Summary of Singular-limit Analysis Of Gradient Descent with Noise Injection, by Anna Shalova et al.
Singular-limit analysis of gradient descent with noise injection
by Anna Shalova, André Schlichting, Mark Peletier
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR)
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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 A machine learning paper explores the dynamics of large classes of noisy gradient descent systems in the overparameterized regime. In this setting, the algorithm evolves along the set of global minimizers of the loss function, which is important for generalization properties. The study characterizes this evolution for a broad class of algorithms and shows that different types of noise affect not only the process but also its time scale. The findings are applied to various scenarios, including Dropout, label noise, and classical SGD with minibatching. The results demonstrate that additional noise is required for regularization in some cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new paper looks at how noisy gradient descent systems work when there’s more information than what’s needed to solve a problem. In this situation, the algorithm moves along a set of points where the loss function is zero. This can help with making good predictions later on. The research shows that different types of noise can make the algorithm move faster or slower and even stop it from moving at all in some cases. It also applies these findings to real-world scenarios like training neural networks. |
Keywords
» Artificial intelligence » Dropout » Generalization » Gradient descent » Loss function » Machine learning » Regularization
