no code implementations • 24 Apr 2024 • Zhiqi Shao, Xusheng Yao, Ze Wang, Junbin Gao
This paper introduces ST-MambaSync, an innovative traffic flow prediction model that combines transformer technology with the ST-Mamba block, representing a significant advancement in the field.
no code implementations • 20 Apr 2024 • Zhiqi Shao, Michael G. H. Bell, Ze Wang, D. Glenn Geers, Haoning Xi, Junbin Gao
Traffic flow prediction, a critical aspect of intelligent transportation systems, has been increasingly popular in the field of artificial intelligence, driven by the availability of extensive traffic data.
no code implementations • 26 Mar 2024 • Zhiqi Shao, Michael G. H. Bell, Ze Wang, D. Glenn Geers, Xusheng Yao, Junbin Gao
Accurate, and effective traffic forecasting is vital for smart traffic systems, crucial in urban traffic planning and management.
no code implementations • 6 Sep 2023 • Zhiqi Shao, Dai Shi, Andi Han, Yi Guo, Qibin Zhao, Junbin Gao
To explore more flexible filtering conditions, we further generalize MHKG into a model termed G-MHKG and thoroughly show the roles of each element in controlling over-smoothing, over-squashing and expressive power.
1 code implementation • 19 Jul 2023 • Dai Shi, Yi Guo, Zhiqi Shao, Junbin Gao
Motivated by the geometric analogy of Ricci curvature in the graph setting, we prove that by inserting the curvature information with different carefully designed transformation function $\zeta$, several known computational issues in GNN such as over-smoothing can be alleviated in our proposed model.
1 code implementation • 13 Jul 2023 • Dai Shi, Zhiqi Shao, Yi Guo, Junbin Gao
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing too much prediction accuracy.
no code implementations • 25 May 2023 • Dai Shi, Zhiqi Shao, Yi Guo, Qibin Zhao, Junbin Gao
We conduct a convergence analysis on pL-UFG, addressing the gap in the understanding of its asymptotic behaviors.
1 code implementation • 27 Oct 2022 • Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, Junbin Gao
This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN.
no code implementations • 8 Oct 2022 • Andi Han, Dai Shi, Zhiqi Shao, Junbin Gao
In this work, we provide a theoretical understanding of the framelet-based graph neural networks through the perspective of energy gradient flow.