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
Implementation of a framework for deploying AI inference engines in FPGAs
Ryan Herbst, Ryan Coffee, Nathan Fronk, Kukhee Kim, Kuktae Kim, Larry Ruckman, J. J. Russell
AdANNS: A Framework for Adaptive Semantic Search
Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham Kakade, Prateek Jain, Ali Farhadi
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague, Nouha Chatti, Colin Bellinger, Mitchell Shahen, Nicholas Paquin, Mark Baula, Amanuel Dawit, Zihan Yang, Xinkai Li, Mark Crowley, Isaac Tamblyn
Process-To-Text: A Framework for the Quantitative Description of Processes in Natural Language
Yago Fontenla-Seco, Alberto Bugarín-Diz, Manuel Lama
Low-complexity deep learning frameworks for acoustic scene classification using teacher-student scheme and multiple spectrograms
Lam Pham, Dat Ngo, Cam Le, Anahid Jalali, Alexander Schindler
STLCCP: An Efficient Convex Optimization-based Framework for Signal Temporal Logic Specifications
Yoshinari Takayama, Kazumune Hashimoto, Toshiyuki Ohtsuka
Style Transfer Enabled Sim2Real Framework for Efficient Learning of Robotic Ultrasound Image Analysis Using Simulated Data
Keyu Li, Xinyu Mao, Chengwei Ye, Ang Li, Yangxin Xu, Max Q. -H. Meng