Idx_DeFilippo

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Publications, Michele De Filippo

A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients

Calesella, F., Testolin, A., De Filippo De Grazia, M., & Zorzi, M. (2021). A comparison of feature extraction methods for prediction of neuropsychological scores from functional connectivity data of stroke patients. Brain Informatics, 8(1), 1-13.

PDF document icon Calesella et al - Brain Informatics - 2021.pdf — PDF document, 3.33 MB (3487234 bytes)

A new adaptive videogame for training attention and executive functions: design principles and initial validation

Montani, Veronica, Michele De Filippo De Grazia, and Marco Zorzi. "A new adaptive videogame for training attention and executive functions: design principles and initial validation." Frontiers in psychology 5 (2014).

PDF document icon MDZ attention videogame Frontiers 2014.pdf — PDF document, 1.50 MB (1571044 bytes)

Cognition-based networks: a new perspective on network optimization using learning and distributed intelligence

Zorzi, M., Zanella, A., Testolin, A. , De Filippo De Grazia, M., and Zorzi, M. (2015). Cognition-based networks: a new perspective on network optimization using learning and distributed intelligence. IEEE Access, vol. 3, pg. 1512–1530.

PDF document icon Zorzi_et_al-2015.pdf — PDF document, 5.74 MB (6017614 bytes)

Deep unsupervised learning on a desktop PC: A primer for cognitive scientists

Testolin, A., Stoianov, I., De Filippo De Grazia, M., & Zorzi, M. (2013). Deep unsupervised learning on a desktop PC: A primer for cognitive scientists. Frontiers in Psychology, 4(251).

PDF document icon Testolin et al. - 2013 - Deep unsupervised learning on a desktop PC A primer for cognitive scientists.pdf — PDF document, 1.07 MB (1126074 bytes)

La gara dei numeri: un videogioco educativo per il potenziamento delle abilità numeriche e il trattamento della discalculia

Berteletti, I., De Filippo De Grazia, M., & Zorzi, M. (2012). La gara dei numeri: un videogioco educativo per il potenziamento delle abilità numeriche e il trattamento della discalculia. Difficoltà in matematica, 9(1).

PDF document icon BDZ DiM 2012 Gara dei numeri.pdf — PDF document, 604 KB (619305 bytes)

On the Relationship between the Underwater Acoustic and Optical Channels

Diamant, R., Campagnaro, F., De Grazia, M. D. F., Casari, P., Testolin, A., Calzado, V. S., & Zorzi, M. (2017). On the Relationship between the Underwater Acoustic and Optical Channels. IEEE Transactions on Wireless Communications.

PDF document icon Diamant et al 2017 - IEEE TWCOM.pdf — PDF document, 1.79 MB (1875139 bytes)

Parallelization of Deep Networks

De Filippo, M., Stoianov, I., & Zorzi, M. (2012). Parallelization of Deep Networks. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN

PDF document icon De Filippo, Stoianov, Zorzi. ESANN. 2012.pdf — PDF document, 273 KB (279624 bytes)

QoE Multi-Stage Machine Learning for Dynamic Video Streaming

De Grazia, M. D. F., Zucchetto, D., Testolin, A., Zanella, A., Zorzi, M., & Zorzi, M. (2018). QoE Multi-Stage Machine Learning for Dynamic Video Streaming. IEEE Transactions on Cognitive Communications and Networking, 4(1), 146-161.

PDF document icon 2018-IEEETransCognCommNet.pdf — PDF document, 1.50 MB (1567947 bytes)

Space coding for sensorimotor transformations can emerge through unsupervised learning

De Filippo De Grazia, M., Cutini, S., Lisi, M., & Zorzi, M. (2012). Space coding for sensorimotor transformations can emerge through unsupervised learning. Cognitive processing, 13, 141-146. doi:10.1007/s10339-012-0478-4

PDF document icon De Filippo De Grazia et al. Cognitive processing. 2012.pdf — PDF document, 727 KB (745135 bytes)

The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding

Testolin A, De Filippo De Grazia M and Zorzi M (2017) The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding. Front. Comput. Neurosci. 11:13.

PDF document icon fncom-11-00013.pdf — PDF document, 3.97 MB (4161613 bytes)