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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[Jan 23rd 2019] LabMeeting: Invertible conditional GANs

Lisa Graziani (DIISM, University of Siena) Jan 16, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Generative Adversarial Networks (GANs) allow to generate images and are used in a variety of applications, as image synthesis, semantic image editing, style transfer, image super-resolution, image transformation and classification. Relevant extensions of GANs […]

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[Jan 16th 2019] LabMeeting: Study of potential impact of copy number alterations of MAPK15 in High Grade Serous Ovarian Cancer

Giorgia Giacomini (DIISM, University of Siena) Jan 16, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description A powerful way to discover key genes playing causal roles in oncogenesis is to identify genomic regions that undergo frequent alteration in human cancers. Copy Number Alterations (CNAs) are structural rearrangements involving DNA segments, […]

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Events

July 15-19, 2019 The workshop is organized the 20th of July 2019 in conjunction with the 2nd Advanced Course on Data Science & Machine Learning at Certosa di Pontignano, Siena – Tuscany, Italy https://acdl2019.icas.xyz. GNN Workshop 2019

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[Jan 9th 2019] LabMeeting: Automatic prediction of druggability for protein surface pockets

Pietro Bongini (DIISM, University of Siena) Jan 09, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Drug discovery is a flourishing field of research with the goal of improving human health. Even though, in the last two decades, a massive amount of informatics has been introduced in this field, providing […]

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[Dec 17th 2018] Deep Graph Infomax

Pietro Liò (University of Cambridge) Dec 17, 2018 – 2:30 PM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs—both […]

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[Dec 12th 2018] LabMeeting: How to extend GNNs: from changing the neuron model to transductive learning

Giorgio Ciano (DIISM, University of Siena) Dec 12, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Most of the techniques used in Machine Learning (e.g. Deep Neural Networks) use vectors as inputs. However, graphs are a data structure that is more general and suitable to represent real-world problems. Even if […]

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[Dec 12th 2018] Evolutionary Coresets and Machine Learning Epistemology

Pietro Barbiero (DISMA, Politecnico di Torino) Dec 12, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In recent years, machine learning research has proposed effective algorithms, with huge impacts on applications ranging from medicine to autonomous driving cars. Although these models are powerful and accurate, they show serious weaknesses, such […]

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[Dec 12th 2018] Neural Biclustering in Gene Expression Analysis

Giansalvo Cirrincione (University of Picardie Jules Verne (UPJV), Amiens (France) and University of South Pacific (USP), Suva, Fiji) Dec 12, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Clustering in high dimensional spaces is a very difficult task. Dealing with DNA microarrays is even more difficult because gene subsets are […]

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[Dec 5th 2018] LabMeeting: Unity: A General Platform for Intelligent Agents

Luca Pasqualini (DIISM, University of Siena) Dec 5, 2018 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Recent advances in Deep Reinforcement Learning and Robotics have been driven by the presence of increasingly realistic and complex simulation environments. Many of the existing platforms, however, provide either unrealistic visuals, inaccurate physics, low […]

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