# Group-invariant tensor train networks for supervised learning

@article{Sprangers2022GroupinvariantTT, title={Group-invariant tensor train networks for supervised learning}, author={Brent Sprangers and Nick Vannieuwenhoven}, journal={ArXiv}, year={2022}, volume={abs/2206.15051} }

. Invariance has recently proven to be a powerful inductive bias in machine learning models. One such class of predictive or generative models are tensor networks. We introduce a new numerical algorithm to construct a basis of tensors that are invariant under the action of normal matrix representations of an arbitrary discrete group. This method can be up to several orders of magnitude faster than previous approaches. The group-invariant tensors are then combined into a group-invariant tensor…

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