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Seminario: “Learning for structures: advances for efficient recursive and deep approaches”

Giovedì 4 Aprile 2019, ore 17:00 - Aula 1BC45 - Alessio Micheli

ARGOMENTI: Seminars

Giovedì 4 Aprile 2019 alle ore 17:00 in Aula 1BC45, Alessio Micheli (Computational Intelligence & Machine Learning Group (CIML), University of Pisa) terrà un seminario dal titolo “Learning for structures: advances for efficient recursive and deep approaches”.

Abstract
Moving to Structured Domains (SD - sequences, trees and graphs data) is a mainstream in the current evolution of Neural Networks and Machine Learning, which is rooted in foundational works developed in the last 20 years. Recent advancements which still is accompanying the current “deep learning revolution” are often at the price of high computation cost, empathizing the need of efficient approaches.
We will use results from my research group to show how we can move toward deep and efficient approaches for SD (both for sequences, trees and graphs) and, more in general, to show the many potential benefits in the extension and analysis of the impact of deep approaches for SD.

Short Bio
Prof. Dr. Alessio Micheli is Associate Professor at the Department of Computer Science of the University of Pisa, where he is the head of the Computational Intelligence & Machine Learning Group (CIML). He is the national coordinator of the "Italian Working group on Machine Learning and Data Mining" of AI*IA. His research interests include machine learning, neural networks, deep learning, sequence and structured domains learning, recurrent and recursive neural networks, reservoir computing models, kernel-based learning for structured data, and applications. He currently serves as an Associate Editor of IEEE TNNLS.