A survey on modern trainable activation functions A Apicella, F Donnarumma, F Isgrò, R Prevete Neural Networks 138, 14-32, 2021 | 367 | 2021 |
Is it the right answer? exploiting web redundancy for answer validation B Magnini, M Negri, R Prevete, H Tanev proceedings of the 40th annual meeting of the association for computational …, 2002 | 252 | 2002 |
A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis P Arpaia, N Moccaldi, R Prevete, I Sannino, A Tedesco IEEE Transactions on Instrumentation and Measurement 69 (10), 8335-8343, 2020 | 112 | 2020 |
A WordNet-based approach to named entites recognition B Magnini, M Negri, R Prevete, H Tanev COLING-02: SEMANET: Building and Using Semantic Networks, 2002 | 82 | 2002 |
Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality A Montalto, S Stramaglia, L Faes, G Tessitore, R Prevete, D Marinazzo Neural networks 71, 159-171, 2015 | 76 | 2015 |
Mining Knowledge from Repeated Co-Occurrences: DIOGENE at TREC 2002. B Magnini, M Negri, R Prevete, H Tanev TREC, 2002 | 53 | 2002 |
Machine learning for beam dynamics studies at the CERN Large Hadron Collider P Arpaia, G Azzopardi, F Blanc, G Bregliozzi, X Buffat, L Coyle, E Fol, ... Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2021 | 52 | 2021 |
Multilingual Question/Answering: the DIOGENE System. B Magnini, M Negri, R Prevete, H Tanev TREC, 2001 | 42 | 2001 |
A simple and efficient architecture for trainable activation functions A Apicella, F Isgro, R Prevete Neurocomputing 370, 1-15, 2019 | 41 | 2019 |
Comparing statistical and content-based techniques for answer validation on the web B Magnini, M Negri, R Prevete, H Tanev Proceedings of the VIII convegno AI* IA, 11-13, 2002 | 39 | 2002 |
Virtuality in neural dynamical systems F Donnarumma, R Prevete, G Trautteur Poster presented at the International Conference on Morphological …, 2007 | 33 | 2007 |
Enhancement of SSVEPs classification in BCI-based wearable instrumentation through machine learning techniques A Apicella, P Arpaia, E De Benedetto, N Donato, L Duraccio, S Giugliano, ... IEEE Sensors Journal 22 (9), 9087-9094, 2022 | 31 | 2022 |
A programmer–interpreter neural network architecture for prefrontal cognitive control F Donnarumma, R Prevete, F Chersi, G Pezzulo International journal of neural systems 25 (06), 1550017, 2015 | 31 | 2015 |
The MAGIC-5 project: Medical applications on a grid infrastructure connection R Bellotti, S Bagnasco, U Bottigli, M Castellano, R Cataldo, E Catanzariti, ... IEEE Symposium Conference Record Nuclear Science 2004. 3, 1902-1906, 2004 | 28 | 2004 |
Evidence for sparse synergies in grasping actions R Prevete, F Donnarumma, A d’Avella, G Pezzulo Scientific reports 8 (1), 616, 2018 | 27 | 2018 |
Hierarchical and multiple hand action representation using temporal postural synergies G Tessitore, C Sinigaglia, R Prevete Experimental brain research 225, 11-36, 2013 | 27 | 2013 |
Programming in the brain: a neural network theoretical framework F Donnarumma, R Prevete, G Trautteur Connection Science 24 (2-3), 71-90, 2012 | 26 | 2012 |
Color cues for traffic scene analysis E De Micheli, R Prevete, G Piccioli, M Campani Proceedings of the Intelligent Vehicles' 95. Symposium, 466-471, 1995 | 24 | 1995 |
Conceptual design of a machine learning-based wearable soft sensor for non-invasive cardiovascular risk assessment P Arpaia, R Cuocolo, F Donnarumma, A Esposito, N Moccaldi, A Natalizio, ... Measurement 169, 108551, 2021 | 23 | 2021 |
From motor to sensory processing in mirror neuron computational modelling G Tessitore, R Prevete, E Catanzariti, G Tamburrini Biological cybernetics 103, 471-485, 2010 | 23 | 2010 |