Výsledky vyhledávání - "Wen, Yanlong"

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EffiCANet: Efficient Time Series Forecasting with Convolutional Attention

Autor: Zhou, Xinxing, Ye, Jiaqi, Zhao, Shubao, Jin, Ming, Yang, Chengyi, Wen, Yanlong, Yuan, Xiaojie

The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models. Current deep learning methods often fail to adequately capture lon

Externí odkaz: http://arxiv.org/abs/2411.04669

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Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting

Autor: Zhou, Xinxing, Ye, Jiaqi, Zhao, Shubao, Jin, Ming, Hou, Zhaoxiang, Yang, Chengyi, Li, Zengxiang, Wen, Yanlong, Yuan, Xiaojie

In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional forecasting methods struggle to cope with the growing complexity and variabi

Externí odkaz: http://arxiv.org/abs/2409.15794

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Graffin: Stand for Tails in Imbalanced Node Classification

Autor: Qi, Xiaorui, Wen, Yanlong, Yuan, Xiaojie

Graph representation learning (GRL) models have succeeded in many scenarios. Real-world graphs have imbalanced distribution, such as node labels and degrees, which leaves a critical challenge to GRL. Imbalanced inputs can lead to imbalanced outputs.

Externí odkaz: http://arxiv.org/abs/2409.05339

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Node-like as a Whole: Structure-aware Searching and Coarsening for Graph Classification

Autor: Qi, Xiaorui, Bai, Qijie, Wen, Yanlong, Zhang, Haiwei, Yuan, Xiaojie

Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on node-central perspectives and lac

Externí odkaz: http://arxiv.org/abs/2404.11869

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TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding

Autor: Liu, Zichen, Liu, Xuyuan, Wen, Yanlong, Zhao, Guoqing, Xia, Fen, Yuan, Xiaojie

ICD coding is designed to assign the disease codes to electronic health records (EHRs) upon discharge, which is crucial for billing and clinical statistics. In an attempt to improve the effectiveness and efficiency of manual coding, many methods have

Externí odkaz: http://arxiv.org/abs/2305.18576

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