Welcome!

This is the official web site of Siena Artificial Intelligence Laboratory. The focus of our research is on machine learning. In the last few years, we’ve been mainly involved in the conception of new theories of learning in structured domains and in their applications to pattern recognition and mining the web. We are also interested […]

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[Oct 20th 2021] LabMeeting: Continuous learning in video streams exploiting attention trajectory

Simone Marullo (University of Siena) When: Oct 20th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Designing agents that autonomously learn in a visual environment is not that easy, since standard supervised offline strategies are not available. We want to discuss novel techniques, exploiting a human-like attention mechanism both for learning and […]

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[Oct 13th 2021] LabMeeting: A comprehensive Deep Learning-based approach to Reduced Order Modeling of Nonlinear Time-Dependent Parametrized PDEs

Giuseppe Alessio D’Inverno (University of Siena) When: Oct 13th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Conventional reduced order modeling techniques such as the reduced basis (RB) method (relying, e.g., on proper orthogonal decomposition (POD)) may incur in severe limita- tions when dealing with nonlinear time-dependent parametrized PDEs, as these are […]

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[Oct 5th 2021] LabMeeting: Neural Ordinary Differential Equation

Veronica Lachi (University of Siena) When: Oct 5th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network […]

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[Sep 29th 2021] LabMeeting: Learning Representations for Sub-Symbolic Reasoning

Caterina Graziani (University of Siena) When: Sep 29th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Neuro-symbolic methods integrate neural architectures, knowledge representation and reasoning. However, they have been struggling at both dealing with the intrinsic uncertainty of the observations and scaling to real world applications. This paper presents Relational Reasoning Networks […]

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[Sep 22nd 2021] LabMeeting: Trustworthy and Explainable Artificial Intelligence: an application to complex genetic diseases

Nicola Picchiotti (University of Pavia) When: Sep 22nd, 2021 – 11:00 – 11:45 AM Where: Google meet link Description The “black box” nature of deep neural network models is often a limit for safe applications since the reliability of the model predictions can be affected by the incompleteness in the optimization problem formalization. To frame […]

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[Sep 8th 2021] LabMeeting: Entropy-based Logic Explanations of Neural Networks

Gabriele Ciravegna (University of Siena) When: Sep 8th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class memberships. However, […]

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[Sep 1st 2021] LabMeeting: Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

Anna Visibelli (University of Siena) When: Sep 1st, 2021 – 11:00 – 11:45 AM Where: Google meet link Description By: Edison Ong, Haihe Wang, Mei U Wong, Meenakshi Seetharaman, Ninotchka Valdez and Yongqun He. Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy […]

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[Jul 27th 2021] LabMeeting: Continuous Action Spaces vs Discrete Action Spaces in Reinforcement Learning: a Practical Example

Luca Pasqualini (University of Siena) When: Jul 27th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description In reinforcement learning, with the exception of some control problems, de-facto continuous action spaces are rare. They have a powerful feature though: they can be used to reduce the complexity of combinatorial discrete action spaces. Through […]

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[Jul 21st 2021] LabMeeting: Computer-aided diagnosis of prostate cancer using multiparametric MRI and clinical features: a patient-level classification framework

Giorgio Ciano (University of Siena) When: Jul 21st, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Computer-aided diagnosis (CAD) of prostate cancer (PCa) using multiparametric magnetic resonance imaging (mpMRI) is actively being investigated as a means to provide clinical decision support to radiologists. Typically, these sy tems are trained using lesion annotations. […]

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[Jul 14th 2021] LabMeeting: Continual learning and catastrophic forgetting, a general overview

Lapo Faggi (University of Florence) When: Jul 14th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Traditional machine learning techniques usually assume static input data and the existence of a neat distinction between a training and a test phase. Input data, entirely available at the beginning of the learning procedure, are processed […]

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