271 citations · 597 across the 16 of their papers we have counts for
19 papers
An Analytical Theory of Auxiliary Learning
Federico Milanesio, Alessandro Ingrosso, Matteo Osella
Auxiliary learning is an optimization paradigm in which a neural network's performance on a target task is improved by jointly training it on additional tasks. However, the mechani…
Alternative routes to universal diversity scaling in component systems: from proteomes to large language models
Andrea Mazzolini, Leonardo Agasso, Filippo Valle +3
Remarkably common statistical laws characterize the diversity scaling and its fluctuations across a wide range of complex "component systems". These regularities are often interpre…
Component systems: do null models explain everything?
Andrea Mazzolini, Mattia Corigliano, Rossana Droghetti +2
Component systems - ensembles of realizations built from a shared repertoire of modular parts - are ubiquitous in biological, ecological, technological, and socio-cultural domains.…
The Advantage of Fine-Grained Training
Davide Pirovano, Federico Milanesio, Michele Caselle +2
In classification problems, models are trained to predict a class label based on the input data features. However, class labels are organized hierarchically in many datasets. While…
Ranking nodes in bipartite systems with a non-linear iterative map
Andrea Mazzolini, Michele Caselle, Matteo Osella
Ranking nodes in networks according to a defined measure of importance is an extensively studied task, with applications in ecology, economic trade networks, and social networks. T…
The effect of a linear feedback mechanism in a homeostasis model
Antonio Francesco Zirattu, Marta Biondo, Matteo Osella +1
Feedback loops are essential for regulating cell proliferation and maintaining the delicate balance between cell division and cell death. Thanks to the exact solution of a few simp…