1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2025
Memory-Efficient Fine-Tuning of Transformers via Token Selection
Antoine Simoulin, Namyong Park, Xiaoyi Liu +1
Fine-tuning provides an effective means to specialize pre-trained models for various downstream tasks. However, fine-tuning often incurs high memory overhead, especially for large…
cs.LG2024
Forward Learning of Graph Neural Networks
Namyong Park, Xing Wang, Antoine Simoulin +5
Graph neural networks (GNNs) have achieved remarkable success across a wide range of applications, such as recommendation, drug discovery, and question answering. Behind the succes…
cs.LG2024★ 1 cited
GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection
Namyong Park, Ryan Rossi, Xing Wang +3
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting…