no code implementations • 14 Dec 2020 • Jarrad Courts, Johannes Hendriks, Adrian Wills, Thomas Schön, Brett Ninness
In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution.
1 code implementation • 5 Feb 2020 • Johannes Hendriks, Carl Jidling, Adrian Wills, Thomas Schön
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints.
no code implementations • 4 Sep 2019 • Carl Jidling, Johannes Hendriks, Thomas B. Schön, Adrian Wills
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions.
no code implementations • 16 May 2017 • Adrian G. Wills, Johannes Hendriks, Christopher Renton, Brett Ninness
A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures.