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cs.LG2026
Variational meta-learning inference for low dimensional neural system identification
Matteo Rufolo, Dario Piga, Marco Forgione
Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack relia…
cs.LG2026
Diffusion Sequence Models for Generative In-Context Meta-Learning of Robot Dynamics
Angelo Moroncelli, Matteo Rufolo, Gunes Cagin Aydin +2
Accurate modeling of robot dynamics is essential for model-based control, yet remains challenging under distributional shifts and real-time constraints. In this work, we formulate…