Tensor similarity is a symmetry-invariant metric that measures functional equivalence between tensor-based networks using a recursive algorithm for cross-layer mechanisms.
Maxime Méloux, Silviu Maniu, François Portet, and Maxime Peyrard
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abstract
We show that Isserlis' theorem follows as a corollary to the invariant tensor theorem for isotropic tensors.
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Under the common-cause hypothesis, the nonlocality window is contained in the speciation window, and both sharpen to a single critical time in the large-system limit, verified analytically in Gaussian mixtures.
Resolvents of the sample covariances in the separable mixture model approximate deterministic matrices defined via solutions to a dual system of equations, without simultaneous diagonalizability assumptions.
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When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability
Tensor similarity is a symmetry-invariant metric that measures functional equivalence between tensor-based networks using a recursive algorithm for cross-layer mechanisms.