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of 43 294
pro vyhledávání: '"A. GULLO"'
Autor:
Giuseppe Pierino
Prefazione di Aldo Tortorella Fausto Gullo appare oggi una figura pressoché sconosciuta. Ignoto ai giovani e obliato dalle generazioni più adulte è la dolente metafora di una Calabria colta, garbata, ma velata anch'essa dal pregiudizio e negletta.
Autor:
Hall, Georgia (AUTHOR) georgia.hall@crain.com
Publikováno v:
Automotive News. 7/1/2024, Vol. 98 Issue 7149, p29-29. 1p.
Autor:
Palmieri, A.
Publikováno v:
Il Foro Italiano, 2016 Dec 01. 139(12), 3771/3772-3783/3784.
Externí odkaz:
https://www.jstor.org/stable/44878962
Autor:
Settino, J., Salatino, L., Mariani, L., Channab, M., Bozzolo, L., Vallisa, S., Barillà, P., Policicchio, A., Gullo, N. Lo, Giordano, A., Mastroianni, C., Plastina, F.
Reservoir computing (RC) is an effective method for predicting chaotic systems by using a high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning phase linear, which simplifies training and reduces computational com
Externí odkaz:
http://arxiv.org/abs/2409.09886
Autor:
De Lorenzis, A., Casado, M. P., Estarellas, M. P., Gullo, N. Lo, Lux, T., Plastina, F., Riera, A., Settino, J.
Interest in quantum machine learning is increasingly growing due to its potential to offer more efficient solutions for problems that are difficult to tackle with classical methods. In this context, the research work presented here focuses on the use
Externí odkaz:
http://arxiv.org/abs/2409.00998
Akademický článek
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Autor:
Wight, Kelley Gullo (AUTHOR) kgullo@iu.edu, Liu, Peggy J. (AUTHOR), Zhou, Lingrui (AUTHOR), Fitzsimons, Gavan J. (AUTHOR)
Publikováno v:
Journal of Marketing Research (JMR). Jun2024, Vol. 61 Issue 3, p451-471. 21p.
Publikováno v:
Antike Kunst, 2024 Jan 01. 67, 81-11.
Externí odkaz:
https://www.jstor.org/stable/27332769
We introduce an implementation of Bayesian measurement error mitigation tailored for multiqubit experiments on near-term quantum devices. Our approach leverages complete information from the readout signal, which is available before any binary state
Externí odkaz:
http://arxiv.org/abs/2408.00869
Autor:
Egger, Maximilian K., Ma, Wenyue, Mottin, Davide, Karras, Panagiotis, Bordino, Ilaria, Gullo, Francesco, Anagnostopoulos, Aris
Can we assess a priori how well a knowledge graph embedding will perform on a specific downstream task and in a specific part of the knowledge graph? Knowledge graph embeddings (KGEs) represent entities (e.g., "da Vinci," "Mona Lisa") and relationshi
Externí odkaz:
http://arxiv.org/abs/2404.16572