Summary of Scalable Optimization in the Modular Norm, by Tim Large and Yang Liu and Minyoung Huh and Hyojin Bahng and Phillip Isola and Jeremy Bernstein
Scalable Optimization in the Modular Norm
by Tim Large, Yang Liu, Minyoung Huh, Hyojin Bahng, Phillip Isola, Jeremy Bernstein
First submitted to arxiv on: 23 May 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 ways to scale up neural networks by increasing the number and size of layers. To achieve this, the authors introduce a concept called the “modular norm,” which is defined recursively based on the network architecture. This norm enables normalization of weight updates for any base optimizer, making it possible to transfer learning rates across different widths and depths. The authors also show that the gradient of the neural network is Lipschitz-continuous in the modular norm, opening up opportunities for applying standard optimization techniques from deep learning. The paper includes a Python package called Modula that automatically normalizes weight updates using the modular norm. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve deep learning by making it easier to scale up neural networks. It does this by defining a new way to measure how big or small a network is, which they call the “modular norm.” This norm can be used to make sure that the updates to the network’s weights are properly sized, so that the network trains smoothly when its width and depth change. The authors also show that their norm has some nice theoretical properties, like being connected to Lipschitz continuity. They provide a Python package called Modula that makes it easy to use this norm in real-world applications. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Optimization » Transfer learning
