Summary of To Clip or Not to Clip: the Dynamics Of Sgd with Gradient Clipping in High-dimensions, by Noah Marshall et al.
To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions
by Noah Marshall, Ke Liang Xiao, Atish Agarwala, Elliot Paquette
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 novel theoretical analysis of gradient clipping, a practical adaptive optimization method, is presented in this work. The study focuses on least squares problems under streaming stochastic gradient descent (SGD) and develops a framework to understand the learning dynamics in the limit of large intrinsic dimension. This model-dependent notion of dimensionality is found to be crucial in determining the performance of clipped SGD on various datasets, including synthetic data, CIFAR10, and Wikitext2. The results demonstrate that clipping cannot improve SGD performance when Gaussian noise is present, but can provide benefits in other noisy settings with careful tuning of the clipping threshold. A simple heuristic for near-optimal scheduling of the clipping threshold is proposed, requiring the tuning of only one hyperparameter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gradient clipping is a way to make machine learning models work better on big datasets. This paper looks at how clipping works in a special kind of problem called least squares. The study finds a formula that shows how the loss (how well the model does) changes over time when using clipped SGD. They test this formula on fake data, pictures of animals and objects (CIFAR10), and text data (Wikitext2). The results show that clipping doesn’t help when there’s just noise in the data, but it can make a difference if there are other kinds of noise. The paper also suggests an easy way to set the right amount for clipping. |
Keywords
* Artificial intelligence * Hyperparameter * Machine learning * Optimization * Stochastic gradient descent * Synthetic data
