Search Results for author: Michael Tiemann

Found 7 papers, 2 papers with code

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems

no code implementations ICML 2020 Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

To address this shortcoming, we employ Gaussian ODE filtering (a probabilistic numerical method for ODEs) to construct a local Gaussian approximation to the likelihood.

ResNet After All? Neural ODEs and Their Numerical Solution

1 code implementation30 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.

valid

ResNet After All: Neural ODEs and Their Numerical Solution

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.

valid

Structure-preserving Gaussian Process Dynamics

no code implementations2 Feb 2021 Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, Sebastian Trimpe

On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.

Variational Inference

Combining Slow and Fast: Complementary Filtering for Dynamics Learning

no code implementations27 Feb 2023 Katharina Ensinger, Sebastian Ziesche, Barbara Rakitsch, Michael Tiemann, Sebastian Trimpe

This filtering technique combines two signals by applying a high-pass filter to one signal, and low-pass filtering the other.

Sensor Fusion

Uncertainty and Structure in Neural Ordinary Differential Equations

no code implementations22 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.

Uncertainty Quantification

Bayesian Numerical Integration with Neural Networks

1 code implementation22 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.

Numerical Integration

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