Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TensorLy: Tensor Learning in Python   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

7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex