[Jul 23th 2018] On stochastic gradient descent, flatness and generalization – Seminar by Prof. Yoshua Bengio

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 […]

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[Jun 28th 2018] LabMeeting: Learning with Architectural Constraints

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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Award cerimony

First Place at Hackathon Infinity

Three of our PhD students (Giuseppe Marra, Dario Zanca, and Andrea Zugarini, together with Giovanni Ciccone) won the Hackathon Mediaset Infinity, held in Rimini at Web Marketing Festival 2018. The goal of the challenge was to predict churn rate of users registered in infinitytv platform. Ten teams, composed of both students and professionals from Statistics […]

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[ Jun 21st 2018] Lab Meeting: Loss functions generation by means of fuzzy aggregators

Francesco Giannini (DIISM, University of Siena) Jun 21, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In a learning from constraints problem, the prior knowledge can be expressed by logical formulas and then converted into real valued functions. Hence the satisfaction of a certain formula F can be enforced by […]

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[ Jun 14th 2018] Lab Meeting: A Character-Aware Neural Model to Learn Word and Context Representations

Andrea Zugarini (DIISM, University of Siena) Jun 14, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Word and context embeddings have been of significant help in achieving state-of-the-art results in different Natural Language Processing (NLP) tasks. The success of these representations comes from the learning process, tipycally accomplished in unsupervised […]

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[Jun 7th 2018] LabMeeting: Probabilistic Soft Logic

Giuseppe Marra (DIISM, University of Siena) Jun 7, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured […]

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[May 31st 2018] LabMeeting: Depth growing neural networks

Vincenzo Laveglia (DIISM, University of Siena) May 31, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Being able to train neural networks (shallow or deep) requires the ability to identify the right hyper-parameters for the model. Some of these hyper-parameters are more related to the training procedure (epochs, learning rate, […]

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[May 24th 2018] LabMeeting: Geometric deep learning: dealing to structured data

Alberto Rossi (DIISM, University of Siena) May 24, 2018 – 9:30 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Many research areas deal with structured data, like social science, biology and computer graphics. Such geometric data could be very large and complex (social network), and machine learning techniques reveal their power to deal […]

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[May 28th 2018] Deep Learning of Barcode Sequences

Dr. Stefan C. Kremer, Professor (School of Computer and Science, University of Guelph, ON, CANADA) May 28, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Biological systems, on all scales, are essentially information processing and control systems. From this perspective, analogies between biological processes and computational processes can be drawn. […]

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