Summary of Features Are Fate: a Theory Of Transfer Learning in High-dimensional Regression, by Javan Tahir et al.
Features are fate: a theory of transfer learning in high-dimensional regression
by Javan Tahir, Surya Ganguli, Grant M. Rotskoff
First submitted to arxiv on: 10 Oct 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 In this paper, researchers explore how pre-trained neural networks can be adapted for use in downstream tasks with limited data. They examine existing methods such as fine-tuning, preference optimization, and transfer learning, which work well when the target task is similar to the source task. However, they show that simple measures of similarity between source and target distributions are not sufficient to predict the success of transfer. Instead, they propose a feature-centric view on transfer learning, establishing theoretical results demonstrating that when the target task is well-represented by the pre-trained model’s feature space, transfer learning outperforms training from scratch. The study focuses on deep linear networks as a minimal model, analytically characterizing the transferability phase diagram and rigorously showing that strong feature space overlap between source and target tasks improves performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using pre-trained neural networks for new tasks with limited data. It looks at different ways to make this work, like fine-tuning and transfer learning. The researchers found that just comparing the data from the old task to the new one isn’t enough – we need a better approach. They came up with an idea based on what features are important in the pre-trained network, and showed that when these features match well between tasks, it works better. |
Keywords
» Artificial intelligence » Fine tuning » Optimization » Transfer learning » Transferability
