A Framework for Neural Network Pruning Using Gibbs Distributions
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Neural network pruning is an important technique for creating efficient machine learning models that can run on edge devices. We propose a new, highly flexible approach to neural network pruning based on Gibbs distributions. We apply it with Hamiltonians that are based on weight magnitude, using the annealing capabilities of Gibbs distributions to smoothly move from regularization to adaptive pruning during an ordinary neural network training schedule. This method can be used for either unstructured or structured pruning, and we provide explicit formulations for both. We compare our proposed method to several established pruning methods on ResNet variants and find that it outperforms them for unstructured, kernel-wise, and filter-wise pruning.
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