3 citations · 6 across the 7 of their papers we have counts for
Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Multigrid Training for Molecular Generation using Graph Neural Networks
Zixuan Ling, Paula Mercurio, Di Liu
Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D grids. A major challenge is tha…
cs.LG2024★ 1 cited
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
JianHao Zhu, Changze Lv, Xiaohua Wang +7
Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for paramete…
cs.LG2024
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Muling Wu, Wenhao Liu, Xiaohua Wang +7
Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adju…