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)

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)

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)

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)

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)

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)

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)