Graph Reparameterizations for Enabling 1000+ Monte Carlo Iterations in Bayesian Deep Neural Networks.

ABSTRACT: Uncertainty estimation in deep models is essential in many real-world applications and has benefited from developments over the last several years. Recent evidence [Farquhar et al., 2020] suggests that existing solutions dependent on simple Gaussian formulations may not be sufficient. However, moving to other distributions necessitates Monte Carlo (MC) sampling to estimate quantities such as the KL divergence: it could be expensive and scales poorly as the dimensions of both the input data and the model grow. This is directly related to the structure of the computation graph, which can grow linearly as a function of the number of MC samples needed. Here, we construct a framework to describe these computation graphs, and identify probability families where the graph size can be in

SUBMITTER: Nazarovs J 

PROVIDER: S-EPMC8432623 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature