Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal FiLM: Capturing Long-Range Sequence Dependencies with
  Feature-Wise Modulations   Learning representations that accurately capture long-range dependencies in
sequential inputs -- including text, audio, and genomic data -- is a key
problem in deep learning. Feed-forward convolutional models capture only
feature interactions within finite receptive fields while recurrent
architectures can be slow and difficult to train due to vanishing gradients.
Here, we propose Temporal Feature-Wise Linear Modulation (TFiLM) -- a novel
architectural component inspired by adaptive batch normalization and its
extensions -- that uses a recurrent neural network to alter the activations of
a convolutional model. This approach expands the receptive field of
convolutional sequence models with minimal computational overhead. Empirically,
we find that TFiLM significantly improves the learning speed and accuracy of
feed-forward neural networks on a range of generative and discriminative
learning tasks, including text classification and audio super-resolution

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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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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: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: ao-sci
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: nlin