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cs.LG2024
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…
cs.LG2024
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.…