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Christopher Kanan

University of Rochester

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#machine learning Preprint Aug 2026

More Data Cannot Break a Symmetry: Identifiability by Design

Unsupervised representational alignment recovers a stimulus-by-stimulus correspondence from geometry alone, but the automorphism group of the stimulus geometry bounds what any such alignment can identify, before data exist. The obvious diagnostic for this degeneracy, the cheapest non-identity relabelling, ranks two pub...

Jing Xu, Christopher Kanan · 0 citations

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