Year: 2021
Author: Chirag Agarwal, Joe Klobusicky, Dan Schonfeld
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 1 : pp. 147–158
Abstract
We study a class of deep neural networks with architectures that form a directed acyclic graph (DAG). For backpropagation defined by gradient descent with adaptive momentum, we show weights converge for a large class of nonlinear activation functions. The proof generalizes the results of Wu et al. (2008) who showed convergence for a feed-forward network with one hidden layer. For an example of the effectiveness of DAG architectures, we describe an example of compression through an AutoEncoder, and compare against sequential feed-forward networks under several metrics.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1912-m2018-0279
Journal of Computational Mathematics, Vol. 39 (2021), Iss. 1 : pp. 147–158
Published online: 2021-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Backpropagation with momentum Autoencoders Directed acyclic graphs.
Author Details
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