6 citations · 14 across the 16 of their papers we have counts for
Showing 2024Show all
3 papers · 1 filter
cs.LG2024
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach
Yequan Zhao, Hai Li, Ian Young +1
Back propagation (BP) is the default solution for gradient computation in neural network training. However, implementing BP-based training on various edge devices such as FPGA, mic…
cs.LG2024★ 2 cited
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…
cs.LG2024★ 4 cited
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…