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Poster
tntorch: Tensor Network Learning with PyTorch
Mikhail Usvyatsov · Rafael Ballester-Ripoll · Konrad Schindler

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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.

Keywords: Journal Track · JMLR
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Author Information

Mikhail Usvyatsov (ETH Zürich)
Rafael Ballester-Ripoll
Konrad Schindler (ETH Zürich)

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