Summary of Efficient Sketches For Training Data Attribution and Studying the Loss Landscape, by Andrea Schioppa
Efficient Sketches for Training Data Attribution and Studying the Loss Landscape
by Andrea Schioppa
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 framework offers a scalable solution for gradient and HVP sketching, addressing the memory constraints that come with storing large quantities of gradients or HVPs in modern machine learning models. The method is designed to leverage contemporary hardware capabilities, providing theoretical guarantees for its effectiveness. The authors demonstrate the practical applications of their approach in areas such as training data attribution, Hessian spectrum analysis, and intrinsic dimension computation for pre-trained language models. By shedding new light on the behavior of pre-trained language models, this work challenges prevailing assumptions about their intrinsic dimensionality and Hessian properties. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to store huge amounts of information about machine learning models without running out of memory. This is important because many machine learning models need to store lots of data to train properly. The new method works well on modern computers and can be used for tasks like figuring out how data affects model decisions, analyzing the structure of models’ internal calculations, and understanding how large language models work. By studying how these language models behave, this research challenges common assumptions about what makes them tick. |
Keywords
* Artificial intelligence * Machine learning
