8 citations · 14 across the 5 of their papers we have counts for
5 papers
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks
Yaochu Jin, Xueming Yan, Shiqing Liu +1
Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial optimization problems (COPs), exhibiting state-of-the-art performance in both graph-structure…
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems
Shiqing Liu, Xueming Yan, Yaochu Jin
In recent years, there has been a notable surge in research on machine learning techniques for combinatorial optimization. It has been shown that learning-based methods outperform…
End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location
Shiqing Liu, Xueming Yan, Yaochu Jin
The facility location problems (FLPs) are a typical class of NP-hard combinatorial optimization problems, which are widely seen in the supply chain and logistics. Many mathematical…
A Survey on Computationally Efficient Neural Architecture Search
Shiqing Liu, Haoyu Zhang, Yaochu Jin
Neural architecture search (NAS) has become increasingly popular in the deep learning community recently, mainly because it can provide an opportunity to allow interested users wit…
Federated Learning on Non-IID Data: A Survey
Hangyu Zhu, Jinjin Xu, Shiqing Liu +1
Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse performance than…