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Bayesian Sparsification of Gated Recurrent Neural Networks
[article]
2018
arXiv
pre-print
Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e. g. neurons. We apply and further develop this approach for gated recurrent architectures. Specifically, in addition to sparsification of individual weights and neurons, we propose to sparsify preactivations of gates and information flow in LSTM. It makes some gates and information flow components constant, speeds up forward pass and improves compression.
arXiv:1812.05692v1
fatcat:ex3adxatfjaqhanfsirkulum6i