3 papers
cs.LG2026
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…
cs.LG2024
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…
stat.ML2024
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…