4 papers · 1 filter
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…
Explicit Density Approximation for Neural Implicit Samplers Using a Bernstein-Based Convex Divergence
José Manuel de Frutos, Manuel A. Vázquez, Pablo M. Olmos +1
Rank-based statistical metrics, such as the invariant statistical loss (ISL), have recently emerged as robust and practically effective tools for training implicit generative model…
Robust training of implicit generative models for multivariate and heavy-tailed distributions with an invariant statistical loss
José Manuel de Frutos, Manuel A. Vázquez, Pablo Olmos +1
Traditional implicit generative models are capable of learning highly complex data distributions. However, their training involves distinguishing real data from synthetically gener…
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks
Mikkel Jordahn, Pablo M. Olmos
Deep Neural Networks (DNN) have shown great promise in many classification applications, yet are widely known to have poorly calibrated predictions when they are over-parametrized.…