Liam Fowl

11 papers and 143 indexed citations i.

Liam Fowl is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Liam Fowl has authored 11 papers receiving a total of 143 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 1 paper in Molecular Biology. Recurrent topics in Liam Fowl’s work include Adversarial Robustness in Machine Learning (9 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Advanced Neural Network Applications (3 papers). Liam Fowl is often cited by papers focused on Adversarial Robustness in Machine Learning (9 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Advanced Neural Network Applications (3 papers). Liam Fowl collaborates with scholars based in United States and Germany. Liam Fowl's co-authors include Tom Goldstein, Micah Goldblum, Soheil Feizi, Jonas Geiping, Valeriia Cherepanova, Amin Ghiasi, Arjun K. Gupta, Richard G. Baraniuk, Yehuda Dar and Gavin Taylor and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and International Conference on Machine Learning.