1 citations · 2 across the 11 of their papers we have counts for
3 papers · 1 filter
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2
Diffusion-based generators set the current state of the art for synthetic tabular data. These methods approach but rarely exceed real-data utility, and closing this synthetic-real…
POMONAG: Pareto-Optimal Many-Objective Neural Architecture Generator
Eugenio Lomurno, Samuele Mariani, Matteo Monti +1
Neural Architecture Search (NAS) automates neural network design, reducing dependence on human expertise. While NAS methods are computationally intensive and dataset-specific, auxi…
Stable Diffusion Dataset Generation for Downstream Classification Tasks
Eugenio Lomurno, Matteo D'Oria, Matteo Matteucci
Recent advances in generative artificial intelligence have enabled the creation of high-quality synthetic data that closely mimics real-world data. This paper explores the adaptati…