# Bayesian Sparsification of Gated Recurrent Neural Networks

arXiv: https://arxiv.org/abs/1812.05692 · HTML: https://mlsanity.com/paper/1812.05692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [Sparse Deep Learning for Time Series Data: Theory and Applications](https://mlsanity.com/paper/2310.03243/) (2023)

## Abstract (arXiv)

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. Moreover, the resulting structure of gate sparsity is interpretable and depends on the task. Code is available on github: https://github.com/tipt0p/SparseBayesianRNN