The paper frames Cayley-table completion as the discrete algebraic analog to matrix completion and poses the open problem of proving exact recovery bounds under flatness priors that favor associativity.
Simplicity bias in 1-hidden layer neural networks.arXiv preprint arXiv:2302.00457,
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Open Problem: Separating Geometric and Algorithmic Compression via Cayley-Table Completion
The paper frames Cayley-table completion as the discrete algebraic analog to matrix completion and poses the open problem of proving exact recovery bounds under flatness priors that favor associativity.