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
Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses
Michael R. Dawson, Adam S. R. Parker, Heather E. Williams, Ahmed W. Shehata, Jacqueline S. Hebert, Craig S. Chapman, Patrick M. Pilarski
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang
PACMAN: a framework for pulse oximeter digit detection and reading in a low-resource setting
Chiraphat Boonnag, Wanumaidah Saengmolee, Narongrid Seesawad, Amrest Chinkamol, Saendee Rattanasomrerk, Kanyakorn Veerakanjana, Kamonwan Thanontip, Warissara Limpornchitwilai, Piyalitt Ittichaiwong, Theerawit Wilaiprasitporn
The Normalized Cross Density Functional: A Framework to Quantify Statistical Dependence for Random Processes
Bo Hu, Jose C. Principe
Towards Automatic Cetacean Photo-Identification: A Framework for Fine-Grain, Few-Shot Learning in Marine Ecology
Cameron Trotter, Nick Wright, A. Stephen McGough, Matt Sharpe, Barbara Cheney, Mònica Arso Civil, Reny Tyson Moore, Jason Allen, Per Berggren
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation
Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields