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Noise Optimization for Artificial Neural Networks
[article]
2021
arXiv
pre-print
Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work. In this work, we propose a new technique to compute the pathwise stochastic gradient estimate with respect to the standard deviation of the Gaussian noise added to each neuron of the ANN. By our proposed technique, the gradient estimate with respect to noise levels is a byproduct of the backpropagation algorithm for estimating gradient with respect to synaptic weights in ANN. Thus,
arXiv:2102.04450v1
fatcat:opijf3tjjvdndpe6lojxkfl5vi