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Complete or Sparse: A Tale of Two Identifiabilities

arXiv:2610.20851v1 Announce Type: new Abstract: Understanding when neural networks learn interpretable representations is central to mechanistic interpretability. We study this through identifiability: when can supervised learning recover the true latent variables underlying the data? We introduce…

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  1. 2026-10-16 04:00 · arXiv stat.ML
    Complete or Sparse: A Tale of Two Identifiabilities

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