3 papers
stat.ML2025
dynoGP: Deep Gaussian Processes for dynamic system identification
Alessio Benavoli, Dario Piga, Marco Forgione +1
In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are construc…
cs.LG2024
Enhanced Transformer architecture for in-context learning of dynamical systems
Matteo Rufolo, Dario Piga, Gabriele Maroni +1
Recently introduced by some of the authors, the in-context identification paradigm aims at estimating, offline and based on synthetic data, a meta-model that describes the behavior…
cs.RO2024
RoboMorph: In-Context Meta-Learning for Robot Dynamics Modeling
Manuel Bianchi Bazzi, Asad Ali Shahid, Christopher Agia +6
The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Nove…