143 citations · 179 across the 11 of their papers we have counts for
4 papers · 2 filters
Factorized Gaussian Process Variational Autoencoders
Metod Jazbec, Michael Pearce, Vincent Fortuin
Variational autoencoders often assume isotropic Gaussian priors and mean-field posteriors, hence do not exploit structure in scenarios where we may expect similarity or consistency…
Sparse Gaussian Process Variational Autoencoders
Matthew Ashman, Jonathan So, Will Tebbutt +3
Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generat…
Scalable Gaussian Process Variational Autoencoders
Metod Jazbec, Matthew Ashman, Vincent Fortuin +3
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs…
Practical Bayesian Optimization of Objectives with Conditioning Variables
Michael Pearce, Janis Klaise, Matthew Groves
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization. We consider the more general case where a user is faced with mul…