Kun Xie
14 Papers
Kun Xie is an academic researcher. The author has contributed to research in topics: Computer science & Exploit. The author has an hindex of 2, co-authored 6 publications.
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Papers
Spatial-Temporal Aware Inductive Graph Neural Network for C-ITS Data Recovery
TL;DR: Wang et al. as discussed by the authors proposed a spatial-temporal aware data recovery network (STAR), which uses a residual gated temporal convolution network to learn the temporal pattern from long sequences with masks and an adaptive memory-based attention model for utilizing implicit spatial correlation.
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Multi-range bidirectional mask graph convolution based GRU networks for traffic prediction
Na Hu,Dafang Zhang,Kun Xie,Wei Liang,Chunyan Diao,Kuan Li +5 more
TL;DR: Wang et al. as mentioned in this paper proposed a bidirectional mask graph convolutional GRU layer, where a mask adaptive adjacency matrix generation algorithm is designed to learn spatial relationships adaptively in the traffic data, and implemented a mask matrix to filter the noise spatial relationships during the adaptive graph learning for more accurate traffic prediction.
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LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning
Yuhui Li,Wei Liang,Kun Xie,Dafang Zhang,Songyou Xie +4 more
- 17 May 2023
TL;DR: LightNestle is proposed, a novel sequential tensor completion scheme based on meta-learning, which designs an expressive neural network to transfer spatial knowledge from previous embeddings to currentembeddings and an attention-based module to transfer temporal patterns into current embedDings in linear complexity.
14
Deep Adversarial Tensor Completion for Accurate Network Traffic Measurement
TL;DR: Wang et al. as mentioned in this paper designed a novel Deep Adversarial Tensor Completion (DATC) scheme based on Deep Learning (DL) techniques to recover the missing data.
9
Multi-graph fusion based graph convolutional networks for traffic prediction
Na Hu,Dafang Zhang,Kun Xie,Wei Liang,Kuan Li,Albert Y. Zomaya +5 more
TL;DR: This study proposes a multi-graph fusion-based graph convolutional network (GFGCN) for traffic prediction, addressing spatial and temporal dependencies with a novel adjacency matrix and temporal module, outperforming baselines on three real-world datasets.
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