Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Channel selection using Gumbel Softmax   Important applications such as mobile computing require reducing the
computational costs of neural network inference. Ideally, applications would
specify their preferred tradeoff between accuracy and speed, and the network
would optimize this end-to-end, using classification error to remove parts of
the network. Increasing speed can be done either during training - e.g.,
pruning filters - or during inference - e.g., conditionally executing a subset
of the layers. We propose a single end-to-end framework that can improve
inference efficiency in both settings. We use a combination of batch activation
loss and classification loss, and Gumbel reparameterization to learn network
structure. We train end-to-end, and the same technique supports pruning as well
as conditional computation. We obtain promising experimental results for
ImageNet classification with ResNet (45-52% less computation).

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The categories of this paper are: eess
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
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The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
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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: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: econ