Summary of Gradient Descent with Generalized Newton’s Method, by Zhiqi Bu et al.
Gradient descent with generalized Newton’s method
by Zhiqi Bu, Shiyun Xu
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 generalized Newton’s method (GeN) is a new approach that improves optimization in machine learning models by dynamically selecting the optimal learning rate. This method can be used with various optimizers, including popular ones like SGD and Adam, and even covers the well-known Newton-Raphson method as a special case. GeN requires only minor additional computations during training, making it easily implementable. The authors demonstrate the effectiveness of GeN by matching state-of-the-art performance on language and vision tasks such as GPT and ResNet, which was previously achieved through careful tuning of learning rate schedulers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GeN is a new way to make machine learning models better. It helps optimizers like SGD and Adam find the best learning rate for themselves. This means we don’t need to spend time finding the right learning rate schedule anymore. GeN only takes a little extra computer power during training, making it easy to use. The authors tested GeN on some big tasks in language and vision, and it performed just as well as the best methods that required lots of tuning. |
Keywords
* Artificial intelligence * Gpt * Machine learning * Optimization * Resnet
