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
Limits for Learning with Language Models
Nicholas Asher, Swarnadeep Bhar, Akshay Chaturvedi, Julie Hunter, Soumya Paul
Pushing the Limits of Machine Design: Automated CPU Design with AI
Shuyao Cheng, Pengwei Jin, Qi Guo, Zidong Du, Rui Zhang, Yunhao Tian, Xing Hu, Yongwei Zhao, Yifan Hao, Xiangtao Guan, Husheng Han, Zhengyue Zhao, Ximing Liu, Ling Li, Xishan Zhang, Yuejie Chu, Weilong Mao, Tianshi Chen, Yunji Chen
Faith and Fate: Limits of Transformers on Compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro