Summary of Pretraining with Random Noise For Fast and Robust Learning Without Weight Transport, by Jeonghwan Cheon et al.
Pretraining with Random Noise for Fast and Robust Learning without Weight Transport
by Jeonghwan Cheon, Sang Wan Lee, Se-Bum Paik
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 presents research on pre-training neural networks using random noise, which enhances learning efficiency and generalization abilities without requiring weight transport. The authors demonstrate that this approach modifies forward weights to match backward synaptic feedback, necessary for teaching errors with feedback alignment. A network with pre-aligned weights learns faster than one without random noise training, even comparable to backpropagation algorithms. Sequential training with both random noise and data brings weights closer to synaptic feedback, enabling precise credit assignment and faster learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, the paper shows that randomly generating noise can help neural networks learn better and generalize well. This is achieved by modifying how the network’s connections are set up, allowing it to learn more quickly and accurately. By using this approach, the network becomes better at solving problems and adapting to new situations. |
Keywords
» Artificial intelligence » Alignment » Backpropagation » Generalization
