Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fractional Max-Pooling   Convolutional networks almost always incorporate some form of spatial
pooling, and very often it is alpha times alpha max-pooling with alpha=2.
Max-pooling act on the hidden layers of the network, reducing their size by an
integer multiplicative factor alpha. The amazing by-product of discarding 75%
of your data is that you build into the network a degree of invariance with
respect to translations and elastic distortions. However, if you simply
alternate convolutional layers with max-pooling layers, performance is limited
due to the rapid reduction in spatial size, and the disjoint nature of the
pooling regions. We have formulated a fractional version of max-pooling where
alpha is allowed to take non-integer values. Our version of max-pooling is
stochastic as there are lots of different ways of constructing suitable pooling
regions. We find that our form of fractional max-pooling reduces overfitting on
a variety of datasets: for instance, we improve on the state-of-the art for
CIFAR-100 without even using dropout.

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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th