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• W3087194612 abstract "Recent work has explored the possibility of pruning neural networks at initialization. We assess proposals for doing so: SNIP (Lee et al., 2019), GraSP (Wang et al., 2020), SynFlow (Tanaka et al., 2020), and magnitude pruning. Although these methods surpass the trivial baseline of random pruning, they remain below the accuracy of magnitude pruning after training, and we endeavor to understand why. We show that, unlike pruning after training, accuracy is the same or higher when randomly shuffling which weights these methods prune within each layer or sampling new initial values. As such, the per-weight pruning decisions made by these methods can be replaced by a per-layer choice of the fraction of weights to prune. This property undermines the claimed justifications for these methods and suggests broader challenges with the underlying pruning heuristics, the desire to prune at initialization, or both." @default.
• W3087194612 created "2020-09-25" @default.
• W3087194612 creator A5021062973 @default.
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• W3087194612 date "2020-09-18" @default.
• W3087194612 modified "2023-09-23" @default.
• W3087194612 title "Pruning Neural Networks at Initialization: Why are We Missing the Mark?" @default.
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• W3087194612 hasPublicationYear "2020" @default.
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