Dual-Channel Tensor Neural Networks: Finite-Sample Theory and Conformal Structure Selection

ArXi:2605.19122v1 Announce Type: cross Tensor-valued data arise naturally in neuroimaging, genomics, climate science, and spatiotemporal networks, where multilinear dependencies across modes carry information that is destroyed under vectorization. Existing approaches either impose a single low-rank structure, which can miss localized signal, or treat the tensor as a long vector, which discards its multiway geometry.