Jörn-Henrik Jacobsen

9 papers and 262 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 262 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, David Duvenaud, Will Grathwohl, Kuan-Chieh Wang and Jens Behrmann and has published in prestigious journals such as Nature Machine Intelligence.