Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple Baseline for Bayesian Uncertainty in Deep Learning   We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose
approach for uncertainty representation and calibration in deep learning.
Stochastic Weight Averaging (SWA), which computes the first moment of
stochastic gradient descent (SGD) iterates with a modified learning rate
schedule, has recently been shown to improve generalization in deep learning.
With SWAG, we fit a Gaussian using the SWA solution as the first moment and a
low rank plus diagonal covariance also derived from the SGD iterates, forming
an approximate posterior distribution over neural network weights; we then
sample from this Gaussian distribution to perform Bayesian model averaging. We
empirically find that SWAG approximates the shape of the true posterior, in
accordance with results describing the stationary distribution of SGD iterates.
Moreover, we demonstrate that SWAG performs well on a wide variety of tasks,
including out of sample detection, calibration, and transfer learning, in
comparison to many popular alternatives including MC dropout, KFAC Laplace,
SGLD, and temperature scaling.

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The categories of this paper are: hep-th
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The categories of this paper are: econ
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom