tntorch:使用PyTorch学习张量网络

Mikhail (Misha) Usvyatsov, R. Ballester-Ripoll, K. Schindler
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引用次数: 12

摘要

我们提出了一个张量学习框架tntorch,它支持在统一接口下的多种分解(包括Candecomp/Parafac、Tucker和tensor Train)。使用我们的库,用户可以学习和处理低秩张量,具有自动微分,无缝GPU支持以及PyTorch API的便利性。除了分解算法,tntorch还实现了可微张量代数、秩截断、交叉逼近、批处理、综合张量算法等。
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tntorch: Tensor Network Learning with PyTorch
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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