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Xutao Li
Researcher at Harbin Institute of Technology
Publications - 129
Citations - 3297
Xutao Li is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 19, co-authored 88 publications receiving 2063 citations. Previous affiliations of Xutao Li include Nanyang Technological University.
Papers
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Proceedings Article
Personalized ranking metric embedding for next new POI recommendation
TL;DR: This paper proposes a personalized ranking metric embedding method (PRME) to model personalized check-in sequences and develops a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance.
Proceedings ArticleDOI
Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation
TL;DR: A ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges of scarcity of check-in data and context information, and outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
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Hyperspectral Image Classification With Deep Learning Models
TL;DR: This paper advocates four new deep learning models, namely, 2-D convolutional neural network, 3-D-CNN, recurrent 2- D CNN, recurrent R-2-D CNN, and recurrent 3- D-CNN for hyperspectral image classification.
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Road Detection and Centerline Extraction Via Deep Recurrent Convolutional Neural Network U-Net
TL;DR: The experimental results demonstrate the superiority of the proposed RCNN-UNet model for both the road detection and the centerline extraction tasks, and a multitask learning scheme is designed so that two predictors can be simultaneously trained to improve both effectiveness and efficiency.
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Stratified sampling for feature subspace selection in random forests for high dimensional data
TL;DR: A stratified sampling method to select the feature subspaces for random forests with high dimensional data to better that of state-of-the-art algorithms including SVM, the four variants of random forests, and nearest neighbor algorithms.