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Michele De Filippo De Grazia
Michele De Filippo De Grazia
Computational Cognitive Neuroscience Lab, Department of General Psychology, University of Padova
Verified email at unipd.it - Homepage
Title
Cited by
Cited by
Year
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
2282020
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
1582015
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
902019
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
622014
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
602014
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
472017
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
432013
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
412022
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
282021
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
262017
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
232021
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
212021
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
212009
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
192017
Parallelization of Deep Networks
M De Filippo De Grazia, I Stoianov, M Zorzi
European Symposium on Artificial Neural Networks, Computational Intelligence …, 2012
18*2012
A developmental approach for training deep belief networks
M Zambra, A Testolin, M Zorzi
Cognitive Computation 15 (1), 103-120, 2023
172023
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
162021
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
142021
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
72012
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
62019
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