Summary of Nepenthe: Entropy-based Pruning As a Neural Network Depth’s Reducer, by Zhu Liao et al.
NEPENTHE: Entropy-Based Pruning as a Neural Network Depth’s Reducer
by Zhu Liao, Victor Quétu, Van-Tam Nguyen, Enzo Tartaglione
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research proposes a new approach called NEPENTHE to reduce the computational burden of deep neural networks by pruning unimportant connections. The authors identify that many tasks can be solved using shallower models, and their method focuses on removing layers with low entropy to linearize some layers without sacrificing performance. The technique is tested on popular architectures such as MobileNet and Swin-T, showing promise in reducing model depth while maintaining accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are super smart at solving tricky problems, but they use up a lot of computer power. Researchers have been trying to make these models more efficient by making them smaller, rather than deeper. This new method, called NEPENTHE, helps reduce the size of deep neural networks by removing connections that aren’t very important. The team tested this approach on some popular models and found that it can make the models smaller without losing their ability to solve problems. |
Keywords
» Artificial intelligence » Pruning
