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Poster

Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting

Hippolyt Ritter · Aleksandar Botev · David Barber

Room 517 AB #135

Keywords: [ Online Learning ] [ Supervised Deep Networks ] [ Probabilistic Methods ]


Abstract:

We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks. The method is grounded in a Bayesian online learning framework, where we recursively approximate the posterior after every task with a Gaussian, leading to a quadratic penalty on changes to the weights. The Laplace approximation requires calculating the Hessian around a mode, which is typically intractable for modern architectures. In order to make our method scalable, we leverage recent block-diagonal Kronecker factored approximations to the curvature. Our algorithm achieves over 90% test accuracy across a sequence of 50 instantiations of the permuted MNIST dataset, substantially outperforming related methods for overcoming catastrophic forgetting.

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