SiPPing Neural Networks: Sensitivity-informed Provable Pruning of Neural Networks

11 Oct 2019Cenk BaykalLucas LiebenweinIgor GilitschenskiDan FeldmanDaniela Rus

We introduce a pruning algorithm that provably sparsifies the parameters of a trained model in a way that approximately preserves the model's predictive accuracy. Our algorithm uses a small batch of input points to construct a data-informed importance sampling distribution over the network's parameters, and adaptively mixes a sampling-based and deterministic pruning procedure to discard redundant weights... (read more)

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