Marco Zanetti
6 papers 66 citations h-index 4 2019–2025
Learning new physics efficiently with nonparametric methods
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric...
Fast kernel methods for Data Quality Monitoring as a goodness-of-fit test
We here propose a machine learning approach for monitoring particle detectors in real-time. The goal is to assess the compatibility of incoming experimental data with a reference dataset,...
Machine Learning Pipelines with Modern Big Data Tools for High Energy Physics
The effective utilization at scale of complex machine learning (ML) techniques for HEP use cases poses several technological challenges, most importantly on the actual implementation of dedicated...
Using Big Data Technologies for HEP Analysis
The HEP community is approaching an era were the excellent performances of the particle accelerators in delivering collision at high rate will force the experiments to record a large amount of...
Learning to Validate Generative Models: a Goodness-of-Fit Approach
Generative models are increasingly central to scientific workflows, yet their systematic use and interpretation require a proper understanding of their limitations through rigorous validation....
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks,...