Summary of Can Sparse Autoencoders Make Sense Of Latent Representations?, by Viktoria Schuster
Can sparse autoencoders make sense of latent representations?
by Viktoria Schuster
First submitted to arxiv on: 15 Oct 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 The proposed research explores the potential of sparse autoencoders (SAEs) in decomposing latent representations in complex biological data, where variables are often unknown. The study finds that SAEs can encode observable upstream hidden variables in superposition, with the degree of learning dependent on variable type and model architecture. While superpositions are not identifiable if generative variables are unknown, SAEs can recover these variables and their structure. Applications to single-cell multi-omics data show that SAEs can uncover key biological processes. The research also presents an automated method for linking SAE features to biological concepts, enabling large-scale analysis of single-cell expression models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sparse autoencoders (SAEs) help us understand complex biological data better. They can mix and match different parts of hidden variables in a special way called superposition. How well they learn depends on the type of variable and the model used. If we don’t know what the original variables are, SAEs can still find them and show how they’re connected to the things we can observe. This is useful for analyzing single-cell data, which has many different types of information all mixed together. |
