26 citations · 95 across the 9 of their papers we have counts for
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cs.LG2022★ 6 cited
Theoretical characterization of uncertainty in high-dimensional linear classification
Lucas Clarté, Bruno Loureiro, Florent Krzakala +1
Being able to reliably assess not only the \emph{accuracy} but also the \emph{uncertainty} of models' predictions is an important endeavour in modern machine learning. Even if the…
cs.LG2021★ 22 cited
Probing transfer learning with a model of synthetic correlated datasets
Federica Gerace, Luca Saglietti, Stefano Sarao Mannelli +2
Transfer learning can significantly improve the sample efficiency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source tas…