Idx_Testolin

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Publications, Alberto Testolin

A HMM-based Pre-training Approach for Sequential Data

Pasa, L., Testolin, A. & Sperduti, A. (2014). A HMM-based pre-training approach for sequential data. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (BE)

PDF document icon es2014-166.pdf — PDF document, 1.21 MB (1267386 bytes)

A machine learning approach to QoE-based video admission control and resource allocation in wireless systems

Testolin, A., Zanforlin, M., De Filippo De Grazia, M., Munaretto, D., Zanella, A., Zorzi, M. & Zorzi, M. (2014). A machine learning approach to QoE-based video admission control and resource allocation in wireless systems. IEEE IFIP Annual Mediterranean Ad Hoc Networking Workshop, Piran (SL)

PDF document icon 2014 - Testolin et al. - IEEE IFIP Annual Mediterranean Ad Hoc Networking Workshop.pdf — PDF document, 801 KB (820669 bytes)

An emergentist perspective on the origin of number sense

Zorzi, M., & Testolin, A. (2018). An emergentist perspective on the origin of number sense. Phil. Trans. R. Soc. B, 373(1740), 20170043.

PDF document icon Zorzi, Testolin - 2018 - PTRS-B.pdf — PDF document, 918 KB (940317 bytes)

Assessment of Sequential Boltzmann Machines on a Lexical Processing Task

Testolin, A., Sperduti, A., Stoianov, I., & Zorzi, M. (2012). Assessment of Sequential Boltzmann Machines on a Lexical Processing Task. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN.

PDF document icon Testolin et al. ESANN. 2012.pdf — PDF document, 198 KB (203477 bytes)

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)

Cognition-based networks: applying cognitive science to wireless networking

Badia, L., Munaretto, D., Testolin, A., Zanella, A., Zorzi, M. & Zorzi, M. (2014). Cognition-based networks: applying cognitive science to wireless networking. IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, Sydney (AUS)

PDF document icon 2014 - Badia et al. - IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.pdf — PDF document, 308 KB (315574 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)

Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets

Testolin, A., & Diamant, R. (2020). Combining Denoising Autoencoders and Dynamic Programming for Acoustic Detection and Tracking of Underwater Moving Targets. Sensors, 20(10), 2945.

PDF document icon TestolinDiamant-Sensors-2020.pdf — PDF document, 8.96 MB (9396271 bytes)

Deep learning systems as complex networks

Testolin, A., Piccolini, M., & Suweis, S. (2019). Deep learning systems as complex networks. Journal of Complex Networks.

PDF document icon Testolin.et.al.2019-JCompNet.pdf — PDF document, 1.17 MB (1223495 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)

Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies

Testolin, A., & McClelland, J. L. (2020). Do estimates of numerosity really adhere to Weber’s law? A reexamination of two case studies. Psychonomic Bulletin & Review, 1-11.

PDF document icon Testolin_McClelland-2020-Psychonomic_Bulletin_&_Review.pdf — PDF document, 1.38 MB (1443375 bytes)

Emergence of Network Motifs in Deep Neural Networks

Zambra, M., Maritan, A., & Testolin, A. (2020). Emergence of Network Motifs in Deep Neural Networks. Entropy, 22(2), 204.

PDF document icon Zambra.et.al.-Entropy-2020.pdf — PDF document, 6.49 MB (6801416 bytes)

Learning orthographic structure with generative neural networks

Testolin, A., Stoianov, I., Sperduti, A., and Zorzi, M. (2016). Learning orthographic structure with generative neural networks. Cognitive Science.

PDF document icon Testolin_et_al-2015-Cognitive_Science.pdf — PDF document, 638 KB (653712 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)

Modeling language and cognition with deep unsupervised learning: a tutorial overview.

Zorzi, M., Testolin, A., & Stoianov, I. (2013). Modeling language and cognition with deep unsupervised learning: a tutorial overview. Frontiers in Psychology, 4 (515)

PDF document icon fpsyg-04-00515.pdf — PDF document, 2.62 MB (2745262 bytes)

Neural Networks for Sequential Data: a Pre‐training Approach based on Hidden Markov Models

Pasa, Luca, Alberto Testolin, and Alessandro Sperduti. "Neural Networks for Sequential Data: a Pre-training Approach based on Hidden Markov Models." Neurocomputing (2015).

PDF document icon 1-s2.0-S0925231215003689-main.pdf — PDF document, 888 KB (910322 bytes)

Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics

Testolin, A., Zou, W. Y., & McClelland, J. L. (2020). Numerosity discrimination in deep neural networks: Initial competence, developmental refinement and experience statistics. Developmental Science

PDF document icon Testolin.et.al.-DevSci-2020.pdf — PDF document, 1.40 MB (1468640 bytes)

Numerosity Representation in InfoGAN: An Empirical Study

Zanetti, A., Testolin, A., Zorzi, M., & Wawrzynski, P. (2019). Numerosity Representation in InfoGAN: An Empirical Study. In International Work-Conference on Artificial Neural Networks

PDF document icon Zanetti.et.al.2019-IWANN.pdf — PDF document, 2.37 MB (2485610 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)