Search Results for author: Zhengyang Zhou

Found 10 papers, 1 papers with code

FairSTG: Countering performance heterogeneity via collaborative sample-level optimization

no code implementations19 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 implementations4 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

Graph Multi-Similarity Learning for Molecular Property Prediction

no code implementations31 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.

Attribute Contrastive Learning +5

Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model

no code implementations13 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.

Molecular Identification and Peak Assignment: Leveraging Multi-Level Multimodal Alignment on NMR

no code implementations23 Nov 2023 Hao Xu, Zhengyang Zhou, Pengyu Hong

Nuclear magnetic resonance (NMR) spectroscopy plays an essential role in deciphering molecular structure and dynamic behaviors.

Contrastive Learning Meta-Learning +1

Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation

1 code implementation29 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.

Decision Making Fairness

Graph-Free Learning in Graph-Structured Data: A More Efficient and Accurate Spatiotemporal Learning Perspective

no code implementations27 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.

Graph Learning

Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics

no code implementations17 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.

Uncertainty Quantification

STUaNet: Understanding uncertainty in spatiotemporal collective human mobility

no code implementations9 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.

Uncertainty Quantification

RiskOracle: A Minute-level Citywide Traffic Accident Forecasting Framework

no code implementations19 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).

Management

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