SECCIA, RUGGIERO
SECCIA, RUGGIERO
DIPARTIMENTO DI INGEGNERIA INFORMATICA, AUTOMATICA E GESTIONALE -ANTONIO RUBERTI-
A Gray-Box Approach for Curriculum Learning
2019 Foglino, Francesco; Leonetti, Matteo; Sagratella, Simone; Seccia, Ruggiero
A machine-learning-based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease
2021 Raparelli, V; Proietti, M; Romiti, G. F.; Seccia, R; Di Teodoro, G.; Tanzilli, G; Marrapodi, R; Flego, D; Corica, B; Cangemi, R; Palagi, L; Basili, S; Stefanini, L; Group, Eva
A novel optimization perspective to the problem of designing sequences of tasks in a reinforcement learning framework
2022 Seccia, R.; Foglino, F.; Leonetti, M.; Sagratella, S.
Block layer decomposition schemes for training deep neural networks
2020 Palagi, L.; Seccia, R.
Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis
2020 Seccia, R.; Gammelli, D.; Dominici, F.; Romano, S.; Landi, A. C.; Salvetti, M.; Tacchella, A.; Zaccaria, A.; Crisanti, A.; Grassi, F.; Palagi, L.
Data of patients undergoing rehabilitation programs
2020 Seccia, Ruggiero; Boresta, Marco; Fusco, Federico; Tronci, Edoardo; Di Gemma, Emanuele; Palagi, Laura; Mangone, Massimiliano; Agostini, Francesco; Bernetti, Andrea; Santilli, Valter; Damiani, Carlo; Goffredo, Michela; Franceschini, Marco
Machine learning use for prognostic purposes in multiple sclerosis
2021 Seccia, Ruggiero; Romano, Silvia; Salvetti, Marco; Crisanti, Andrea; Palagi, Laura; Grassi, Francesca
On the convergence of a Block-Coordinate Incremental Gradient method
2021 Palagi, Laura; Seccia, Ruggiero
ONLINE BLOCK LAYER DECOMPOSITION SCHEMES FOR TRAINING DEEP NEURAL NETWORKS
2019 Palagi, Laura; Seccia, Ruggiero
| Titolo | Data di pubblicazione | Autore(i) | File |
|---|---|---|---|
| A Gray-Box Approach for Curriculum Learning | 2019 | Foglino, Francesco; Leonetti, Matteo; Sagratella, Simone; Seccia, Ruggiero | |
| A machine-learning-based bio-psycho-social model for the prediction of non-obstructive and obstructive coronary artery disease | 2021 | Raparelli, V; Proietti, M; Romiti, G. F.; Seccia, R; Di Teodoro, G.; Tanzilli, G; Marrapodi, R; Flego, D; Corica, B; Cangemi, R; Palagi, L; Basili, S; Stefanini, L; Group, Eva | |
| A novel optimization perspective to the problem of designing sequences of tasks in a reinforcement learning framework | 2022 | Seccia, R.; Foglino, F.; Leonetti, M.; Sagratella, S. | |
| Block layer decomposition schemes for training deep neural networks | 2020 | Palagi, L.; Seccia, R. | |
| Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis | 2020 | Seccia, R.; Gammelli, D.; Dominici, F.; Romano, S.; Landi, A. C.; Salvetti, M.; Tacchella, A.; Zaccaria, A.; Crisanti, A.; Grassi, F.; Palagi, L. | |
| Data of patients undergoing rehabilitation programs | 2020 | Seccia, Ruggiero; Boresta, Marco; Fusco, Federico; Tronci, Edoardo; Di Gemma, Emanuele; Palagi, Laura; Mangone, Massimiliano; Agostini, Francesco; Bernetti, Andrea; Santilli, Valter; Damiani, Carlo; Goffredo, Michela; Franceschini, Marco | |
| Machine learning use for prognostic purposes in multiple sclerosis | 2021 | Seccia, Ruggiero; Romano, Silvia; Salvetti, Marco; Crisanti, Andrea; Palagi, Laura; Grassi, Francesca | |
| On the convergence of a Block-Coordinate Incremental Gradient method | 2021 | Palagi, Laura; Seccia, Ruggiero | |
| ONLINE BLOCK LAYER DECOMPOSITION SCHEMES FOR TRAINING DEEP NEURAL NETWORKS | 2019 | Palagi, Laura; Seccia, Ruggiero |