Summary of Can Llms Predict the Convergence Of Stochastic Gradient Descent?, by Oussama Zekri et al.
Can LLMs predict the convergence of Stochastic Gradient Descent?
by Oussama Zekri, Abdelhakim Benechehab, Ievgen Redko
First submitted to arxiv on: 3 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 abstract describes a study on large-language models (LLMs) that surprisingly excel at understanding governing principles of dynamical systems satisfying Markovian properties. The research explores this capability further by analyzing the dynamics of stochastic gradient descent (SGD) in convex and non-convex optimization, leveraging the theoretical link between SGD and Markov chains. The authors demonstrate LLMs’ remarkable zero-shot performance in predicting local minima that SGD converges to for previously unseen starting points. This raises possibilities for using LLMs in performing zero-shot randomized trials for larger deep learning models used in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can do amazing things, like understand how systems work and make predictions without being trained on them before. In this study, researchers looked at how these models work with a special kind of math called dynamical systems. They found that the models are really good at understanding how these systems behave, which could be useful for other types of artificial intelligence too. |
Keywords
* Artificial intelligence * Deep learning * Optimization * Stochastic gradient descent * Zero shot
