Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TensorFlow RiemOpt: a library for optimization on Riemannian manifolds   The adoption of neural networks and deep learning in non-Euclidean domains
has been hindered until recently by the lack of scalable and efficient learning
frameworks. Existing toolboxes in this space were mainly motivated by research
and education use cases, whereas practical aspects, such as deploying and
maintaining machine learning models, were often overlooked.
  We attempt to bridge this gap by proposing TensorFlow RiemOpt, a Python
library for optimization on Riemannian manifolds in TensorFlow. The library is
designed with the aim for a seamless integration with the TensorFlow ecosystem,
targeting not only research, but also streamlining production machine learning
pipelines.

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The categories of this paper are: eess
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The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: stat