[Jan 15th 2020] LabMeeting: Toward in silico Design of Rhodopsin Mutants by integrating Hybrid QM/MM and Machine Learning Approaches

Anna Visibelli (University of Siena) Jan 15, 2020 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Microbial rhodopsins, light-dependent ion-transporting membrane proteins, are widely used in optogenetics, due to their low phototoxicity and high tissue penetration. To widen possible optogenetic applications (e.g., diverse optical control of neural activity) novel rhodopsins, exhibiting […]

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[Jan 8th 2020] LabMeeting: Deep Learning and the Renormalization Group

Lapo Faggi (University of Florence) Jan 8, 2020 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Despite their enormous success, a clear theoretical understanding on why Deep Neural Networks work so well and on how they can efficiently extract specific features is still lacking. Recently, some possible explanations were proposed, based […]

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[Dec 18th 2019] LabMeeting: Toward the probabilistic modeling of sequences of generalized random graphs: a hybrid approach

Edmondo Trentin (University of Siena) Dec 18, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Structured data in the form of labeled graphs (with variable order and topology) may be thought of as the outcomes of a random graph generating process, characterized by an underlying probabilistic law. In this talk […]

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[Dec 11th 2019] LabMeeting: Network Analysis for the Integration of Histone Modification Data to Explain Haematopoiesis

Federica Baccini (University of Pisa) Dec 11, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In this seminar a model that combines different techniques of network analysis is presented, together with its application to epigenetic data, in order to study haematopoiesis, i.e. the process of differentiation of blood cells. Currently, […]

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[Dec 4th 2019] LabMeeting: Graph Neural Network for the Prediction of Protein-Protein Interfaces

Niccolò Pancino (University of Florence) Dec 04, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Binding site identification allows to determine the functionality and the quaternary structure of protein–protein complexes. Various approaches to this problem have been proposed without reaching a viable solution. Graph theory is a very helpful instrument […]

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[Nov 27th 2019] LabMeeting: Learning in Virtual Environments

Enrico Meloni (University of Florence) Nov 27, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Recently, there have been many breakthroughs in computer vision thanks to Deep Learning. The availability of huge annotated datasets was one of the reasons of this success, if not the most important. However, these datasets […]

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[Nov 20th 2019] LabMeeting: A Two Stage GAN for High Resolution Retinal Image Generation and Segmentation

Paolo Andreini (University of Siena) Nov 20, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description In recent years, the use of deep learning is becoming increasingly popular in computer vision. However, the effective training of deep architectures usually relies on huge sets of annotated data. This is critical in the […]

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[Nov 13th 2019] LabMeeting: Neural Text Degeneration

Andrea Zugarini (University of Florence) Nov 13, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Despite considerable advancements with deep neural language models, they still struggle when tested as text generators. In this seminar we analyse the reasons of such a counter-intuitive behaviour, mainly guided by the considerations and findings […]

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[Nov 6th 2019] LabMeeting: Threat of Adversarial Attacks on Deep Learning in Computer Vision: a Survey

Gabriele Ciravegna (University of Florence) Nov 6, 2019 – 11:00 AM DIISM, Artificial Intelligence laboratory (room 201), Siena SI Description Deep learning is at the heart of the current rise of artificial intelligence. In the field of Computer Vision, it has become the workhorse for applications ranging from self-driving cars to surveillance and security. Whereas […]

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