Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dropout Inference with Non-Uniform Weight Scaling   Dropout as regularization has been used extensively to prevent overfitting
for training neural networks. During training, units and their connections are
randomly dropped, which could be considered as sampling many different
submodels from the original model. At test time, weight scaling and Monte Carlo
approximation are two widely applied approaches to approximate the outputs.
Both approaches work well practically when all submodels are low-bias complex
learners. However, in this work, we demonstrate scenarios where some submodels
behave closer to high-bias models and a non-uniform weight scaling is a better
approximation for inference.

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The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
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The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci