Principled Pruning of Bayesian Neural Networks through Variational Free Energy Minimization
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Bayesian model reduction provides an efficient approach for comparing the performance of all nested sub-models of a model, without re-evaluating any of these sub-models. Until now, Bayesian model reduction has been applied mainly in the computational neuroscience community. In this paper, we formulate and apply Bayesian model reduction to perform principled pruning of Bayesian neural networks, based on variational free energy minimization. This novel parameter pruning scheme solves the shortcomings of many current state-of-the-art pruning methods that are used by the signal processing community. The proposed approach has a clear stopping criterion and minimizes the same objective that is used during training. Next to these theoretical benefits, our experiments indicate better model performance in comparison to state-of-the-art pruning schemes.
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