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
Multi-legged matter transport: a framework for locomotion on noisy landscapes
Baxi Chong, Juntao He, Daniel Soto, Tianyu Wang, Daniel Irvine, Grigoriy Blekherman, Daniel I. Goldman
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis
Ross Greer, Samveed Desai, Lulua Rakla, Akshay Gopalkrishnan, Afnan Alofi, Mohan Trivedi
A Framework for Analyzing Cross-correlators using Price's Theorem and Piecewise-Linear Decomposition
Zhili Xiao, Shantanu Chakrabartty
NPS: A Framework for Accurate Program Sampling Using Graph Neural Network
Yuanwei Fang, Zihao Liu, Yanheng Lu, Jiawei Liu, Jiajie Li, Yi Jin, Jian Chen, Yenkuang Chen, Hongzhong Zheng, Yuan Xie