Summary of Sofim: Stochastic Optimization Using Regularized Fisher Information Matrix, by Mrinmay Sen et al.
SOFIM: Stochastic Optimization Using Regularized Fisher Information Matrix
by Mrinmay Sen, A. K. Qin, Gayathri C, Raghu Kishore N, Yen-Wei Chen, Balasubramanian Raman
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 introduces a new stochastic optimization method called SOFIM, which efficiently utilizes the regularized Fisher information matrix to approximate the Hessian matrix. This allows for efficient computation of Newton’s gradient update in large-scale machine learning model optimization. The approach can be viewed as a variant of natural gradient descent, addressing the challenge of calculating the full FIM through regularization and Sherman-Morrison matrix inversion. Like Adam, SOFIM uses the first moment to address non-stationary objectives. The method demonstrates improved convergence rate with same space and time complexities as SGD with momentum. Extensive experiments on deep learning model training using image classification datasets show that SOFIM outperforms state-of-the-art Newton optimization methods in terms of convergence speed for pre-specified training, test losses, and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to optimize machine learning models called SOFIM. It’s like a superpower for computers to learn from big data quickly. The method uses something called the Fisher information matrix to make it more efficient. This allows for faster and better optimization of deep learning models. Experiments show that this new method is much better than other state-of-the-art methods at optimizing machine learning models. |
Keywords
* Artificial intelligence * Deep learning * Gradient descent * Image classification * Machine learning * Optimization * Regularization
