6 citations · 7 across the 2 of their papers we have counts for
2 papers
cs.LG2022★ 6 cited
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique
Andrea Falanti, Eugenio Lomurno, Stefano Samele +2
Automating the research for the best neural network model is a task that has gained more and more relevance in the last few years. In this context, Neural Architecture Search (NAS)…
cs.LG2022★ 1 cited
On the utility and protection of optimization with differential privacy and classic regularization techniques
Eugenio Lomurno, Matteo matteucci
Nowadays, owners and developers of deep learning models must consider stringent privacy-preservation rules of their training data, usually crowd-sourced and retaining sensitive inf…