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During several decades, neural network architectures have undergone considerable evolution: from shallow to deep, from fully connected to structured (e.g. convolutional, recurrent, or residual), from unnormalized to normalized. The present thesis contribut ...
Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...
Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...
