Search Results for author:
Found 5 papers, 4 papers with code
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work in practice.
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a latent second order ODE model for high-dimensional sequential data.
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE).
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
The results support our theory and show that the proposed algorithm provides a significant speedup over the recently proposed synchronous distributed L-BFGS algorithm.
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated.
