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- W4297776108 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." @default.
- W4297776108 created "2022-10-01" @default.
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- W4297776108 date "2021-02-27" @default.
- W4297776108 modified "2023-09-26" @default.
- W4297776108 title "Variational Laplace for Bayesian neural networks" @default.
- W4297776108 doi "https://doi.org/10.48550/arxiv.2103.00222" @default.
- W4297776108 hasPublicationYear "2021" @default.
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