Flexible Toolkit
Flexible toolkits are software packages designed to streamline various research and application tasks by providing standardized workflows, pre-built modules, and readily available datasets. Current research focuses on developing toolkits for diverse areas, including machine learning model development and evaluation (e.g., federated learning, model verification, fairness), image and video processing (e.g., change detection, aesthetics analysis, instance segmentation), and natural language processing (e.g., large language model evaluation, question answering). These toolkits significantly enhance reproducibility, facilitate collaboration, and accelerate progress by lowering the barrier to entry for researchers and practitioners across multiple scientific disciplines.
Papers
ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks
Tianhao Wei, Luca Marzari, Kai S. Yun, Hanjiang Hu, Peizhi Niu, Xusheng Luo, Changliu Liu
OxonFair: A Flexible Toolkit for Algorithmic Fairness
Eoin Delaney, Zihao Fu, Sandra Wachter, Brent Mittelstadt, Chris Russell