract
information from protein sequences and to predict the effects of mutations is extremely …

Understanding the consequences of mutation for molecular fitness and function is a
fundamental problem in biology. Recently, generative probabilistic models have emerged as …

Abstract Models with a large number of latent variables are often used to utilize the
information in big or complex data, but can be difficult to estimate. Variational inference …

Bayesian Neural Networks (BNNs) have recently received increasing attention for their
ability to provide well-calibrated posterior uncertainties. However, model selection---even …