2 citations · 3 across the 10 of their papers we have counts for
7 papers · 1 filter
Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations
Zongwei Zhang, Chin Chun Ooi, Lianlei Lin +8
The high proportion of wind power connected to the grid places higher demands on fine-grained knowledge of regional wind fields. Since the wind information directly obtainable in a…
Taming the Instability: A Robust Second-Order Optimizer for Federated Learning over Non-IID Data
Yuanqiao Zhang, Tiantian He, Yuan Gao +5
In this paper, we present Federated Robust Curvature Optimization (FedRCO), a novel second-order optimization framework designed to improve convergence speed and reduce communicati…
Stabilized Fine-Tuning with LoRA in Federated Learning: Mitigating the Side Effect of Client Size and Rank via the Scaling Factor
Jiayu Huang, Xiaohu Wu, Tiantian He +1
Large Language Models (LLMs) are pivotal in natural language processing. The impracticality of full fine-tuning has prompted Parameter-Efficient Fine-Tuning (PEFT) methods like Low…
Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning
Mengmeng Chen, Xiaohu Wu, Qiqi Liu +5
Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for…
Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening
Zhangfan Yang, Junkai Ji, Shan He +5
Molecular docking is a crucial step in drug development, which enables the virtual screening of compound libraries to identify potential ligands that target proteins of interest. H…
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning
Zhilong Li, Xiaohu Wu, Xiaoli Tang +6
There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative t…
Road Network Representation Learning with the Third Law of Geography
Haicang Zhou, Weiming Huang, Yile Chen +3
Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we id…