Christophe Desceliers

Uncertainty Quantification in Neural Networks for Latent-Driven Non-Gaussian Outputs

Uncertainty quantification (UQ) is essential for reliable predictions in artificial neural networks when latent sources of randomness produce non-Gaussian outputs for a given deterministic input. We propose UQ-ANN, a data-driven framework composed of two coupled subnetworks. The...