26949

tntorch: Tensor Network Learning with PyTorch

Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
ETH Zurich, Switzerland
arXiv:2206.11128 [cs.LG], (22 Jun 2022)

@misc{https://doi.org/10.48550/arxiv.2206.11128,

   doi={10.48550/ARXIV.2206.11128},

   url={https://arxiv.org/abs/2206.11128},

   author={Usvyatsov, Mikhail and Ballester-Ripoll, Rafael and Schindler, Konrad},

   keywords={Machine Learning (cs.LG), Mathematical Software (cs.MS), FOS: Computer and information sciences, FOS: Computer and information sciences},

   title={tntorch: Tensor Network Learning with PyTorch},

   publisher={arXiv},

   year={2022},

   copyright={Creative Commons Attribution 4.0 International}

}

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.
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