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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[Sep 21st 2022] LabMeeting: Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging

Eklia Giuseppe Ceroni When: Sep 21st, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging by Lin Lu, Laurent Dercle, Binsheng Zhao, and Lawrence H. Schwartz In current clinical practice, tumor response assessment is usually based […]

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[Sep 14th 2022] LabMeeting: On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs

Caterina Graziani When: Sep 14th, 2022 – 11:45 – 12:30 AM Where: Google meet link Description On the Extension of the Weisfeiler-Lehman Hierarchy by WL Tests for Arbitrary Graphs Graph isomorphism (GI) has occupied both theoreticians and applied scientists since the early 1950s. Over the years, several approaches and algorithms with which an isomorphism between […]

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[Sep 14th 2022] LabMeeting: Weisfeiler-Lehman goes dynamic: an analysis of the expressive power of Graph Neural Network for Attributed and Dynamic Graphs

Veronica Lachi When: Sep 14th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Weisfeiler-Lehman goes dynamic: an analysis of the expressive power of Graph Neural Network for Attributed and Dynamic Graphs Graph Neural Networks (GNNs) are a large class of connectionist models for graph processing. Recent theoretical studies on the expressive power […]

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[Jun 29th 2022] LabMeeting: Learning to Prompt for Continual Learning

Simone Marullo When: Jun 29th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Learning to Prompt for Continual Learning The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge. Typical methods rely on a rehearsal buffer or known […]

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[Jun 8th 2022] LabMeeting: 1-Lipschitz Neural Networks: a splines-based approach

Giuseppe Alessio D’Inverno (University of Siena) When: Jun 8th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description 1-Lipschitz Neural Networks: a splines-based approach Lipschitz-constrained neural networks have many applications in machine learning. Since designing and training expressive Lipschitz-constrained networks is very challenging, there is a need for improved methods and a better […]

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[Jun 1st 2022] LabMeeting: Neural dynamic in temporal environments

Lapo Faggi (University of Siena) When: Jun 1st, 2022 – 11:00 – 11:45 AM Where: Google meet link Description Neural dynamic in temporal environments Learning in a continual manner is one of the main challenges that the machine learning community is currently facing. The importance of the problem can be readily understood as soon as […]

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[May 18th 2022] LabMeeting: DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations

Niccolò Pancino (University of Siena) When: May 18th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations Drug combination therapy has become an increasingly promising method in the treatment of cancer. However, the number of possible drug combinations is so […]

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[May 11th 2022] LabMeeting: An introduction to higher order networks and simplicial complexes

Federica Baccini (University of Siena) When: May 11th, 2022 – 11:00 – 11:45 AM Where: Google meet link Description An introduction to higher order networks and simplicial complexes Network science aims to capture the complexity of a system by studying the interactions among its constituents. For overcoming the shortfall of standard network models, research is […]

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Constrained Affective Computing

Author: Lisa Graziani Date: May, 2021 Topics: Affective Computing, Learning from Constraints, Facial Expression Recognition, Text Emotion Recognition, Speech Emotion Recognition, Facial Expression Generation. Abstract Emotions have an important role in daily life, influence decision-making, human interaction, perception, attention, self-regulation. They have been studied since ancient times, philosophers have been always interested in analyzing human […]

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