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2020 – today
- 2025
[j4]Dai Shi, Andi Han, Lequan Lin, Yi Guo
, Zhiyong Wang, Junbin Gao:
Design your own universe: a physics-informed agnostic method for enhancing graph neural networks. Int. J. Mach. Learn. Cybern. 16(2): 1129-1144 (2025)- [c5]Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao:
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning. ICLR 2025 - [c4]Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao:
When Graph Neural Networks Meet Dynamic Mode Decomposition. ICLR 2025 - [c3]Lequan Lin, Dai Shi, Andi Han, Junbin Gao:
SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. IJCNN 2025: 1-10 - [i13]Dai Shi, Kuan Yan, Lequan Lin, Yue Zeng, Ting Zhang, Dmytro Matsypura, Mark C. Gillies, Ling Zhu, Junbin Gao:
Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond. CoRR abs/2502.06126 (2025) - [i12]Feng Chen, Yefei He, Lequan Lin, Jing Liu, Bohan Zhuang, Qi Wu:
ZipR1: Reinforcing Token Sparsity in MLLMs. CoRR abs/2504.18579 (2025) - 2024
- [j3]Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao:
Diffusion models for time-series applications: a survey. Frontiers Inf. Technol. Electron. Eng. 25(1): 19-41 (2024) - [j2]Chunya Zou, Andi Han, Lequan Lin, Ming Li, Junbin Gao:
A Simple Yet Effective Framelet-Based Graph Neural Network for Directed Graphs. IEEE Trans. Artif. Intell. 5(4): 1647-1657 (2024) - [j1]Andi Han, Dai Shi, Lequan Lin, Junbin Gao:
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. Trans. Mach. Learn. Res. 2024 (2024) - [c2]Jiayu Zhai, Lequan Lin, Dai Shi, Junbin Gao:
Bregman Graph Neural Network. ICASSP 2024: 6250-6254 - [i11]Lequan Lin, Dai Shi, Andi Han, Junbin Gao:
SpecSTG: A Fast Spectral Diffusion Framework for Probabilistic Spatio-Temporal Traffic Forecasting. CoRR abs/2401.08119 (2024) - [i10]Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao:
Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks. CoRR abs/2401.14580 (2024) - [i9]Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao:
Unleash Graph Neural Networks from Heavy Tuning. CoRR abs/2405.12521 (2024) - [i8]Dai Shi, Lequan Lin, Andi Han, Zhiyong Wang, Yi Guo, Junbin Gao:
When Graph Neural Networks Meet Dynamic Mode Decomposition. CoRR abs/2410.05593 (2024) - [i7]Lequan Lin, Dai Shi, Andi Han, Zhiyong Wang, Junbin Gao:
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning. CoRR abs/2410.05697 (2024) - 2023
- [c1]Lequan Lin, Junbin Gao:
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs. ICASSP 2023: 1-5 - [i6]Lequan Lin, Zhengkun Li, Ruikun Li, Xuliang Li, Junbin Gao:
Diffusion Models for Time Series Applications: A Survey. CoRR abs/2305.00624 (2023) - [i5]Jiayu Zhai, Lequan Lin, Dai Shi, Junbin Gao:
Bregman Graph Neural Network. CoRR abs/2309.06645 (2023) - [i4]Andi Han, Dai Shi, Lequan Lin, Junbin Gao:
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. CoRR abs/2310.10121 (2023) - [i3]Dai Shi, Andi Han, Lequan Lin, Yi Guo, Junbin Gao:
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges. CoRR abs/2311.07073 (2023) - 2022
- [i2]Chunya Zou, Andi Han, Lequan Lin, Junbin Gao:
A Simple Yet Effective SVD-GCN for Directed Graphs. CoRR abs/2205.09335 (2022) - [i1]Lequan Lin, Junbin Gao:
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs. CoRR abs/2210.10993 (2022)
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