Understanding Uncertainty in Bayesian Deep Learning
This addresses uncertainty estimation for Bayesian deep learning practitioners, but it is incremental as it builds on existing NLM frameworks.
The paper tackled the problem of Neural Linear Models (NLM) underestimating uncertainty in data-scarce regions, and the result was a novel training method that captures predictive uncertainties and allows for domain knowledge incorporation.
Neural Linear Models (NLM) are deep Bayesian models that produce predictive uncertainty by learning features from the data and then performing Bayesian linear regression over these features. Despite their popularity, few works have focused on formally evaluating the predictive uncertainties of these models. Furthermore, existing works point out the difficulties of encoding domain knowledge in models like NLMs, making them unsuitable for applications where interpretability is required. In this work, we show that traditional training procedures for NLMs can drastically underestimate uncertainty in data-scarce regions. We identify the underlying reasons for this behavior and propose a novel training method that can both capture useful predictive uncertainties as well as allow for incorporation of domain knowledge.