| With respect to the deep learning networks ( as well as more traditional with just a weight matrix and bias), we can look to matrices for this. I will offer a compressed representation, not so much a way of pruning, but I explain why below. Each single layer neural network is made up of 3 matrices, a weight matrix (connections), visible bias, and a hidden bias. In theory, this can be represented as a flattened array. This is what I do in deeplearning4j[1] for optimization (note: I'm the author) The problem with pruning neural nets so to speak is this isn't really a search problem we're solving like A*. Both are graphs in a sense, but each neuron in a neural net when we have backpropagation gets updated to correct for error that it caused, rather than pruning like in Alpha Beta Pruning for Game playing AI. I will offer one last thing and say that the way neural nets learn (especially if you stream data in to it for training rather than training all at once via online/mini batch learning) each neuron also tends to learn different components of an overall solution and some will activate more on certain feature vectors when you train them on an overall data set. A solution to this is to set the neurons relative to the input size[2]. [1] https://deeplearning4j.org/ [2] http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf |