We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low-rank tensors with automatic differentiation, seamless GPU support, and the convenience of PyTorch's API. Besides decomposition algorithms, tntorch implements differentiable tensor algebra, rank truncation, cross-approximation, batch processing, comprehensive tensor arithmetics, and more.
@article{arxiv.2206.11128,
title = {tntorch: Tensor Network Learning with PyTorch},
author = {Mikhail Usvyatsov and Rafael Ballester-Ripoll and Konrad Schindler},
journal= {arXiv preprint arXiv:2206.11128},
year = {2022}
}