Energy-based models for supervised deep neural networks and their applications
Forskningsprojekt, 2020
– 2025
Despite deep learning-based methods being the state-of-the-art in many AI-related applications, there is a lack of consensus of how to understand and interpret deep neural networks in order to reason about their strengths and weaknesses. Energy-based models in machine learning have a long tradition as a framework to learn from unlabeled data, i.e. unsupervised learning. The purpose of this project is to enrich our understanding of deep machine learning with the help of energy-based models, where we build on existing experience of relating feed-forward deep networks and EBMs.
Deltagare
Christopher Zach (kontakt)
Digitala bildsystem och bildanalys
Morteza Haghir Chehreghani
Chalmers, Data- och informationsteknik, Data Science
Finansiering
Chalmers AI-forskningscentrum (CHAIR)
Finansierar Chalmers deltagande under 2020–2025
Chalmers AI-forskningscentrum (CHAIR)
Finansierar Chalmers deltagande under 2020–
Relaterade styrkeområden och infrastruktur
Informations- och kommunikationsteknik
Styrkeområden