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Reducing the size of a neural network (pruning) by removing weights withoutimpacting its performance is an important problem for resource-constraineddevices. In the past, pruning was typically accomplished by ranking orpenalizing weights based on criteria like magnitude and removing low-rankedweights before retraining the remaining ones. Pruning strategies may alsoinvolve removing neurons from the network in order to achieve the desiredreduction in network size. We formulate pruning as an optimization problem withthe objective of minimizing misclassifications by selecting specific weights.To accomplish this, we have introduced the concept of chaos in learning(Lyapunov exponents) via weight updates and exploiting causality to identifythe causal weights responsible for misclassification. Such a pruned networkmaintains the original performance and retains feature explainability.

To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks