4 citations · 7 across the 6 of their papers we have counts for
7 papers
DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data
Yunsheng Yuan, Shaowei Li, Kai Wang +5
Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging. Since training data are often distributed across multiple c…
ZOAF: Towards Efficient Zeroth-Order Optimization for Analog/RF Circuit Design
Liyan Tan, Yequan Zhao, Jinming Lu +3
Circuit optimization is an indispensable step in analog/RF IC design. Classical fast gradient-based optimization methods are typically infeasible due to lack of access to simulator…
GRZO: Group-Relative Zeroth-Order Optimization for Large Language Model Fine-Tuning
Liyan Tan, Yequan Zhao, Yifan Yang +3
Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limited by the high variance of gra…
Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks
Yequan Zhao, Xinling Yu, Xian Xiao +6
Physics intelligence and digital twins often require rapid and repeated performance evaluation of various engineering systems (e.g. robots, autonomous vehicles, semiconductor chips…
Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
Yequan Zhao, Xian Xiao, Antoine Descos +6
Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back…
Separable Operator Networks
Xinling Yu, Sean Hooten, Ziyue Liu +4
Operator learning has become a powerful tool in machine learning for modeling complex physical systems governed by partial differential equations (PDEs). Although Deep Operator Net…
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Yequan Zhao, Xian Xiao, Xinling Yu +5
Solving partial differential equations (PDEs) numerically often requires huge computing time, energy cost, and hardware resources in practical applications. This has limited their…