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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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. […]

Caterina Graziani (University of Siena) When: Apr 28, 2021 – 11:00 – 11:45 AM Where: Google meet link Description Many tasks in AI can be divided into roughly two categories: those that require low-level perception, and those that require high-level reasoning. At the same time, there is a growing consensus that being capable of tackling […]

Giuseppe Alessio D’Inverno (University of Siena) When: Apr 21, 2021 – 11:00 – 11:45 AM Where: Google meet link Description We propose to optimize the activation functions of a deep neural network by adding a corresponding functional regularization to the cost function. We justify the use of a second- order total-variation criterion. This allows us […]

Federica Baccini (University of Siena) When: Apr 14, 2021 – 11:00 – 11:45 AM Where: Google meet link Description We propose a method for gene expression based analysis of cancer phenotypes incorporating network biology knowledge through unsupervised construction of computational graphs. The structural construction of the computational graphs is driven by the use of topological […]