Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sampling-free Uncertainty Estimation in Gated Recurrent Units with
  Exponential Families   There has recently been a concerted effort to derive mechanisms in vision and
machine learning systems to offer uncertainty estimates of the predictions they
make. Clearly, there are enormous benefits to a system that is not only
accurate but also has a sense for when it is not sure. Existing proposals
center around Bayesian interpretations of modern deep architectures -- these
are effective but can often be computationally demanding. We show how classical
ideas in the literature on exponential families on probabilistic networks
provide an excellent starting point to derive uncertainty estimates in Gated
Recurrent Units (GRU). Our proposal directly quantifies uncertainty
deterministically, without the need for costly sampling-based estimation. We
demonstrate how our model can be used to quantitatively and qualitatively
measure uncertainty in unsupervised image sequence prediction. To our
knowledge, this is the first result describing sampling-free uncertainty
estimation for powerful sequential models such as GRUs.

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The categories of this paper are: mtrl-th
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The categories of this paper are: physics
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The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: supr-con
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: quant-ph