New Frontier
"New Frontier" research encompasses the application of advanced machine learning techniques, particularly deep learning models like transformers and diffusion models, to diverse and challenging problems across various scientific domains. Current efforts focus on improving model efficiency and accuracy for tasks such as medical image segmentation, autonomous navigation and exploration, and large-scale data analysis, often leveraging techniques like attention mechanisms and novel fusion strategies for multimodal data. This research is significant because it pushes the boundaries of what's computationally feasible, leading to advancements in fields ranging from healthcare and robotics to materials science and financial technology.
Papers
FrontierNet: Learning Visual Cues to Explore
Boyang Sun, Hanzhi Chen, Stefan Leutenegger, Cesar Cadena, Marc Pollefeys, Hermann Blum
Scaling Large Language Model Training on Frontier with Low-Bandwidth Partitioning
Lang Xu, Quentin Anthony, Jacob Hatef, Aamir Shafi, Hari Subramoni, Dhabaleswar K. (DK) Panda