Search Results for author: Shiqing Liu

Found 6 papers, 0 papers with code

Students Rather Than Experts: A New AI For Education Pipeline To Model More Human-Like And Personalised Early Adolescences

no code implementations21 Oct 2024 Yiping Ma, Shiyu Hu, Xuchen Li, Yipei Wang, Shiqing Liu, Kang Hao Cheong

Specifically, we: (1) develop a theoretical framework for generating LVSA; (2) integrate human subjective evaluation metrics into GPT-4 assessments, demonstrating a strong correlation between human evaluators and GPT-4 in judging LVSA authenticity; and (3) validate that LLMs can generate human-like, personalized virtual student agents in educational contexts, laying a foundation for future applications in pre-service teacher training and multi-agent simulation environments.

A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks

no code implementations19 Jun 2024 Yaochu Jin, Xueming Yan, Shiqing Liu, Xiangyu Wang

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-structured and non-graph-structured domains.

Combinatorial Optimization

An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems

no code implementations10 Oct 2023 Shiqing Liu, Xueming Yan, Yaochu Jin

It has been shown that learning-based methods outperform traditional heuristics and mathematical solvers on the Traveling Salesman Problem (TSP) in terms of both performance and computational efficiency.

Combinatorial Optimization Computational Efficiency +4

End-to-End Pareto Set Prediction with Graph Neural Networks for Multi-objective Facility Location

no code implementations27 Oct 2022 Shiqing Liu, Xueming Yan, Yaochu Jin

The network outputs are then converted into the probability distribution of the Pareto set, from which a set of non-dominated solutions can be sampled non-autoregressively.

Combinatorial Optimization

A Survey on Computationally Efficient Neural Architecture Search

no code implementations3 Jun 2022 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 without rich expertise to benefit from the success of deep neural networks (DNNs).

Computational Efficiency Neural Architecture Search +1

Federated Learning on Non-IID Data: A Survey

no code implementations12 Jun 2021 Hangyu Zhu, Jinjin Xu, Shiqing Liu, Yaochu Jin

Federated learning is an emerging distributed machine learning framework for privacy preservation.

BIG-bench Machine Learning Survey +1

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