Michele De Filippo De Grazia

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

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Michele De Filippo De Grazia

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

Verificeret mail på unipd.it - Startside

Machine Learning Computational Models Sensorimotor Transformations


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
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	46	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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