4 citations · 6 across the 5 of their papers we have counts for
5 papers
A Fully Dual Domain Decomposition Method for Extreme Learning Machines Using GenEO Spectral Coarse Spaces
Chang-Ock Lee, Byungeun Ryoo
Extreme learning machines (ELMs) are a class of machine learning algorithms that randomly intialize and subsequently fix the hidden layer parameters, and solve for the last layer c…
Hybrid Least Squares/Gradient Descent Methods for MIONets
Jun Choi, Chang-Ock Lee, Minam Moon
In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes the LSGD method for DeepONets…
Hybrid Least Squares/Gradient Descent Methods for DeepONets
Jun Choi, Chang-Ock Lee, Minam Moon
We propose an efficient hybrid least squares/gradient descent method to accelerate DeepONet training. Since the output of DeepONet can be viewed as linear with respect to the last…
A Neumann-Neumann Acceleration with Coarse Space for Domain Decomposition of Extreme Learning Machines
Chang-Ock Lee, Byungeun Ryoo
Extreme learning machines (ELMs), which preset hidden layer parameters and solve for last layer coefficients via a least squares method, can typically solve partial differential eq…
A Nonoverlapping Domain Decomposition Method for Extreme Learning Machines: Elliptic Problems
Chang-Ock Lee, Youngkyu Lee, Byungeun Ryoo
Extreme learning machine (ELM) is a methodology for solving partial differential equations (PDEs) using a single hidden layer feed-forward neural network. It presets the weight/bia…