Pruning a neural network using Bayesian inference

• URL: http://arxiv.org/abs/2308.02451v1
• Date: Fri, 4 Aug 2023 16:34:06 GMT
• Title: Pruning a neural network using Bayesian inference
• Authors: Sunil Mathew, Daniel B. Rowe
• Abstract summary: Neural network pruning is a highly effective technique aimed at reducing the computational and memory demands of large neural networks. We present a novel approach to pruning neural networks utilizing Bayesian inference, which can seamlessly integrate into the training procedure.
• Score: 1.776746672434207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network pruning is a highly effective technique aimed at reducing the computational and memory demands of large neural networks. In this research paper, we present a novel approach to pruning neural networks utilizing Bayesian inference, which can seamlessly integrate into the training procedure. Our proposed method leverages the posterior probabilities of the neural network prior to and following pruning, enabling the calculation of Bayes factors. The calculated Bayes factors guide the iterative pruning. Through comprehensive evaluations conducted on multiple benchmarks, we demonstrate that our method achieves desired levels of sparsity while maintaining competitive accuracy.

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