Summary of Revisiting Scalable Hessian Diagonal Approximations For Applications in Reinforcement Learning, by Mohamed Elsayed et al.
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
by Mohamed Elsayed, Homayoon Farrahi, Felix Dangel, A. Rupam Mahmood
First submitted to arxiv on: 5 Jun 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 A novel approach to computing second-order information is proposed, focusing on approximating Hessian diagonals at a cost similar to gradient computation. The authors revisit an early approximation scheme introduced by Becker and LeCun (1989), dubbed HesScale, which builds upon this idea and adds negligible extra computation. Experimental results demonstrate that HesScale outperforms existing methods in small networks, exhibiting higher quality and faster optimization rates. This breakthrough has implications for scaling second-order methods in larger models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers found a new way to get useful information from complex math problems. They looked at an old idea that’s been around since the 1980s and improved it to make it work better. This new method, called HesScale, is fast and accurate, which means it can help machines learn faster and more efficiently. The scientists tested their idea on small networks and found that it works better than other methods that are already known. They’re excited about the possibilities for using this approach in bigger models to make even more progress. |
Keywords
» Artificial intelligence » Optimization
