Summary of Data-induced Multiscale Losses and Efficient Multirate Gradient Descent Schemes, by Juncai He et al.
Data-induced multiscale losses and efficient multirate gradient descent schemes
by Juncai He, Liangchen Liu, Yen-Hsi Richard Tsai
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how machine learning algorithms, particularly deep learning models, are affected by multiscale data distributions. Multiscale datasets exhibit large variations in scale across different directions, which can impact the performance of these algorithms. The study reveals that multiscale structures appear in the loss landscape, including gradients and Hessians inherited from the dataset. To address this issue, the authors propose a novel gradient descent approach inspired by multiscale algorithms used in scientific computing. This approach aims to overcome the need for empirical learning rate selection, instead offering a more systematic and data-informed strategy to improve training efficiency, especially in later stages. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how big datasets can affect machine learning models. Big datasets can have different scales or sizes in different directions, which can make it harder for machines to learn from them. The study found that these differences create patterns in the way the model learns, including gradients and shapes of the learning process. To solve this problem, researchers came up with a new way for computers to adjust how they learn from data. This new method tries to find the best way to improve learning efficiency by using patterns in the data instead of just guessing. |
Keywords
* Artificial intelligence * Deep learning * Gradient descent * Machine learning