  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.
