Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FreezeOut: Accelerate Training by Progressively Freezing Layers   The early layers of a deep neural net have the fewest parameters, but take up
the most computation. In this extended abstract, we propose to only train the
hidden layers for a set portion of the training run, freezing them out
one-by-one and excluding them from the backward pass. Through experiments on
CIFAR, we empirically demonstrate that FreezeOut yields savings of up to 20%
wall-clock time during training with 3% loss in accuracy for DenseNets, a 20%
speedup without loss of accuracy for ResNets, and no improvement for VGG
networks. Our code is publicly available at
https://github.com/ajbrock/FreezeOut

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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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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: nucl-ex