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
A Framework for Extracting and Encoding Features from Object-Centric Event Data
Jan Niklas Adams, Gyunam Park, Sergej Levich, Daniel Schuster, Wil M. P. van der Aalst
nnOOD: A Framework for Benchmarking Self-supervised Anomaly Localisation Methods
Matthew Baugh, Jeremy Tan, Athanasios Vlontzos, Johanna P. Müller, Bernhard Kainz
JARVIS: A Neuro-Symbolic Commonsense Reasoning Framework for Conversational Embodied Agents
Kaizhi Zheng, Kaiwen Zhou, Jing Gu, Yue Fan, Jialu Wang, Zonglin Di, Xuehai He, Xin Eric Wang
Map Container: A Map-based Framework for Cooperative Perception
Kun Jiang, Yining Shi, Benny Wijaya, Mengmeng Yang, Tuopu Wen, Zhongyang Xiao, Diange Yang