Michele De Filippo De Grazia

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Marco ZorziProfessor of Cognition and Artificial Intelligence, University of Padova, ItalyAdresse e-mail validée de unipd.it
Alberto TestolinUniversity of PadovaAdresse e-mail validée de unipd.it
Maurizio CorbettaChair and Professor of Neurology, University of Padua, ItalyAdresse e-mail validée de unipd.it
Michele ZorziDept. of Information Engineering - University of Padova, ItalyAdresse e-mail validée de dei.unipd.it
Thiebaut de Schotten, MBrain Connectivity and Behaviour LaboratoryAdresse e-mail validée de cnrs.fr
Alessandro SalvalaggioUniversità degli Studi di PadovaAdresse e-mail validée de unipd.it
Andrea ZanellaFull Professor of Telecommunications Engineering, University of PadovaAdresse e-mail validée de dei.unipd.it
Ivilin Peev StoianovIstituto di Scienze e Tecnologie della Cognizione, CNR, ItalyAdresse e-mail validée de unipd.it
Paolo CasariAssociate Professor, University of TrentoAdresse e-mail validée de unitn.it
Violeta Sanjuan CalzadoNASA Goddard Space Flight CenterAdresse e-mail validée de nasa.gov
Roee DiamantUniversity of HaifaAdresse e-mail validée de marsci.haifa.ac.il
Dr. sucheta chauhanResearch Scholar, Jawaharlal Nehru UniversityAdresse e-mail validée de jnu.ac.in
Federico CalesellaVita-Salute San Raffaele UniversityAdresse e-mail validée de studenti.unisr.it
Daniele MunarettoAthonet, a HPE acquisitionAdresse e-mail validée de hpe.com
Veronica MontaniDepartment of Neurosciences Biomedicine & Movement Sciences, University of VeronaAdresse e-mail validée de univr.it
Zaira RomeoNeuroscience Institute, National Research Council, Padova, ItalyAdresse e-mail validée de unipd.it
Daniela D'ImperioSan Camillo IRCCS S.R.L.Adresse e-mail validée de ospedalesancamillo.net
Alessandro SperdutiUniversity of PadovaAdresse e-mail validée de math.unipd.it
Fabio AiolliUniversity of PadovaAdresse e-mail validée de math.unipd.it
Matteo LisiRoyal Holloway, University of LondonAdresse e-mail validée de rhul.ac.uk

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Michele De Filippo De Grazia

Computational Cognitive Neuroscience Lab, Department of General Psychology, University of Padova

Adresse e-mail validée de unipd.it - Page d'accueil

Machine Learning Computational Models Sensorimotor Transformations


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Post-stroke deficit prediction from lesion and indirect structural and functional disconnection A Salvalaggio, M De Filippo De Grazia, M Zorzi, M Thiebaut de Schotten, ... Brain 143 (7), 2173-2188, 2020	238	2020
Cognition-Based Networks: a New Perspective on Network Optimization Using Learning and Distributed Intelligence M Zorzi, A Zanella, A Testolin, M De Filippo De Grazia, M Zorzi IEEE, 2015	162	2015
A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images S Chauhan, L Vig, M De Filippo De Grazia, M Corbetta, S Ahmad, M Zorzi Frontiers in neuroinformatics 13, 53, 2019	94	2019
A machine learning approach to QoE-based video admission control and resource allocation in wireless systems A Testolin, M Zanforlin, MDF De Grazia, D Munaretto, A Zanella, M Zorzi, ... 2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), 31-38, 2014	63	2014
A new adaptive videogame for training attention and executive functions: design principles and initial validation V Montani, M De Filippo De Grazia, M Zorzi Frontiers in Psychology 5, 409, 2014	62	2014
On the relationship between the underwater acoustic and optical channels R Diamant, F Campagnaro, MDF De Grazia, P Casari, A Testolin, ... IEEE Transactions on Wireless Communications 16 (12), 8037-8051, 2017	49	2017
Recovery of neural dynamics criticality in personalized whole-brain models of stroke RP Rocha, L Koçillari, S Suweis, M De Filippo De Grazia, MT de Schotten, ... Nature Communications 13 (1), 3683, 2022	47	2022
Deep unsupervised learning on a desktop PC: A primer for cognitive scientists A Testolin, I Stoianov, M De Filippo De Grazia, M Zorzi Frontiers in Psychology 4, 251, 2013	43	2013
A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients F Calesella, A Testolin, M De Filippo De Grazia, M Zorzi Brain Informatics 8 (1), 8, 2021	30	2021
QoE multi-stage machine learning for dynamic video streaming MDF De Grazia, D Zucchetto, A Testolin, A Zanella, M Zorzi, M Zorzi IEEE Transactions on Cognitive Communications and Networking 4 (1), 146-161, 2017	27	2017
A novel stroke lesion network mapping approach: improved accuracy yet still low deficit prediction L Pini, A Salvalaggio, M De Filippo De Grazia, M Zorzi, ... Brain communications 3 (4), fcab259, 2021	23	2021
Sensorimotor, attentional, and neuroanatomical predictors of upper limb motor deficits and rehabilitation outcome after stroke D D’Imperio, Z Romeo, L Maistrello, E Durgoni, C Della Pietà, ... Neural plasticity 2021 (1), 8845685, 2021	21	2021
Application of the preference learning model to a human resources selection task F Aiolli, M De Filippo De Grazia, A Sperduti Computational Intelligence and Data Mining, 2009. CIDM'09. IEEE Symposium on …, 2009	21	2009
The role of architectural and learning constraints in neural network models: a case study on visual space coding A Testolin, M De Filippo De Grazia, M Zorzi Frontiers in computational neuroscience 11, 13, 2017	19	2017
A developmental approach for training deep belief networks M Zambra, A Testolin, M Zorzi Cognitive Computation 15 (1), 103-120, 2023	18	2023
Parallelization of Deep Networks M De Filippo De Grazia, I Stoianov, M Zorzi European Symposium on Artificial Neural Networks, Computational Intelligence …, 2012	18*	2012
Reply: Lesion network mapping predicts post-stroke behavioural deficits and improves localization A Salvalaggio, M De Filippo De Grazia, L Pini, M Thiebaut De Schotten, ... Brain 144 (4), e36-e36, 2021	16	2021
Reply: Lesion network mapping: where do we go from here? A Salvalaggio, L Pini, M De Filippo De Grazia, M Thiebaut De Schotten, ... Brain 144 (1), e6-e6, 2021	15	2021
Space coding for sensorimotor transformations can emerge through unsupervised learning M De Filippo De Grazia, S Cutini, M Lisi, M Zorzi Cognitive processing 13 (1), 141-146, 2012	7	2012
A comparison of shallow and deep learning methods for predicting cognitive performance of stroke patients from MRI lesion images Front S Chauhan, L Vig, M De Filippo De Grazia, M Corbetta, S Ahmad, M Zorzi Neuroinform. 0 Online 10, 2019	6	2019

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