no code implementations • 19 Mar 2024 • Gengyu Lin, Zhengyang Zhou, Qihe Huang, Kuo Yang, Shifen Cheng, Yang Wang
To fix this gap, we propose a model-independent Fairness-aware framework for SpatioTemporal Graph learning (FairSTG), which inherits the idea of exploiting advantages of well-learned samples to challenging ones with collaborative mix-up.
no code implementations • 4 Mar 2024 • Zhengyang Zhou, Qihe Huang, Binwu Wang, Jianpeng Hou, Kuo Yang, Yuxuan Liang, Yang Wang
Motivated by complementary learning in neuroscience, we introduce a prompt-based complementary spatiotemporal learning termed ComS2T, to empower the evolution of models for data adaptation.
no code implementations • 31 Jan 2024 • Hao Xu, Zhengyang Zhou, Pengyu Hong
Additionally, previous multi-similarity approaches require the specification of positive and negative pairs to attribute distinct predefined weights to different relative similarities, which can introduce potential bias.
no code implementations • 13 Dec 2023 • Hao Wu, Shilong Wang, Yuxuan Liang, Zhengyang Zhou, Wei Huang, Wei Xiong, Kun Wang
Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community.
no code implementations • 23 Nov 2023 • Hao Xu, Zhengyang Zhou, Pengyu Hong
Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors.
1 code implementation • 29 May 2023 • Yifei Wang, Zhengyang Zhou, Liqin Wang, John Laurentiev, Peter Hou, Li Zhou, Pengyu Hong
The confounding factors, which are non-sensitive variables but manifest systematic differences, can significantly affect fairness evaluation.
no code implementations • 27 Jan 2023 • Xu Wang, Pengfei Gu, Pengkun Wang, Binwu Wang, Zhengyang Zhou, Lei Bai, Yang Wang
In this paper, with extensive and deep-going experiments, we comprehensively analyze existing spatiotemporal graph learning models and reveal that extracting adjacency matrices with carefully design strategies, which are viewed as the key of enhancing performance on graph learning, are largely ineffective.
no code implementations • 17 Aug 2022 • Zhengyang Zhou, Yang Kuo, Wei Sun, Binwu Wang, Min Zhou, Yunan Zong, Yang Wang
To infer region-wise proximity under flexible factor-wise combinations and enable dynamic neighborhood aggregations, we further disentangle compounded influences of exogenous factors on region-wise proximity and learn to aggregate them.
no code implementations • 9 Feb 2021 • Zhengyang Zhou, Yang Wang, Xike Xie, Lei Qiao, Yuantao Li
The high dynamics and heterogeneous interactions in the complicated urban systems have raised the issue of uncertainty quantification in spatiotemporal human mobility, to support critical decision-makings in risk-aware web applications such as urban event prediction where fluctuations are of significant interests.
no code implementations • 19 Feb 2020 • Zhengyang Zhou, Yang Wang, Xike Xie, Lianliang Chen, Hengchang Liu
Real-time traffic accident forecasting is increasingly important for public safety and urban management (e. g., real-time safe route planning and emergency response deployment).