SAILab

Giuseppe Alessio D’Inverno (University of Siena) Nov 18, 2020 – 11:45 – 12:30 AM Conference Meeting Description Neural networks are nowadays one of the hottest topics in Applied Mathematics and, from Bioinformatics to Artificial Intelligence, their use spreads over several scientific disciplines. Beside the analytical approach, which is predominant in the literature, a new geometric […]

Federica Baccini (University of Florence) Nov 18, 2020 – 11:00 – 11:45 AM Conference Meeting Description In this talk, I will introduce the problem of processing heterogeneous graphs (HETGs), i.e. graphs where nodes represent different entities, and edges denote multiple relation types. Despite heterogeneous graphs are able to model a broad variety of real world […]

José Manuel Gomez-Perez Nov 11, 2020 – 11:00 – 11:45 AM Conference Meeting Description In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based in deep […]

Alberto Rossi (University of Florence) Nov 11, 2020 – 11:00 – 11:45 AM Conference Meeting Description Nuclear magnetic resonance (NMR) is a powerful and non–invasive diagnostic tool. However, NMR scanned images are often noisy due to patient motions or breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with […]

Andrea Zugarini (University of Florence) Nov 4, 2020 – 11:00 – 11:45 AM Conference Meeting Description In the last decade most of the advances in NLP were achieved by learning textual representations with unsupervised Language Modeling-related tasks on large corpora. Recently, this principle was pushed even further with transformers, where the scales of datasets and […]

Gabriele Ciravegna (University of Florence) Oct 28, 2020 – 11:00 – 11:45 AM Conference Meeting Description One of the key limitations of modern deep learning approaches lies in the amount of data required to train them. Humans, by contrast, can learn to recognize novel categories from just a few examples. Instrumental to this rapid learning […]

Anna Visibelli (University of Siena) Oct 21, 2020 – 11:00 – 11:45 AM Conference Meeting Description Vast majority of human genetic disorders are associated with mutations that affect protein-protein interactions by altering wild type binding affinity. Therefore, it is extremely important to assess the effect of mutations on protein-protein binding free energy to assist the […]

Giorgio Ciano (University of Florence) Oct 7, 2020 – 11:00 – 11:45 AM Conference Meeting Description Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research […]

Lapo Faggi (University of Florence) Sep 30, 2020 – 11:00 AM Conference Meeting Description In this seminar, some recent imitation learning algorithms will be presented. Imitation learning, also known as learning from demonstrations, is a powerful and practical alternative to reinforcement learning for learning sequential decision-making policies without the need of defining any (hand-crafted) reward […]