Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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Towards global explanations of convolutional neural networks with concept attribution
W Wu, Y Su, X Chen, S Zhao, I King, MR Lyu, YW Tai. Cited by 70
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Time series forecasting with multi-headed attention-based deep learning for residential energy consumption
SJ Bu, SB Cho. Cited by 91
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Online Deep Clustering for Unsupervised Representation Learning
X Zhan, J Xie, Z Liu, YS Ong, CC Loy. Cited by 267
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When Gaussian process meets big data: A review of scalable GPs
H Liu, YS Ong, X Shen, J Cai. Cited by 1107
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Large-Scale Heteroscedastic Regression via Gaussian Process
H Liu, YS Ong, J Cai. Cited by 27
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Evolutionary Multiagent Transfer Learning With Model-Based Opponent Behavior Prediction
Y Hou, YS Ong, J Tang, Y Zeng. Cited by 18
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Feature selection in GSNFS-based marker identification
S Kozuevanich, JH Chan, A Meechai. Cited by 2
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The Blessing of Dimensionality in Many-Objective Search: An Inverse Machine Learning Insight
A Gupta, YS Ong, M Shakeri, C Xu, AZ NengSheng. Cited by 9
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Coping with Big Data in Transfer Optimization
M Shakeri, A Gupta, YS Ong, C Xu, AZ NengSheng. Cited by 9
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Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning
JY Kim, SB Cho. Cited by 37
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Manifold Regularized Stochastic Block Model
T He, L Bai, YS Ong. Cited by 10
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Tuning a Cancer Patient Typology Based on Emergency Department Visits
M Rouzbahman, L Wang, M Chignell, L Zucherman, N Charoenkitkarn, LC Barbera. Cited by 1