  While deep learning methods continue to improve in predictive accuracy on a
wide range of application domains, significant issues remain with other aspects
of their performance including their ability to quantify uncertainty and their
robustness. Recent advances in approximate Bayesian inference hold significant
promise for addressing these concerns, but the computational scalability of
these methods can be problematic when applied to large-scale models. In this
paper, we describe initial work on the development ofURSABench(the Uncertainty,
Robustness, Scalability, and Accu-racy Benchmark), an open-source suite of
bench-marking tools for comprehensive assessment of approximate Bayesian
inference methods with a focus on deep learning-based classification tasks
