Summary of Clustering and Alignment: Understanding the Training Dynamics in Modular Addition, by Tiberiu Musat
Clustering and Alignment: Understanding the Training Dynamics in Modular Addition
by Tiberiu Musat
First submitted to arxiv on: 18 Aug 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 recent study on the training dynamics of a small neural network with 2-dimensional embeddings reveals that embedding vectors organize into grid and circle structures during modular addition tasks. This is attributed to two simple tendencies: clustering and alignment between pairs of embeddings, which can be modeled using explicit formulae as interaction forces. The emergence of these structures is fully accounted for by constructing an equivalent particle simulation. Weight decay plays a crucial role in this setup, linking regularization to training dynamics. An interactive demo supporting the findings is available at https://modular-addition.vercel.app/. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A neural network learns algorithms to solve simple problems, but how these algorithms emerge during training isn’t well understood. This study looks at a small network with 2D embeddings and finds that they organize into grid-like patterns. It seems that the network is trying to group similar things together and line them up in a specific way. The researchers show that this can be explained by simple rules, like how particles move around each other. They also find that adding some “noise” or regularization helps the network learn better. You can try out their interactive demo online. |
Keywords
» Artificial intelligence » Alignment » Clustering » Embedding » Neural network » Regularization
