Summary of Occam Gradient Descent, by B.n. Kausik
Occam Gradient Descent
by B.N. Kausik
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 algorithm, Occam Gradient Descent, is proposed to balance the competing demands of deep learning models in adapting to their problem domain while avoiding overfitting. This approach interleaves adaptive reduction of model size to minimize generalization error with gradient descent on model weights to minimize fitting error. The algorithm simultaneously descends the space of weights and topological size of any neural network without modification, leveraging learning theory principles. Empirical experiments demonstrate that Occam Gradient Descent outperforms traditional gradient descent in various tasks, including image classification, tabular data classification, and natural language processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to train deep learning models called Occam Gradient Descent. This method helps balance the size of the model with its ability to avoid overfitting. The algorithm works by reducing the size of the model while training it, which makes it more efficient and better at generalizing. In experiments, this approach showed that it can perform better than traditional methods on tasks like image classification, text analysis, and predicting data from tables. |
Keywords
» Artificial intelligence » Classification » Deep learning » Generalization » Gradient descent » Image classification » Natural language processing » Neural network » Overfitting
