Summary of Motif Distribution and Function Of Sparse Deep Neural Networks, by Olivia T. Zahn et al.
Motif distribution and function of sparse deep neural networks
by Olivia T. Zahn, Thomas L. Daniel, J. Nathan Kutz
First submitted to arxiv on: 1 Mar 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 The paper explores the connectivity structure of deep neural networks (DNNs) using network motif theory. It compares the connectivity patterns of 350 DNNs trained to simulate a bio-mechanical flight control system with different randomly initialized parameters. The study finds that despite random initialization, enforced sparsity causes DNNs to converge to similar connectivity patterns characterized by their motif distributions. This suggests that neural network function can be encoded in motif distributions, offering insights into how to inform function and control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at the way deep neural networks are connected inside. It compares many different neural networks that were trained to do a specific task, like controlling a flying robot. The researchers found that even though each network started out differently, they all ended up having similar connections between their “neurons”. This is important because it could help us understand how these networks work and how we can use them in the future. |
Keywords
* Artificial intelligence * Neural network
