Variational Laplace for Bayesian neural networks

• URL: http://arxiv.org/abs/2011.10443v2
• Date: Tue, 10 Aug 2021 13:20:59 GMT
• Title: Variational Laplace for Bayesian neural networks
• Authors: Ali Unlu, Laurence Aitchison
• Abstract summary: We develop variational Laplace for Bayesian neural networks (BNNs) We exploit a local approximation of the curvature of the likelihood to estimate the ELBO without the need for sampling the neural-network weights. We show that early-stopping can be avoided by increasing the learning rate for the variance parameters.
• Score: 33.46810568687292
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop variational Laplace for Bayesian neural networks (BNNs) which exploits a local approximation of the curvature of the likelihood to estimate the ELBO without the need for stochastic sampling of the neural-network weights. The Variational Laplace objective is simple to evaluate, as it is (in essence) the log-likelihood, plus weight-decay, plus a squared-gradient regularizer. Variational Laplace gave better test performance and expected calibration errors than maximum a-posteriori inference and standard sampling-based variational inference, despite using the same variational approximate posterior. Finally, we emphasise care needed in benchmarking standard VI as there is a risk of stopping before the variance parameters have converged. We show that early-stopping can be avoided by increasing the learning rate for the variance parameters.

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