Continuum Limit
The continuum limit explores the behavior of systems as their discrete components become infinitely small, effectively transitioning from a discrete to a continuous representation. Current research focuses on understanding and quantifying the limitations of this transition in various domains, including neural networks, agent-based models, and dynamical systems, often employing techniques like metric entropy analysis and novel algorithms for improved approximation and efficiency. These investigations are crucial for advancing our understanding of complex systems and improving the performance of machine learning models and other computational methods in scenarios with limited resources or inherent constraints.
Papers
Pushing the limits of cell segmentation models for imaging mass cytometry
Kimberley M. Bird, Xujiong Ye, Alan M. Race, James M. Brown
BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Wei Huang, Yangdong Liu, Haotong Qin, Ying Li, Shiming Zhang, Xianglong Liu, Michele Magno, Xiaojuan Qi
Limits of Large Language Models in Debating Humans
James Flamino, Mohammed Shahid Modi, Boleslaw K. Szymanski, Brendan Cross, Colton Mikolajczyk