25 citations · 25 across the 1 of their papers we have counts for
3 papers
Exhaustive Neural Importance Sampling applied to Monte Carlo event generation
Sebastian Pina-Otey, Federico Sánchez, Thorsten Lux +1
The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require simultaneously the description of many degrees of freedom and p…
Efficient sampling generation from explicit densities via Normalizing Flows
Sebastian Pina-Otey, Thorsten Lux, Federico Sánchez +1
For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challeng…
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows
Sebastian Pina-Otey, Federico Sánchez, Vicens Gaitan +1
In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural…