Summary of An Introduction to Graphical Tensor Notation For Mechanistic Interpretability, by Jordan K. Taylor
An introduction to graphical tensor notation for mechanistic interpretability
by Jordan K. Taylor
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces graphical tensor notation, a simple way to denote linear operations on tensors, which is crucial for understanding modern deep learning. The notation helps visualize and simplify complex tensor operations, making it easier to understand neural networks and reverse-engineer their behavior for mechanistic interpretability. The authors apply the notation to various decompositions (SVD, CP, Tucker, and tensor network) and foundational approaches for language models, including an example “induction head” circuit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes tensors easy to understand! It introduces a new way to write down complicated math problems using pictures. This helps us see what’s happening inside big computers that learn like humans do. By using this new way of writing, we can figure out how these computers are working and make them better. The authors show how to use this method with different kinds of math problems and even create a special example for understanding language models. |
Keywords
* Artificial intelligence * Deep learning
