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.


Christopher Zach (contact)

Imaging and Image Analysis

Morteza Haghir Chehreghani

Chalmers, Computer Science and Engineering (Chalmers), Data Science


Chalmers AI Research Centre

Funding Chalmers participation during 2020–2025

Chalmers AI Research Centre (CHAIR)

Funding Chalmers participation during 2020–

Related Areas of Advance and Infrastructure

Information and Communication Technology

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