Lequan Lin

According to our database, Lequan Lin authored at least 19 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

S³GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning.

Dai Shi, Luke Thompson, Linhan Luo, Lequan Lin, Andi Han, Junbin Gao, José Miguel Hernández-Lobato

CoRR, May, 2026

LOFT: Low-Rank Orthogonal Fine-Tuning via Task-Aware Support Selection.

Lanxin Zhao, Bamdev Mishra, Pratik Jawanpuria, Lequan Lin, Dai Shi, Junbin Gao, Andi Han

CoRR, May, 2026

Expanding the Chaos: Neural Operator for Stochastic (Partial) Differential Equations.

Dai Shi, Lequan Lin, Andi Han, Luke Thompson, José Miguel Hernández-Lobato, Zhiyong Wang, Junbin Gao

CoRR, January, 2026

OmniSparse: Training-Aware Fine-Grained Sparse Attention for Long-Video MLLMs.

Feng Chen, Yefei He, Shaoxuan He, Yuanyu He, Jing Liu, Lequan Lin, Akide Liu, Zhaoyang Li, Jiyuan Zhang, Zhenbang Sun, Bohan Zhuang, Qi Wu

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

ACT as Human: Multimodal Large Language Model Data Annotation with Critical Thinking.

Lequan Lin, Dai Shi, Andi Han, Feng Chen, Qiuzheng Chen, Jiawen Li, Zhaoyang Li, Jiyuan Li, Zhenbang Sun, Junbin Gao

CoRR, November, 2025

ZipR1: Reinforcing Token Sparsity in MLLMs.

Feng Chen, Yefei He, Lequan Lin, Jing Liu, Bohan Zhuang, Qi Wu

CoRR, April, 2025

Design your own universe: a physics-informed agnostic method for enhancing graph neural networks.

Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao

Int. J. Mach. Learn. Cybern., February, 2025

Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond.

Dai Shi, Kuan Yan, Lequan Lin, Yue Zeng, Ting Zhang, Dmytro Matsypura, Mark C. Gillies, Ling Zhu, Junbin Gao

CoRR, February, 2025

SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting.

Lequan Lin, Dai Shi, Andi Han, Junbin Gao

Proceedings of the International Joint Conference on Neural Networks, 2025

When Graph Neural Networks Meet Dynamic Mode Decomposition.

Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning.

Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs.

Chunya Zou, Andi Han, Lequan Lin, Ming Li, Junbin Gao

IEEE Trans. Artif. Intell., April, 2024

Diffusion models for time-series applications: a survey.

Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao

Frontiers Inf. Technol. Electron. Eng., January, 2024

From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond.

Andi Han, Dai Shi, Lequan Lin, Junbin Gao

Trans. Mach. Learn. Res., 2024

Unleash Graph Neural Networks from Heavy Tuning.

Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao

CoRR, 2024

Bregman Graph Neural Network.

Jiayu Zhai, Lequan Lin, Dai Shi, Junbin Gao

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges.

Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao

CoRR, 2023

A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs.

Lequan Lin, Junbin Gao

Proceedings of the IEEE International Conference on Acoustics, 2023

2022

A Simple Yet Effective SVD-GCN for Directed Graphs.

Chunya Zou, Andi Han, Lequan Lin, Junbin Gao

CoRR, 2022