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)

Forskningsprofessor vid Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Morteza Haghir Chehreghani

Docent vid Chalmers, Data- och informationsteknik, Data Science

Finansiering

Chalmers AI Research Centre

Finansierar Chalmers deltagande under 2020–2025

Chalmers AI-forskningscentrum (CHAIR)

Finansierar Chalmers deltagande under 2020–

Relaterade styrkeområden och infrastruktur

Informations- och kommunikationsteknik

Styrkeområden

Mer information

Senast uppdaterat

2021-01-13