Summary of Convergence Of Continuous-time Stochastic Gradient Descent with Applications to Linear Deep Neural Networks, by Gabor Lugosi and Eulalia Nualart
Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks
by Gabor Lugosi, Eulalia Nualart
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); 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 In this paper, researchers investigate a continuous-time approximation of stochastic gradient descent, a widely used optimization technique in machine learning. The study establishes general sufficient conditions for convergence, building upon previous work by Chatterjee (2022) on non-stochastic gradient descent. The main result is applied to the case of overparametrized linear neural network training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how computers can learn from data more efficiently. It finds a way to simplify a complex math problem that helps train artificial intelligence models, like those used in self-driving cars or medical diagnosis. By studying how this process works, scientists hope to improve the performance of these AI systems. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Optimization » Stochastic gradient descent
