3 citations · 8 across the 8 of their papers we have counts for
8 papers
Generative Modeling by Value-Driven Transport
Pablo Moreno-Muñoz, Adrian Müller, Gergely Neu
We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic results from control theory, we fo…
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…
Gradients of Functions of Large Matrices
Nicholas Krämer, Pablo Moreno-Muñoz, Hrittik Roy +1
Tuning scientific and probabilistic machine learning models for example, partial differential equations, Gaussian processes, or Bayesian neural networks often relies on eva…
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…
Laplacian Autoencoders for Learning Stochastic Representations
Marco Miani, Frederik Warburg, Pablo Moreno-Muñoz +2
Established methods for unsupervised representation learning such as variational autoencoders produce none or poorly calibrated uncertainty estimates making it difficult to evaluat…
Adaptive Cholesky Gaussian Processes
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz +3
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the sub…