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Found 8 papers, 6 papers with code
GPflux: A Library for Deep Gaussian Processes
GPflux is compatible with and built on top of the Keras deep learning eco-system.
Stochastic Differential Equations with Variational Wishart Diffusions
We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time dynamical modelling.
Deep Gaussian Processes with Importance-Weighted Variational Inference
Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings.
Gaussian Process Conditional Density Estimation
Conditional Density Estimation (CDE) models deal with estimating conditional distributions.
Orthogonally Decoupled Variational Gaussian Processes
It adopts an orthogonal basis in the mean function to model the residues that cannot be learned by the standard coupled approach.
Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models
The natural gradient method has been used effectively in conjugate Gaussian process models, but the non-conjugate case has been largely unexplored.
Doubly Stochastic Variational Inference for Deep Gaussian Processes
Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders
We study a variant of the variational autoencoder model (VAE) with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models.
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