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arXiv · 1911.05585

Structured Sparsification of Gated Recurrent Neural Networks

Ekaterina Lobacheva·Nadezhda Chirkova·Alexander Markovich·Dmitry Vetrov
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Abstract

Recently, a lot of techniques were developed to sparsify the weights of neural networks and to remove networks' structure units, e.g. neurons. We adjust the existing sparsification approaches to the gated recurrent architectures. Specifically, in addition to the sparsification of weights and neurons, we propose sparsifying the preactivations of gates. This makes some gates constant and simplifies LSTM structure. We test our approach on the text classification and language modeling tasks. We observe that the resulting structure of gate sparsity depends on the task and connect the learned structure to the specifics of the particular tasks. Our method also improves neuron-wise compression of the model in most of the tasks.

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Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry Vetrov. 2019-11-13. Structured Sparsification of Gated Recurrent Neural Networks. https://arxiv.org/abs/1911.05585

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