AI RESEARCH
A Muon-Accelerated Algorithm for Low Separation Rank Tensor Generalized Linear Models
arXiv CS.LG
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ArXi:2604.04726v1 Announce Type: cross Tensor-valued data arise naturally in multidimensional signal and imaging problems, such as biomedical imaging. When incorporated into generalized linear models (GLMs), naive vectorization can destroy their multi-way structure and lead to high-dimensional, ill-posed estimation. To address this challenge, Low Separation Rank (LSR) decompositions reduce model complexity by imposing low-rank multilinear structure on the coefficient tensor.