Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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An unsupervised feature selection by back-propagated weighting the non-Gaussianity score of independence components
W Modecrua, P Padungweang, W Krathu
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A Density Discriminant Index for Cluster Validation
S Thanarattananakin, P Padungweang, W Krathu. Cited by 1
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A Combined Framework for Dimensionality Reduction of Hyperspectral Images using Feature Selection and Feature Extraction
S Ghosh, P Pramanik. Cited by 6
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Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods
H Liu, YS Ong, Z Yu, J Cai, X Shen. Cited by 2
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Making Online Sketching Hashing Even Faster
X Chen, H Yang, S Zhao, MRT Lyu, I King. Cited by 23
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STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems
J Zhang, X Shi, S Zhao, I King. Cited by 246
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Contextual Correlation Preserving Multiview Featured Graph Clustering
T He, Y Liu, TH Ko, KCC Chan, YS Ong. Cited by 55
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DDFlow: Learning Optical Flow with Unlabeled Data Distillation
P Liu, I King, MRT Lyu, X Jia. Cited by 177
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Machine and deep learning meet genome-scale metabolic modeling
G Zampieri, S Vijayakumar, E Yaneske, C Angione. Cited by 332
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A Knowledge Discovery of Relationships among Dataset Entities Using Optimum Hierarchical Clustering by DE Algorithm
S Mahdavi, S Rahnamayan, K Deb, M Rahnamayan. Cited by 2
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Deep Validation: Toward Detecting Real-World Corner Cases for Deep Neural Networks
W Wu, HF Xu, S Zhong, MRT Lyu, I King. Cited by 49
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SelFlow: Self-Supervised Learning of Optical Flow
P Liu, MRT Lyu, I King, X Jia. Cited by 342