Summary of Masks, Signs, and Learning Rate Rewinding, by Advait Gadhikar and Rebekka Burkholz
Masks, Signs, And Learning Rate Rewinding
by Advait Gadhikar, Rebekka Burkholz
First submitted to arxiv on: 29 Feb 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 Learning Rate Rewinding (LRR), a variant of Iterative Magnitude Pruning (IMP), to find lottery tickets in deep overparameterized neural networks. LRR combines structure and parameter learning, and understanding its strengths can lead to designing more flexible algorithms that optimize diverse sparse architectures. The study disentangles the effects of mask learning and parameter optimization, demonstrating how both benefit from overparameterization. Results show LRR’s ability to flip parameter signs early and stay robust to sign perturbations makes it effective in identifying masks and optimizing diverse sets, including random ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning Rate Rewinding is a way to improve neural networks by finding special patterns called “lottery tickets” within the network. Researchers are trying to understand how this works so they can create new algorithms that work well with many different types of architectures. They tested Learning Rate Rewinding and compared it to another method, showing that it’s better at finding these special patterns. |
Keywords
* Artificial intelligence * Mask * Optimization * Pruning
