74 citations · 370 across the 44 of their papers we have counts for
4 papers · 1 filter
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…
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Jonas Rothfuss, Vincent Fortuin, Martin Josifoski +1
Meta-learning can successfully acquire useful inductive biases from data. Yet, its generalization properties to unseen learning tasks are poorly understood. Particularly if the num…