Noise benefits in backpropagation and deep bidirectional pre-training


We prove that noise can speed convergence in the backpropagation algorithm. The proof consists of two separate results. The first result proves that the backpropagation algorithm is a special case of the generalized Expectation-Maximization (EM) algorithm for iterative maximum likelihood estimation. The second result uses the recent EM noise benefit to… (More)
DOI: 10.1109/IJCNN.2013.6707022


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