Andrea Zugarini (DIISM, Universities of Florence and Siena) Oct 18, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In this seminar we present a model that generates tercets and verses respectively learning from poetry of Dante and Petrarca. Further details and a demo can be found here.
Read More »Alessandro Betti (DIISM, Universities of Florence and Siena) Oct 11, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description By and large, Backpropagation (BP) is regarded as one of the most important neural computation algorithms at the basis of the impressive progress in machine learning, including the recent advances in deep […]
Read More »Vincenzo Laveglia (DIISM, Universities of Florence and Siena) Oct 4, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In this preliminary work we try to formalize the notion of sparsity in neural networks defining an appropriate indicator that we call sparsity index (SI), and investigate how this indicator evolves during […]
Read More »Alberto Rossi (DIISM, Universities of Florence and Siena) Oct 4, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description The ability of develop a complex reasoning on a relational environment is a fundamental characteristic of the human kind, but has been proven to be a difficult task for neural networks. In […]
Read More »Lisa Graziani (DIISM, Universities of Florence and Siena) Sept 13, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Emotion recognition from audio is a widely studied topic, but is still very challenging because is not entirely clear which features are effective for the recognition task. Moreover voice features keep on […]
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Computational models of visual attention are at the crossroad of disciplines like cognitive science, computational neuroscience, and computer vision. When eye-tracking devices are not a viable option, models of human attention can be used to predict fixations. Not only humans are correlated in terms of the locations they fixate, but they also agree somewhat in the […]
Read More »Matteo Tiezzi (DIISM, University of Siena) Sept 6, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Adversarial examples are defined as “inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake”. Indeed, in the computer vision scenario it has been shown […]
Read More »Prof. Yoshua Bengio (University of Montreal, Department of Computer Science and Operations Research (DIRO) ) Jul 23, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description The traditional Machine Learning picture is that optimization and generalization are neatly separated aspects. That makes theory easier to handle, separately, but unfortunately this is not the case. Stochastic […]
Read More »Paolo Andreini (DIISM, University of Siena) Jul 5, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In recent years, deep neural networks have become the state of the art for semantic segmentation. Nevertheless, most of the success of deep learning rests on large sets of supervised data, which are not […]
Read More »Alessandro Betti (DIISM, Universities of Florence and Siena) Oct 4, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description By and large, Backpropagation (BP) is regarded as one of the most important neural computation algorithms at the basis of the progress in machine learning, including the recent advances in deep learning. […]
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