3 papers
cs.LG2026
Latent Diffusion for Missing Data
Alberte Heering Estad, Ignacio Peis, Jes Frellsen
Diffusion models have emerged as powerful generative approaches for missing-data imputation, yet most existing methods operate directly in data space and degrade when training data…
cs.LG2026
Order-Agnostic Autoregressive Modelling with Missing Data
Ignacio Peis, Pablo M. Olmos, Jes Frellsen
Order-Agnostic autoregressive models have demonstrated strong performance in deep generative modeling, yet their use in settings with incomplete data remains largely unexplored. In…
cs.LG2026
Scalable physical source-to-field inference with hypernetworks
Berian James, Stefan Pollok, Ignacio Peis +3
We present a generative model that amortises computation for the field and potential around e.g.~gravitational or electromagnetic sources. Exact numerical calculation has either co…