Summary of From Neurons to Neutrons: a Case Study in Interpretability, by Ouail Kitouni et al.
From Neurons to Neutrons: A Case Study in Interpretability
by Ouail Kitouni, Niklas Nolte, Víctor Samuel Pérez-Díaz, Sokratis Trifinopoulos, Mike Williams
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Nuclear Theory (nucl-th)
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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 paper explores the idea of Mechanistic Interpretability (MI) in neural networks, aiming to understand how they make predictions. It challenges previous findings that even simple arithmetic models can implement various algorithms depending on initialization and hyperparameters. The researchers argue that high-dimensional neural networks can learn low-dimensional representations of training data, which can be understood through MI lens, providing insights faithful to human-derived domain knowledge. This approach can help derive new understanding from models trained to solve a problem. As a case study, the paper extracts nuclear physics concepts by studying models trained on nuclear data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how neural networks work and why they make certain predictions. It says that even simple math models can do different things depending on how they’re set up. The researchers think that big neural networks can learn smaller versions of the training data, which can be understood to help us understand what’s going on. This approach can help us figure out new things from models trained to solve problems. |
