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The Independence Prior of SAEs Fragments Visual Concepts
Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms. Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as…
AI Summary & Key Takeaways
- Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms
- Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as…
The Independence Prior of SAEs Fragments Visual Concepts
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