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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[Oct 18th 2020] LabMeeting: SAAMBE-SEQ: A Sequence-based Method for Predicting Mutation Effect on Protein-protein Binding Affinity

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 […]

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Vulgaris's Families Timeline

Vulgaris

Have a a look at the Technical report here! Our paper was recently accepted at VarDial 2020 Seventh Workshop on NLP for Similar Languages, Varieties and Dialects co-located with COLING 2020. Cite @misc{zugarini2020vulgaris, title={Vulgaris: Analysis of a Corpus for Middle-Age Varieties of Italian Language}, author={Andrea Zugarini and Matteo Tiezzi and Marco Maggini}, year={2020}, eprint={2010.05993}, archivePrefix={arXiv}, […]

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[Oct 7th 2020] LabMeeting: Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography

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 […]

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[Sep 30th 2020] LabMeeting: Imitation Learning – predicting goal-directed scanpaths

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 […]

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[Sep 23rd 2020] LabMeeting: Evaluating the impact of semantic features and domain knowledge on text categorization

Marco Ernandes Sep 23, 2020 – 11:45 – 12:30 AM Conference Meeting Description The text-mining field is currently in the eye of a technological storm: dozens of novel (and effective!) algorithms and architectures have been recently released, mainly by global AI players, such as Google, OpenAI, Microsoft. From a methodological point of view we observe […]

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[Sep 23th 2020] LabMeeting: Spectral Clustering with Graph Neural Networks for Graph Pooling

Niccolò Pancino (University of Florence) Sep 23, 2020 – 11:00 – 11:45 AM Conference Meeting Description Spectral clustering (SC) is a popular clustering technique to find strongly connected communities on a graph. It can be used in Graph Neural Networks to implement pooling operations that aggregate nodes belonging to the same cluster. However, SC is […]

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[Sep 16th 2020] LabMeeting: SAILenv: Learning in Virtual Visual Environments Made Simple

Enrico Meloni (University of Florence) Sep 16, 2020 – 11:00 AM Conference Meeting Description Recently, researchers in Machine Learning algorithms, Computer Vision scientists, engineers, and others, showed a growing interest in 3D simulators as a mean to artificially create experimental settings that are remarkably close to those in the real world. However, most of the […]

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[Jul 22nd 2020] LabMeeting: Partial Distance Correlation for Exploring Dependence Between Similarity Networks

Federica Baccini (University of Florence) Jul 22, 2020 – 11:00 AM Conference Meeting Description Similarity network fusion is a tool that aggregates the information coming from a multiplex network into a unique layer through a cross diffusion process. However, the process itself does not allow to infer which layer has the major impact in the […]

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[Jul 15th 2020] LabMeeting: Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?

Gabriele Ciravegna (University of Florence) Jul 15, 2020 – 11:00 AM Conference Meeting Description Adversarial attacks on machine learning-based classifiers, along with defence mechanisms, have been widely studied in the context of single-label classification problems. In this paper, we shift the attention to multi-label classification, where the availability of domain knowledge on the relationships among […]

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