6 citations · 6 across the 3 of their papers we have counts for
3 papers
Uncertainty and Structure in Neural Ordinary Differential Equations
Katharina Ott, Michael Tiemann, Philipp Hennig
Neural ordinary differential equations (ODEs) are an emerging class of deep learning models for dynamical systems. They are particularly useful for learning an ODE vector field fro…
Bayesian Numerical Integration with Neural Networks
Katharina Ott, Michael Tiemann, Philipp Hennig +1
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the in…
ResNet After All? Neural ODEs and Their Numerical Solution
Katharina Ott, Prateek Katiyar, Philipp Hennig +1
A key appeal of the recently proposed Neural Ordinary Differential Equation (ODE) framework is that it seems to provide a continuous-time extension of discrete residual neural netw…