research
∙
07/09/2023
Learning Space-Time Continuous Neural PDEs from Partially Observed States
We introduce a novel grid-independent model for learning partial differe...
share
research
∙
10/07/2022
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Training dynamic models, such as neural ODEs, on long trajectories is a ...
research
∙
05/18/2022
An importance sampling approach for reliable and efficient inference in Bayesian ordinary differential equation models
Statistical models can involve implicitly defined quantities, such as so...
research
∙
04/20/2022
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
The variational autoencoder (VAE) is a popular deep latent variable mode...
research
∙
03/02/2022
Learning Conditional Variational Autoencoders with Missing Covariates
Conditional variational autoencoders (CVAEs) are versatile deep generati...
research
∙
11/03/2021
Scalable mixed-domain Gaussian processes
Gaussian process (GP) models that combine both categorical and continuou...
research
∙
06/21/2021
Bayesian inference of ODEs with Gaussian processes
Recent machine learning advances have proposed black-box estimation of u...
research
∙
02/09/2021
Continuous-Time Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) approaches rely on discrete-ti...
research
∙
11/02/2020
Sample-efficient reinforcement learning using deep Gaussian processes
Reinforcement learning provides a framework for learning to control whic...
research
∙
06/17/2020
Longitudinal Variational Autoencoder
Longitudinal datasets measured repeatedly over time from individual subj...
research
∙
06/16/2020
Learning continuous-time PDEs from sparse data with graph neural networks
The behavior of many dynamical systems follow complex, yet still unknown...
research
∙
12/07/2019
An interpretable probabilistic machine learning method for heterogeneous longitudinal studies
Identifying risk factors from longitudinal data requires statistical too...
research
∙
09/04/2019
Latent Gaussian process with composite likelihoods for data-driven disease stratification
Data-driven techniques for identifying disease subtypes using medical re...
research
∙
05/27/2019
ODE^2VAE: Deep generative second order ODEs with Bayesian neural networks
We present Ordinary Differential Equation Variational Auto-Encoder (ODE^...
research
∙
10/09/2018
Deep learning with differential Gaussian process flows
We propose a novel deep learning paradigm of differential flows that lea...
research
∙
07/16/2018
Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching
We introduce a novel paradigm for learning non-parametric drift and diff...
research
∙
04/18/2018
Bayesian Metabolic Flux Analysis reveals intracellular flux couplings
Metabolic flux balance analyses are a standard tool in analysing metabol...
research
∙
03/12/2018
Learning unknown ODE models with Gaussian processes
In conventional ODE modelling coefficients of an equation driving the sy...
research
∙
02/08/2018
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
Proteins are commonly used by biochemical industry for numerous processe...
research
∙
08/18/2015
