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 […]

Read More »

[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 […]

Read More »

[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. […]

Read More »

[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 […]

Read More »

[Jul 7th 2021] LabMeeting: Hyper-SAGNN, a self-attention based graph neural network for hypergraphs

Niccolò Pancino (University of Siena) When: Jul 7th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real-world problems. Current approaches designed for hypergraphs, however, are unable to handle different types of hypergraphs […]

Read More »

[Jun 30th 2021] LabMeeting: Generating Parametric 3D Virtual Environments For Learning

Enrico Meloni (University of Siena) When: Jun 30, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Many existing research works usually involve training and testing of virtual agents on datasets of static images or short videos, considering sequences of distinct learning tasks. However, in order to devise continual learning algorithms that operate […]

Read More »

[Jun 23th 2021] LabMeeting: CaregiverMatcher, graph neural networks for connecting caregivers of rare disease patients

Pietro Bongini (University of Siena) When: Jun 23, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Rare diseases affect a growing number of individuals. One key problem for patients and their caregivers is the difficulty in reaching experts and associations competent on a particular disease. As a consequence, caregivers, often family members […]

Read More »

[Jun 16th 2021] LabMeeting: Advanced ML methods to understand the genetic mechanism of COVID-19 severity and multi-organ involvement

Marco Tanfoni (University of Siena) When: Jun 16th, 2021 – 11:00 – 11:45 AM Where: Google meet link Description The main goal of this study was to establish a link between COVID-19 lung damage and existing mutations on some genes, particularly with regard to disease severity. A regularized logistic regression model was implemented to identify […]

Read More »

[May 19th 2021] LabMeeting: Friendly Training

Simone Marullo (University of Siena) When: May 19, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Two papers by Simone Marullo, Matteo Tiezzi, Marco Gori and Stefano Melacci will be presented: – Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier – Being Friends Instead of Adversaries: Deep Networks Learn […]

Read More »

[May 12th 2021] LabMeeting: Graph Dynamic Embedding

Veronica Lachi (University of Siena) When: May 12, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Representation learning of static and more recently dynamically evolving graphs has gained noticeable attention. Existing approaches for modelling graph dynamics focus extensively on the evolution of individual nodes independently of the evolution of mesoscale community structures. […]

Read More »