New Framework
Recent research focuses on developing versatile frameworks for various tasks, primarily aiming to improve efficiency, reproducibility, and accessibility within their respective domains. These frameworks leverage diverse techniques, including programmatic data generation for LLMs, deep learning architectures for image and audio processing, and reinforcement learning for optimization and automated testing. The resulting advancements enhance the development and evaluation of AI models, improve the reliability of benchmarking processes, and offer new tools for diverse applications ranging from healthcare diagnostics to autonomous vehicle navigation.
Papers
Happily Error After: Framework Development and User Study for Correcting Robot Perception Errors in Virtual Reality
Maciej K. Wozniak, Rebecca Stower, Patric Jensfelt, Andre Pereira
Multitask Learning for Multiple Recognition Tasks: A Framework for Lower-limb Exoskeleton Robot Applications
Joonhyun Kim, Seongmin Ha, Dongbin Shin, Seoyeon Ham, Jaepil Jang, Wansoo Kim
A framework for dynamically training and adapting deep reinforcement learning models to different, low-compute, and continuously changing radiology deployment environments
Guangyao Zheng, Shuhao Lai, Vladimir Braverman, Michael A. Jacobs, Vishwa S. Parekh
DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language Models
Amr Keleg, Walid Magdy
A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AI
Vita Santa Barletta, Danilo Caivano, Domenico Gigante, Azzurra Ragone
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making
Mahault Albarracin, Inês Hipólito, Safae Essafi Tremblay, Jason G. Fox, Gabriel René, Karl Friston, Maxwell J. D. Ramstead
Understanding Progressive Training Through the Framework of Randomized Coordinate Descent
Rafał Szlendak, Elnur Gasanov, Peter Richtárik