Idx_Testolin
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Publications, Alberto Testolin
Learning Numerosity Representations with Transformers: Number Generation Tasks and Out-of-Distribution Generalization
Boccato et al. Entropy 2021.pdf — PDF document, 1.59 MB (1666161 bytes)
Learning orthographic structure with generative neural networks
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&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, Stoianov, Zorzi - 2017 - NHB.pdf — PDF document, 4.51 MB (4733884 bytes)
Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients
Shafiezadeh et al. - Applied Sciences - 2023.pdf — PDF document, 2.36 MB (2477732 bytes)
Modeling language and cognition with deep unsupervised learning: a tutorial overview.
fpsyg-04-00515.pdf — PDF document, 2.62 MB (2745262 bytes)
Neural Networks for Sequential Data: a Preātraining Approach based on Hidden Markov Models
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.et.al.-DevSci-2020.pdf — PDF document, 1.40 MB (1468640 bytes)
Numerosity Representation in InfoGAN: An Empirical Study
Zanetti.et.al.2019-IWANN.pdf — PDF document, 2.37 MB (2485610 bytes)
On the Relationship between the Underwater Acoustic and Optical Channels
Diamant et al 2017 - IEEE TWCOM.pdf — PDF document, 1.79 MB (1875139 bytes)
Poor numerical performance of guppies tested in a Skinner box
Gatto_et_al-2020-Scientific_Reports.pdf — PDF document, 1.25 MB (1311924 bytes)
Probabilistic Models and Generative Neural Networks: Towards an Unified Framework for Modeling Normal and Impaired Neurocognitive Functions
Testolin and Zorzi (2016) Perspective.pdf — PDF document, 1.04 MB (1087183 bytes)
QoE Multi-Stage Machine Learning for Dynamic Video Streaming
2018-IEEETransCognCommNet.pdf — PDF document, 1.50 MB (1567947 bytes)
The Challenge of Modeling the Acquisition of Mathematical Concepts
Testolin - FrontHumNeurosc 2020.pdf — PDF document, 2.11 MB (2215396 bytes)
The Role of Architectural and Learning Constraints in Neural Network Models: A Case Study on Visual Space Coding
fncom-11-00013.pdf — PDF document, 3.97 MB (4161613 bytes)
Visual sense of number vs. sense of magnitude in humans and machines
Testolin_et_al-2020-Scientific_Reports.pdf — PDF document, 3.70 MB (3876567 bytes)