Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mode Normalization   Normalization methods are a central building block in the deep learning
toolbox. They accelerate and stabilize training, while decreasing the
dependence on manually tuned learning rate schedules. When learning from
multi-modal distributions, the effectiveness of batch normalization (BN),
arguably the most prominent normalization method, is reduced. As a remedy, we
propose a more flexible approach: by extending the normalization to more than a
single mean and variance, we detect modes of data on-the-fly, jointly
normalizing samples that share common features. We demonstrate that our method
outperforms BN and other widely used normalization techniques in several
experiments, including single and multi-task datasets.

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The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: eess
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an