Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Baldo, Federico"'
In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena. Most notably, Physics-informed Neural Net
Externí odkaz:
http://arxiv.org/abs/2306.10335
Autor:
Baldo, Federico, Dall'Olio, Lorenzo, Ceccarelli, Mattia, Scheda, Riccardo, Lombardi, Michele, Borghesi, Andrea, Diciotti, Stefano, Milano, Michela
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes. In this paper, we will outline the main Deep Learning appro
Externí odkaz:
http://arxiv.org/abs/2103.02346
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge. We tackle the issue of finding the right balance between the loss (the accuracy of the learner) and
Externí odkaz:
http://arxiv.org/abs/2005.10674
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets. However, purely data-driven models might struggle when very difficult functions need to
Externí odkaz:
http://arxiv.org/abs/2005.10691
Publikováno v:
Nicosia G. et al. (eds) Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science, vol 12565. Springer, Cham
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets. Nevertheless, there are learning problems that cannot be easily solved relying on pure data, e.g. sc
Externí odkaz:
http://arxiv.org/abs/2002.10214
Autor:
Fioretto, Ferdinando, Van Hentenryck, Pascal, Mak, Terrence WK, Tran, Cuong, Baldo, Federico, Lombardi, Michele
This paper explores the potential of Lagrangian duality for learning applications that feature complex constraints. Such constraints arise in many science and engineering domains, where the task amounts to learning optimization problems which must be
Externí odkaz:
http://arxiv.org/abs/2001.09394
Autor:
Baldo, Federico, Piovesan, Allison, Rakvin, Marijana, Ramacieri, Giuseppe, Locatelli, Chiara, Lanfranchi, Silvia, Onnivello, Sara, Pulina, Francesca, Caracausi, Maria, Antonaros, Francesca, Lombardi, Michele, Pelleri, Maria Chiara
Publikováno v:
In Heliyon September 2023 9(9)
Autor:
Yanchen Chen
Publikováno v:
E-Prints Complutense. Archivo Institucional de la UCM
instname
Yanchen Chen
instname
Yanchen Chen
Objetivo Evaluar la capacidad diagnóstica de los más recientes softwares de diagnóstico incluidos en los tomógrafos de coherencia óptica de dominio espectral Cirrus y Spectralis. Métodos Estudio transversal en 109 ojos de 109 pacientes consecut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c4ac54ee18c1c4ce6f210a89a009c8de
https://eprints.ucm.es/id/eprint/72169/
https://eprints.ucm.es/id/eprint/72169/
Publikováno v:
Trustworthy AI-Integrating Learning, Optimization and Reasoning ISBN: 9783030739584
TAILOR
TAILOR
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introducedto improve a predictive model via expert knowledge. Given the recent interest in ethical and trustworthy AI, however, several works are resorting to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4022c40c16a15d18c49fff64410fcce1