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Found 4 papers, 2 papers with code
Uncertainty and Structure in Neural Ordinary Differential Equations
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.
Bayesian Numerical Integration with Neural Networks
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.
ResNet After All: Neural ODEs and Their Numerical Solution
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.
ResNet After All? Neural ODEs and Their Numerical Solution
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.
