Summary of Local Methods with Adaptivity Via Scaling, by Savelii Chezhegov et al.
Local Methods with Adaptivity via Scaling
by Savelii Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Martin Takáč, Yaroslav Kholodov, Aleksandr Beznosikov
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Optimization and Control (math.OC)
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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 introduces a novel approach to distributed learning that combines local training with adaptive scaling techniques. By merging Local SGD with popular scaling methods like Adam, RMSProp, and OASIS, the authors aim to develop efficient distributed optimization strategies for modern machine learning models. The proposed method is theoretically analyzed and validated through experiments on neural network training. The paper’s contributions include a unified framework for analyzing various adaptive scaling approaches and experimental results demonstrating the effectiveness of the new methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make machine learning work better when you have many computers working together. It does this by combining two ideas: letting each computer do some work before sharing with others, and using clever ways to adjust how fast they learn. The goal is to speed up the process so we can train bigger and more complex models faster. The authors test their new method on a neural network and show that it works well. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Optimization
