| |
Author |
Title |
Year |
Publication |
Volume |
Pages |
Links |
|
|
Khosa, C.K.; Sanz, V.; Soughton, M. |
A simple guide from machine learning outputs to statistical criteria in particle physics |
2022 |
Scipost Physics Core |
5 |
050 - 31pp |
|
|
|
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
2022 |
Seismological Research Letters |
93 |
2529-2542 |
|
|
|
Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. |
A deep Generative Artificial Intelligence system to predict species coexistence patterns |
2022 |
Methods in Ecology and Evolution |
13 |
1052-1061 |
|
|
|
Conde, D.; Castillo, F.L.; Escobar, C.; García, C.; Garcia Navarro, J.E.; Sanz, V.; Zaldívar, B.; Curto, J.J.; Marsal, S.; Torta, J.M. |
Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning |
2023 |
Space Weather |
21 |
e2023SW003474 - 27pp |
|
|
|
Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. |
Mapping the SMEFT to discoverable models |
2022 |
Journal of High Energy Physics |
09 |
229 - 34pp |
|
