Summary of Geometric Sparsification in Recurrent Neural Networks, by Wyatt Mackey et al.
Geometric sparsification in recurrent neural networks
by Wyatt Mackey, Ioannis Schizas, Jared Deighton, David L. Boothe Jr., Vasileios Maroulas
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to sparsifying recurrent neural networks (RNNs) is proposed, combining moduli regularization with magnitude pruning. Moduli regularization leverages the RNN’s dynamical system to induce a geometric relationship between neurons in the hidden state, providing an explicit description of the desired sparse architecture and end-to-end learning of RNN geometry. This technique is verified under diverse conditions, including navigation, natural language processing, and addition tasks. Key results include achieving 90% sparsity while maintaining model performance in navigation tasks, as well as inducing more stable RNNs with high-fidelity models above 90% sparsity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make neural networks smaller is described. This method, called moduli regularization, helps recurrent neural nets (RNNs) become less complex without losing their ability to work well. By using this technique in combination with another method called magnitude pruning, researchers can create RNNs that are both efficient and accurate. |
Keywords
» Artificial intelligence » Natural language processing » Pruning » Regularization » Rnn
