3 citations · 5 across the 4 of their papers we have counts for
4 papers
Decoder ensembling for learned latent geometries
Stas Syrota, Pablo Moreno-Muñoz, Søren Hauberg
Latent space geometry provides a rigorous and empirically valuable framework for interacting with the latent variables of deep generative models. This approach reinterprets Euclide…
Riemannian Laplace approximations for Bayesian neural networks
Federico Bergamin, Pablo Moreno-Muñoz, Søren Hauberg +1
Bayesian neural networks often approximate the weight-posterior with a Gaussian distribution. However, practical posteriors are often, even locally, highly non-Gaussian, and empiri…
On Masked Pre-training and the Marginal Likelihood
Pablo Moreno-Muñoz, Pol G. Recasens, Søren Hauberg
Masked pre-training removes random input dimensions and learns a model that can predict the missing values. Empirical results indicate that this intuitive form of self-supervised l…
Revisiting Active Sets for Gaussian Process Decoders
Pablo Moreno-Muñoz, Cilie W Feldager, Søren Hauberg
Decoders built on Gaussian processes (GPs) are enticing due to the marginalisation over the non-linear function space. Such models (also known as GP-LVMs) are often expensive and n…