Stefano Forte
Introduction
My research:
Parton distributions, Perturbative QCD, Higgs and heavy quark physics, Machine Learning
My expertise is:
QCD, neural networks, bayesian methods and uncertainty estimation
A problem I’m grappling with:
Teach a machine learning model to learn the best methodology on its own and estimate its own accuracy
I’ve got my eyes on:
XAI
I want to know more about:
Interpretable autoencoders
Projects
Comprehensive uncertainties for generative models
Develop a method to include uncertainties, starting from Bayesian generative networks; expand strategies to model systematic uncertainties using conditional training on nuisance parameters; extend NNPDF methodology for architecture-driven and parameter-driven uncertainties to generative models; study the effect of guided implicit bias on amplification factors between training and generated sample size.
