Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantification, and robustness assessment are less systematically addressed. The VERaiPHY initiative (Validation&Evaluation for Robust AI in PHYsics...
Gaia Grosso, Ramon Winterhalder, Lydia Brenner et al.· 2 citations
Machine-learning-based anomaly detection (AD) offers a promising, model-agnostic alternative to traditional LHC analyses, allowing to search for many signals at once. Recent advances in representation learning motivate the use of neural embeddings to map high-dimensional physics observables into low-dimensional latent...
Shelley Tong, Philip Harris, Gaia Grosso· 1 citation
This work introduces SparKer, a sparse ensemble of Gaussian kernels trained in a semi-supervised Neyman-Pearson framework to locally model the likelihood ratio between a sample that may contain anomalies and an anomaly-free reference, and provides theoretical insights into the mechanisms driving detection and self-orga...
Gaia Grosso, Sai Sumedh R. Hindupur, Thomas Fel et al.· arXiv.org· 2 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
