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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Y. Hezaveh

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3 papers indexed here

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Review Open access Sep 2026

Observation-driven simulations of strong-lensing galaxy clusters

Galaxy clusters are the most powerful strong lenses; they greatly magnify the flux of distant and faint sources. Strong lensing also allows for the reconstruction of their mass distribution with sim1% accuracy and enables the investigation of cosmological parameters. The number of known systems of this type is bound to...

L. Leuzzi, M. Meneghetti, A. Adam et al. · 0 citations
#machine learning Preprint Sep 2026

Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit

Tabular foundation models (TFMs) learn to fill in tables the way language models fill in text, and tables are arguably the format in which most physical measurement arrives. Did they learn any physics in the process? They are Bayesian by construction, so the question is what their prior contains. We probe it directly,...

Wassim Tenachi, Y. Hezaveh, L. P. Levasseur et al. · 0 citations
#artificial intelligence Feb 2024

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

PQMass provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models and scales well to moderately high-dimensional data and thus obviates the need for feature extraction in practical applications.

Pablo Lemos, S. Sharief, Esmeralda S. Whitammer et al. · 10 citations

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