Summary of Applications Of Fractional Calculus in Learned Optimization, by Teodor Alexandru Szente et al.
Applications of fractional calculus in learned optimization
by Teodor Alexandru Szente, James Harrison, Mihai Zanfir, Cristian Sminchisescu
First submitted to arxiv on: 22 Nov 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 the capabilities of fractional gradient descent in navigating complex optimization landscapes and solving problems involving non-linearities and chaotic dynamics. Building upon existing research on incorporating fractional-order derivatives into traditional gradient descent methods, this work introduces a novel approach for training neural networks to predict the order of the gradient effectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that it’s possible to train a neural network to predict the order of the gradient, which is important for solving certain types of problems. The researchers are trying to find a way to make fractional gradient descent work better by letting a machine learn how to set the right parameters. |
Keywords
» Artificial intelligence » Gradient descent » Neural network » Optimization
