Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UTS submission to Google YouTube-8M Challenge 2017   In this paper, we present our solution to Google YouTube-8M Video
Classification Challenge 2017. We leveraged both video-level and frame-level
features in the submission. For video-level classification, we simply used a
200-mixture Mixture of Experts (MoE) layer, which achieves GAP 0.802 on the
validation set with a single model. For frame-level classification, we utilized
several variants of recurrent neural networks, sequence aggregation with
attention mechanism and 1D convolutional models. We achieved GAP 0.8408 on the
private testing set with the ensemble model.
  The source code of our models can be found in
\url{https://github.com/ffmpbgrnn/yt8m}.

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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: eess
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom