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SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
[article]
2020
arXiv
pre-print
Uncertainty quantification is a fundamental yet unsolved problem for deep learning. The Bayesian framework provides a principled way of uncertainty estimation but is often not scalable to modern deep neural nets (DNNs) that have a large number of parameters. Non-Bayesian methods are simple to implement but often conflate different sources of uncertainties and require huge computing resources. We propose a new method for quantifying uncertainties of DNNs from a dynamical system perspective. The
arXiv:2008.10546v1
fatcat:2tffhdk3ovaoxf4ybbx42pjykq