Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robustly representing uncertainty in deep neural networks through
  sampling   As deep neural networks (DNNs) are applied to increasingly challenging
problems, they will need to be able to represent their own uncertainty.
Modeling uncertainty is one of the key features of Bayesian methods. Using
Bernoulli dropout with sampling at prediction time has recently been proposed
as an efficient and well performing variational inference method for DNNs.
However, sampling from other multiplicative noise based variational
distributions has not been investigated in depth. We evaluated Bayesian DNNs
trained with Bernoulli or Gaussian multiplicative masking of either the units
(dropout) or the weights (dropconnect). We tested the calibration of the
probabilistic predictions of Bayesian convolutional neural networks (CNNs) on
MNIST and CIFAR-10. Sampling at prediction time increased the calibration of
the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or
Bernoulli, led to more robust representation of uncertainty compared to
sampling of units. However, using either Gaussian or Bernoulli dropout led to
increased test set classification accuracy. Based on these findings we used
both Bernoulli dropout and Gaussian dropconnect concurrently, which we show
approximates the use of a spike-and-slab variational distribution without
increasing the number of learned parameters. We found that spike-and-slab
sampling had higher test set performance than Gaussian dropconnect and more
robustly represented its uncertainty compared to Bernoulli dropout.

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The categories of this paper are: ao-sci
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The categories of this paper are: comp-gas
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: nlin
2
The categories of this paper are: math
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The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph