Jörn-Henrik Jacobsen

9 papers and 268 indexed citations i.

Jörn-Henrik Jacobsen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Statistical and Nonlinear Physics. According to data from OpenAlex, Jörn-Henrik Jacobsen has authored 9 papers receiving a total of 268 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Jörn-Henrik Jacobsen’s work include Adversarial Robustness in Machine Learning (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Jörn-Henrik Jacobsen is often cited by papers focused on Adversarial Robustness in Machine Learning (3 papers), Generative Adversarial Networks and Image Synthesis (3 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Jörn-Henrik Jacobsen collaborates with scholars based in Canada, Germany and United States. Jörn-Henrik Jacobsen's co-authors include Richard S. Zemel, Matthias Bethge, Robert Geirhos, Felix A. Wichmann, Claudio Michaelis, Wieland Brendel, A.W.M. Smeulders, Jan van Gemert, David Madras and Kevin Swersky and has published in prestigious journals such as Nature Machine Intelligence, Wiardi Beckman Foundation (Wiardi Beckman Foundation) and arXiv (Cornell University).