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Yunming Ye
Researcher at Harbin Institute of Technology
Publications - 192
Citations - 5871
Yunming Ye is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 28, co-authored 152 publications receiving 3727 citations. Previous affiliations of Yunming Ye include Harbin Institute of Technology Shenzhen Graduate School.
Papers
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Proceedings ArticleDOI
DeepFM: a factorization-machine based neural network for CTR prediction
TL;DR: This paper shows that it is possible to derive an end-to-end learning model that emphasizes both low- and high-order feature interactions, and combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture.
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DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
TL;DR: DeepFM as mentioned in this paper combines the power of factorization machines for recommendation and deep learning for feature learning in a new neural network architecture, which has a shared input to its "wide" and "deep" parts.
Journal ArticleDOI
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.
Journal ArticleDOI
TW-k-means: Automated two-level variable weighting clustering algorithm for multiview data
TL;DR: TW-k-means, an automated two-level variable weighting clustering algorithm for multiview data, which can simultaneously compute weights for views and individual variables, significantly outperformed the other five clustering algorithms in four evaluation indices.
Journal ArticleDOI
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.