Summary of Inverse-free Fast Natural Gradient Descent Method For Deep Learning, by Xinwei Ou et al.
Inverse-Free Fast Natural Gradient Descent Method for Deep Learning
by Xinwei Ou, Ce Zhu, Xiaolin Huang, Yipeng Liu
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposed Fast Natural Gradient Descent (FNGD) method achieves faster convergence rates compared to first-order methods in deep learning, leveraging second-order optimization techniques. By only requiring inversion during the initial epoch and sharing weighted coefficients across subsequent epochs, FNGD exhibits a computational complexity comparable to that of first-order methods. This approach is demonstrated on image classification and machine translation tasks, achieving speedups of 2.07x over KFAC for training ResNet-18 on CIFAR-100 and outperforming AdamW by 24 BLEU score while requiring similar training time for Transformer on Multi30K. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to train deep learning models is presented. This method, called Fast Natural Gradient Descent (FNGD), makes computers work faster when teaching AI systems. FNGD uses special math tricks to make the training process more efficient. It does this by only doing some complicated calculations once at the beginning and then using those results for the rest of the training process. This makes FNGD run much faster than other methods, which need to do these calculations every time they update their ideas about what’s important in the data. |
Keywords
* Artificial intelligence * Bleu * Deep learning * Gradient descent * Image classification * Optimization * Resnet * Transformer * Translation
