T
Tao Lin
Researcher at Zhejiang University
Publications - 58
Citations - 2978
Tao Lin is an academic researcher from Zhejiang University. The author has contributed to research in topics: Computer science & Biomass. The author has an hindex of 16, co-authored 49 publications receiving 1145 citations. Previous affiliations of Tao Lin include University of Illinois at Urbana–Champaign.
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T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction
TL;DR: In this article, a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is combined with the graph convolutionsal network and the gated recurrent unit (GRU), is proposed.
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DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis
TL;DR: An end-to-end deep learning approach incorporated Inception module, named DeepSpectra, is presented to learn patterns from raw data to improve the model performance and shows improved results than conventional linear and nonlinear calibration approaches in most scenarios.
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Deep learning for vibrational spectral analysis: Recent progress and a practical guide.
TL;DR: This work offers a new solution for chemometrics toward resolving challenges related to spectral data with rapidly increased sample numbers from various sources and provides a practical guide to the development of a deep convolutional neural network-based analytical workflow.
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Modeling real-time human mobility based on mobile phone and transportation data fusion
TL;DR: A novel human mobility model that combines the advantages of mobile phone signaling data and urban transportation data is proposed and a predictive model is deployed to predict crowd gatherings that usually cause severe traffic jams.
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A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level
Hao Jiang,Hao Hu,Renhai Zhong,Jinfan Xu,Jialu Xu,Jingfeng Huang,Shaowen Wang,Yibin Ying,Tao Lin +8 more
TL;DR: A long short-term memory (LSTM) model is developed that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county-level corn yields and showed that the period from silking to dough was most critical for crop yield estimation.