INNER: information theory of deep neural networks
Forskningsprojekt, 2019
– 2021
Over the last decade, deep-learning algorithms have dramatically improved the state of the art in many machine-learning problems, including computer vision, speech recognition, natural language processing, and audio recognition. Despite their success, however, there is no satisfactory mathematical theory that explains the functioning of such algorithms. Indeed, a common critique is that deep-learning algorithms are often used as black box, which is unsatisfactory in all applications for which performance guarantees are critical (e.g., traffic-safety applications).
The purpose of this project is to increase our theoretical understanding of deep neural networks. This will be done by relying on tools of information theory and focusing on specific tasks that are relevant to computer vision.
Deltagare
Giuseppe Durisi (kontakt)
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Fredrik Kahl
Digitala bildsystem och bildanalys
Samarbetspartners
Chalmers AI-forskningscentrum (CHAIR)
Gothenburg, Sweden
Finansiering
Chalmers AI-forskningscentrum (CHAIR)
Finansierar Chalmers deltagande under 2019–2021
Relaterade styrkeområden och infrastruktur
Informations- och kommunikationsteknik
Styrkeområden