Summary of Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders, by Kola Ayonrinde
Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders
by Kola Ayonrinde
First submitted to arxiv on: 4 Nov 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 A novel approach to extracting features from neural networks, sparse autoencoders (SAEs), enables model interpretability and causal interventions on model internals by generating sparse feature representations using sparsifying activation functions. The paper frames token-feature matching as a resource allocation problem constrained by a total sparsity upper bound, introducing TopK SAEs that solve this problem with an additional constraint that each token matches with at most k features. To address limitations in TopK SAEs, the authors propose Feature Choice SAEs and Mutual Choice SAEs, which allow for variable numbers of active features per token. The paper also introduces a new auxiliary loss function, aux_zipf_loss, to mitigate dead and underutilised features. As a result, the proposed methods yield SAEs with fewer dead features and improved reconstruction loss at equivalent sparsity levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sparse autoencoders (SAEs) are a way to make neural networks easier to understand by breaking down what they’re doing. It’s like finding out which parts of a puzzle are most important. The paper talks about how SAEs work, and then shows some new ideas for making them better. This helps us understand complex models and even change their behavior. It’s an important step in using these powerful tools to learn more about the world. |
Keywords
* Artificial intelligence * Loss function * Token
