Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Compression of Deep Neural Networks

arXiv:2606.00130v1 Announce Type: new Abstract: We study Automatically Differentiable Nonlinear Tensor Networks (ADNTNs), a family of structured weight generators whose compact core tensors are trained end-to-end by reverse-mode automatic differentiation (AD). The approach can be viewed as a natural extension of low-rank adaptation and tensor factorisation: instead of using one low-rank matrix update, an ADNTN builds a large weight tensor through a hierarchy of small cores, nonlinear activations, and optional lateral mixing tensors. The paper focuses on three architectures: Tree Tensor Network

Source: arXiv cs.LG — read the full report at the original publisher.

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