FairSTG: Countering performance heterogeneity via collaborative sample-level optimization

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.

Fairness Graph Learning +1

ComS2T: A complementary spatiotemporal learning system for data-adaptive model evolution

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.

Hippocampus