J
Jiexia Ye
Researcher at Chinese Academy of Sciences
Publications - 16
Citations - 189
Jiexia Ye is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Graph (abstract data type) & Computer science. The author has an hindex of 3, co-authored 13 publications receiving 47 citations.
Papers
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Journal ArticleDOI
How to Build a Graph-Based Deep Learning Architecture in Traffic Domain: A Survey
TL;DR: This survey carefully examines various graph-based deep learning architectures in many traffic applications to discuss their shared deep learning techniques, clarifying the utilization of each technique in traffic tasks.
Proceedings ArticleDOI
Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting
TL;DR: This paper model the subway system as a directed weighted graph and proposes a novel spatio-temporal deep learning framework, Multi-STGCnet, for forecasting short-term subway passenger flow at a station level, which outperforms multiple baselines.
Proceedings ArticleDOI
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
TL;DR: A deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement, which is feasible to incorporate more effective stock relationships containing expert knowledge, as well as learn data-driven relationship.
Proceedings ArticleDOI
Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
TL;DR: Wang et al. as mentioned in this paper proposed a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent unit (GRU) to predict stock movement.
Patent
Traffic flow prediction method and device based on deep learning
TL;DR: In this paper, a traffic flow prediction method and device based on deep learning is proposed to improve the accuracy of predicting the traffic flow of the road network, where the influence of the spatial-temporal characteristics of the traffic flows of the whole road network on the target node can be more comprehensively considered from the perspective of spatialtemporal correlation.