4 papers · 1 filter
Separation Power of Equivariant Neural Networks
Marco Pacini, Xiaowen Dong, Bruno Lepri +1
The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy for its expressivity. Indeed, knowing th…
On Universality Classes of Equivariant Networks
Marco Pacini, Gabriele Santin, Bruno Lepri +1
Equivariant neural networks provide a principled framework for incorporating symmetry into learning architectures and have been extensively analyzed through the lens of their separ…
TS-Mob: Social and Geographical-Aware Time Series Foundation-Model Framework for Human Mobility Prediction
Massimiliano Luca, Ciro Beneduce, Bruno Lepri
Short-term forecasting of aggregated human mobility flows supports urban planning, intelligent transportation systems, and emergency response, yet existing models often require sub…
SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
Mátyás Vincze, Laura Ferrarotti, Leonardo Lucio Custode +2
Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box po…