Search Results for author:
Found 3 papers, 2 papers with code
Anomaly Detection in Trajectory Data with Normalizing Flows
The task of detecting anomalous data patterns is as important in practical applications as challenging.
Learning GPLVM with arbitrary kernels using the unscented transformation
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical models.
Recurrent Gaussian Processes
We define Recurrent Gaussian Processes (RGP) models, a general family of Bayesian nonparametric models with recurrent GP priors which are able to learn dynamical patterns from sequential data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
