Tntorch: Tensor Network Learning with PyTorch - Ballester Ripoll, Rafael; Usvyatsov, Mikhail; Schindler, Konrad (JMLR, 2022)
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, crossapproximation, batch processing, comprehensive tensor arithmetics, and more.
Keywords: Tensor decompositions; Pytorch; Low-rank methods; Multilinear algebra