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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[Nov 15th 2023] Applications of Large Language Models in the field of education

KAMYAR ZEINALIPOUR When: Nov 15th, 2023 – 11:30 – 12:00 AM Where: Google meet link Description Applications of Large Language Models in the field of education This seminar explores the transformative impact of Large Language Models (LLMs) on education, focusing on their application in the generation of multilingual educational crosswords and quizzes. The seminar delves […]

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RexLearn Software

The software that was developed in the context of the PRIN2017 Project RexLearn, funded by the Italian Ministry of Education, University and Research (grant no. 2017TWNMH2), can be accessed at the page indicated in this post. RexLearn Software

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TAILOR WP4 Workshop at NeSy2023

Workshop on “Benchmarks for Neural-Symbolic AI”  July 3, 2023 – 11:00-13:00    To participate: in person: at NeSy2023 Workshop, La Certosa di Pontignano, Siena, Italy online: Google Meet Conference Room   Abstract The study of Neural-Symbolic (NeSy) approaches has been a longstanding goal in the field of AI. However, the evaluation of novel ideas and frameworks in this […]

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NeSy 2023 – 17th International Workshop on Neural-Symbolic Learning and Reasoning

We are pleased to announce that SAILab will participate in the organization of the next edition of the NeSy Worshop! Official Website: https://sites.google.com/view/nesy2023 NeSy Workshop dates: 3-5 July 2023 Location: Certosa di Pontignano, Siena, Italy The NeSy workshop series is the longest standing gathering for the presentation and discussion of cutting edge research in neurosymbolic AI. […]

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External Seminar On Bayesian Inference and Generative Models for Wireless Communication Algorithms Organised by prof. Andrea Abrardo [Apr 12th, 2023 – 10:00 – 13:00 AM]

Prof. Wolfang Utschick When: Apr 12th, 2023 – 10:00 – 13:00 AM Where: In presence at the San Niccolò building Bayesian Inference and Generative Models for Wireless Communication Algorithms Abstract: This tutorial first introduces the fundamentals of generative models, followed by the perspective of learning the radio propagation environment (“radio-compatible digital twin”) of wireless communication […]

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[Apr 5th 2023] The expressive power of pooling in Graph Neural Networks

VERONICA LACHI When: Apr 5th, 2023 – 11:00 – 11:30 AM Where: Google meet link Description The expressive power of pooling in Graph Neural Networks In Graph Neural Networks (GNNs), hierarchical pooling operators generate a coarser representation of the input data by creating local summaries of the graph structure and its vertex features. Considerable attention […]

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[Mar 29th 2023] Learning Identity Effects with Graph Neural Networks

GIUSEPPE ALESSIO D’INVERNO When: Mar 29th, 2023 – 15:00 – 15:30 AM Where: Google meet link Description Learning Identity Effects with Graph Neural Networks Graph Neural Networks (GNNs) have emerged in the past years as a powerful tool to learn tasks on a wide range of graph domains in a data-driven fashion; among all the […]

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[Mar 22th 2023] LabMeeting: RandomWalk Graph Neural Networks

Caterina Graziani When: Mar 22th, 2023 – 11:00 – 11:30 AM Where: Google meet link Description RandomWalk Graph Neural Networks Abstract: In recent years, graph neural networks (GNNs) have become the de facto tool for performing machine learning tasks on graphs. Most GNNs belong to the family of message-passing neural networks (MPNNs). These models employ […]

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[Feb 22th 2023] LabMeeting: Template based Graph Neural Network with Optimal Transport Distances

Marco Corneli  When: Feb 22th, 2023 – 11:00 – 11:30 AM Where: Google meet link Description WebCrowDE – Template based Graph Neural Network with Optimal Transport Distances Abstract : Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing and aggregation with a specialized form of pooling. […]

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