Mingming Lu
Central South University
16 Papers
18 Citations
Mingming Lu is an academic researcher from Central South University. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 4, co-authored 11 publications.
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Papers
Urban Traffic Flow Forecast Based on FastGCRNN
Ya Zhang,Mingming Lu,Haifeng Li +2 more
TL;DR: Wang et al. as mentioned in this paper proposed a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow, which can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.
MMHGE: detecting mild cognitive impairment based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning
TL;DR: This work proposes a new MCI detection framework based on multi-atlas multi-view hybrid graph convolutional networks and ensemble learning, which can get better performance than some state-of-the-art methods aboutMCI detection.
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•Posted Content
Program Classification Using Gated Graph Attention Neural Network for Online Programming Service.
TL;DR: A Graph Neural Network (GNN) based model is proposed, which integrates data flow and function call information to the AST, and an improved GNN model is applied to the integrated graph, so as to achieve the state-of-art program classification accuracy.
The purpose driven privacy preservation for accelerometer-based activity recognition
TL;DR: The method leverages a connection to agglomerative information bottleneck, through which the amount of disclosed data can be compressed so that irrelevant private information can be reduced, and a connected to general privacy statistical inference framework, where both of the privacy leakage and utility accuracy are considered as mutual information.
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Vehicle Re-Identification Based on UAV Viewpoint: Dataset and Method
TL;DR: This work proposes a Global Attention and full-Scale Network (GASNet) for the vehicle ReID task based on UAV images, which consists of 172,137 images of 15,085 vehicles captured by UAVs, which has a larger volume and is fully open-source.