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Link to original content: https://doi.org/10.48550/arXiv.2206.11128
[2206.11128] tntorch: Tensor Network Learning with PyTorch
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Computer Science > Machine Learning

arXiv:2206.11128 (cs)
[Submitted on 22 Jun 2022 (v1), last revised 21 Sep 2022 (this version, v2)]

Title:tntorch: Tensor Network Learning with PyTorch

Authors:Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
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Abstract: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.
Subjects: Machine Learning (cs.LG); Mathematical Software (cs.MS)
Cite as: arXiv:2206.11128 [cs.LG]
  (or arXiv:2206.11128v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2206.11128
Journal reference: JMLR (2022) 23-208

Submission history

From: Mikhail Usvyatsov [view email]
[v1] Wed, 22 Jun 2022 14:19:15 UTC (5,052 KB)
[v2] Wed, 21 Sep 2022 07:06:51 UTC (5,055 KB)
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