G
Guangmin Hu
Researcher at University of Electronic Science and Technology of China
Publications - 120
Citations - 602
Guangmin Hu is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Computer science & Sparse approximation. The author has an hindex of 10, co-authored 90 publications receiving 349 citations.
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Unsupervised seismic facies analysis via deep convolutional autoencoders
TL;DR: In this paper, the most important goal of seismic stratigraphy studies is to interpret the elements of the seismic facies with respect to the geologic environment, and the results of the study are presented.
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Multi-waveform classification for seismic facies analysis
TL;DR: A new seismic facies analysis algorithm represented as multi-waveform classification (MWFC) that combines the multilinear subspace learning with self-organizing map (SOM) clustering techniques is developed that reduces the uncertainty of facies maps in the boundaries.
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A data-driven amplitude variation with offset inversion method via learned dictionaries and sparse representation
TL;DR: A novel data-driven inversion method for the AVO inversion problem that can effectively extract useful knowledge from well-log data, including sparse dictionaries of elastic parameters and sparse representation of subsurface model parameters.
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Semi-supervised internet network traffic classification using a Gaussian mixture model
TL;DR: This paper proposes a Gaussian mixture model (GMM)-based semi-supervised classification system to identify different internet applications and achieves an optimum configuration for the GMM-based Semi-Supervised Classification system.
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Learning Graph Topological Features via GAN
TL;DR: In this article, a hierarchical architecture consisting of multiple GANs preserves both local and global topological features and automatically partitions the input graph into representative stages for feature learning, which can be used as indicators of the importance of the associated topological structures.