Energy-based models for supervised deep neural networks and their applications
Research Project, 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.
Participants
Christopher Zach (contact)
Imaging and Image Analysis
Morteza Haghir Chehreghani
Chalmers, Computer Science and Engineering (Chalmers), Data Science
Funding
Chalmers AI Research Centre (CHAIR)
Funding Chalmers participation during 2020–
Chalmers AI Research Centre
Funding Chalmers participation during 2020–2025
Related Areas of Advance and Infrastructure
Information and Communication Technology
Areas of Advance