23 citations · 28 across the 7 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
Variational Mixture of HyperGenerators for Learning Distributions Over Functions
Batuhan Koyuncu, Pablo Sanchez-Martin, Ignacio Peis +2
Recent approaches build on implicit neural representations (INRs) to propose generative models over function spaces. However, they are computationally costly when dealing with infe…
Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo
Ignacio Peis, Chao Ma, José Miguel Hernández-Lobato
Variational Autoencoders (VAEs) have recently been highly successful at imputing and acquiring heterogeneous missing data. However, within this specific application domain, existin…
Unsupervised Learning of Global Factors in Deep Generative Models
Ignacio Peis, Pablo M. Olmos, Antonio Artés-Rodríguez
We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contr…
Deep Sequential Models for Suicidal Ideation from Multiple Source Data
Ignacio Peis, Pablo M. Olmos, Constanza Vera-Varela +4
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential mode…