Idx_2017

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2017

Bilingualism advantage in handwritten character recognition: A deep learning investigation on Persian and Latin scripts

Sadeghi, Z. Testolin, A. and Zorzi, M. (2017). Bilingualism advantage in handwritten character recognition: A deep learning investigation on Persian and Latin scripts. 7th International Conference on Computer and Knowledge Engineering (ICCKE).

PDF document icon SadeghiTestolinZorzi_ICCKE_final.pdf — PDF document, 394 KB (403970 bytes)

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning

Sadeghi, Z., and Testolin, A. (2017). Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning. Cognitive Processing.

PDF document icon Sadeghi&Testolin-2017-CogProc.pdf — PDF document, 1.02 MB (1066889 bytes)

Letter perception emerges from unsupervised deep learning and recycling of natural image features

Testolin, A., Stoianov, I., & Zorzi, M. (2017). Letter perception emerges from unsupervised deep learning and recycling of natural image features. Nature Human Behaviour, 1(9), 657.

PDF document icon Testolin, Stoianov, Zorzi - 2017 - NHB.pdf — PDF document, 4.51 MB (4733884 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)

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)