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)

Forskningsprofessor at Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Morteza Haghir Chehreghani

Associate Professor at Chalmers, Computer Science and Engineering (Chalmers), Data Science

Funding

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