Summary of Corridor Geometry in Gradient-based Optimization, by Benoit Dherin and Mihaela Rosca
Corridor Geometry in Gradient-Based Optimization
by Benoit Dherin, Mihaela Rosca
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 characterizes regions of a loss surface as “corridors” where the continuous curves of steepest descent become straight lines. This phenomenon provides insights into gradient-based optimization, showing that corridors are areas where gradient descent and the gradient flow follow the same trajectory, resulting in linear loss decrease. The authors develop a learning rate adaptation scheme, Corridor Learning Rate (CLR), which coincides with a special case of Polyak step-size. The CLR formulation is tested on CIFAR-10 and ImageNet datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how computers learn from mistakes. It finds a way to make sure that the computer’s learning process stays on track, without getting stuck or going in circles. This is important because it can help computers learn faster and better. The method uses something called “corridors” which are areas where the computer’s learning process goes smoothly. |
Keywords
* Artificial intelligence * Gradient descent * Optimization
