Zobrazeno 1 - 10
of 3 883
pro vyhledávání: '"A. FORGIONE"'
Autor:
A. Arrighetti, B. Fanini, D. Ferdani, A. Forgione, A. Lumini, R. Manganelli Del Fà, S. Pescarin, M. Repole
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-2-W4-2024, Pp 17-24 (2024)
The contribution aims to present the results obtained from the archaeological analysis of the architecture of the Cathedral of San Massimo in Forcona (AQ), which took place in 2021, focusing on the digital systems used for the documentation and valor
Externí odkaz:
https://doaj.org/article/641e4865f595459d9ef60ac0fc022437
Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as intended? With the rise of in-context learning and powerful architect
Externí odkaz:
http://arxiv.org/abs/2411.06482
Autor:
Moroncelli, Angelo, Soni, Vishal, Shahid, Asad Ali, Maccarini, Marco, Forgione, Marco, Piga, Dario, Spahiu, Blerina, Roveda, Loris
Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled datasets, exhibit powerful capabilities in understanding complex patterns and generating sophisticated outputs. However, they often struggle to adapt to specific tasks
Externí odkaz:
http://arxiv.org/abs/2410.16411
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 of a whole class of systems. Once trained, this meta-model is fed wit
Externí odkaz:
http://arxiv.org/abs/2410.03291
Autor:
Bazzi, Manuel Bianchi, Shahid, Asad Ali, Agia, Christopher, Alora, John, Forgione, Marco, Piga, Dario, Braghin, Francesco, Pavone, Marco, Roveda, Loris
The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Novel avenues for physical applications, such as solving Partial Different
Externí odkaz:
http://arxiv.org/abs/2409.11815
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently introduced deep structured state-space models (SSM), which feature linear d
Externí odkaz:
http://arxiv.org/abs/2403.14833
This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing model generalization and robustness in scenarios characterized by data sca
Externí odkaz:
http://arxiv.org/abs/2403.05164
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems. This paradigm facilitates the leveraging of knowledge acquired from observing the beh
Externí odkaz:
http://arxiv.org/abs/2312.04083
State estimation has a pivotal role in several applications, including but not limited to advanced control design. Especially when dealing with nonlinear systems state estimation is a nontrivial task, often entailing approximations and challenging fi
Externí odkaz:
http://arxiv.org/abs/2312.04509
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same class? This central question drives the study presented in this paper. In
Externí odkaz:
http://arxiv.org/abs/2308.13380