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Found 4 papers, 1 papers with code
Variational State and Parameter Estimation
In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution.
Linearly Constrained Neural Networks
We present a novel approach to modelling and learning vector fields from physical systems using neural networks that explicitly satisfy known linear operator constraints.
Deep kernel learning for integral measurements
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions.
A Bayesian Filtering Algorithm for Gaussian Mixture Models
A Bayesian filtering algorithm is developed for a class of state-space systems that can be modelled via Gaussian mixtures.
