no code implementations • 22 May 2023 • Katharina Ott, Michael Tiemann, Philipp Hennig
As a first contribution, we show that basic and lightweight Bayesian deep learning techniques like the Laplace approximation can be applied to neural ODEs to yield structured and meaningful uncertainty quantification.
1 code implementation • 22 May 2023 • Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the integrand, and can quantify uncertainty over estimates of an integral.
no code implementations • ICLR 2021 • Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann
If the trained model is supposed to be a flow generated from an ODE, it should be possible to choose another numerical solver with equal or smaller numerical error without loss of performance.
1 code implementation • 30 Jul 2020 • Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann
If the trained model is supposed to be a flow generated from an ODE, it should be possible to choose another numerical solver with equal or smaller numerical error without loss of performance.