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TensorLy: Tensor Learning in Python

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arxiv 1610.09555 v2 pith:EJKNV2OI submitted 2016-10-29 cs.LG

TensorLy: Tensor Learning in Python

Jean Kossaifi , Yannis Panagakis , Anima Anandkumar , Maja Pantic This is my paper
classification cs.LG
keywords tensorlytensormethodspythonallowsbackenddeeplearning
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Tensors are higher-order extensions of matrices. While matrix methods form the cornerstone of machine learning and data analysis, tensor methods have been gaining increasing traction. However, software support for tensor operations is not on the same footing. In order to bridge this gap, we have developed \emph{TensorLy}, a high-level API for tensor methods and deep tensorized neural networks in Python. TensorLy aims to follow the same standards adopted by the main projects of the Python scientific community, and seamlessly integrates with them. Its BSD license makes it suitable for both academic and commercial applications. TensorLy's backend system allows users to perform computations with NumPy, MXNet, PyTorch, TensorFlow and CuPy. They can be scaled on multiple CPU or GPU machines. In addition, using the deep-learning frameworks as backend allows users to easily design and train deep tensorized neural networks. TensorLy is available at https://github.com/tensorly/tensorly

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