3 papers
cond-mat.soft2026
Learning to flock in open space by avoiding collisions and staying together
Martino Brambati, Antonio Celani, Marco Gherardi +1
We investigate the emergence of cohesive flocking in open, boundless space using a multi-agent reinforcement learning framework. Agents integrate positional and orientational infor…
cond-mat.dis-nn2025
Microscopic and collective signatures of feature learning in neural networks
Andrea Corti, Rosalba Pacelli, Pietro Rotondo +1
Feature extraction - the ability to identify relevant properties of data - is a key factor underlying the success of deep learning. Yet, it has proved difficult to elucidate its na…
stat.ML2025
Feature learning in finite-width Bayesian deep linear networks with multiple outputs and convolutional layers
Federico Bassetti, Marco Gherardi, Alessandro Ingrosso +2
Deep linear networks have been extensively studied, as they provide simplified models of deep learning. However, little is known in the case of finite-width architectures with mult…