Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting H Li, GK Rajbahadur, D Lin, CP Bezemer, ZM Jiang IEEE Access, 2024 | 19 | 2024 |
An empirical study of yanked releases in the rust package registry H Li, FR Cogo, CP Bezemer IEEE Transactions on Software Engineering 49 (1), 437-449, 2022 | 12 | 2022 |
Bridging the language gap: an empirical study of bindings for open source machine learning libraries across software package ecosystems H Li, CP Bezemer Empirical Software Engineering 30 (1), 6, 2025 | 3* | 2025 |
Studying the impact of tensorflow and pytorch bindings on machine learning software quality H Li, GK Rajbahadur, CP Bezemer ACM Transactions on Software Engineering and Methodology 34 (1), 1-31, 2024 | 3 | 2024 |
Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models H Li, CP Bezemer, AE Hassan arXiv preprint arXiv:2410.09012, 2024 | 1 | 2024 |
Systems, methods, and non-transitory computer-readable storage devices for training deep learning and neural network models using overfitting detection and prevention H Li, GK Rajbahadur, D Lin, Z Jiang US Patent App. 18/384,634, 2024 | | 2024 |
Investigating the Quality of Bindings for Machine Learning Libraries in Software Package Ecosystems H Li | | 2024 |