Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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Modulating scalable Gaussian processes for expressive statistical learning
H Liu, YS Ong, X Jiang, X Wang. Cited by 7
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Modeling Customer Experience in a Contact Center through Process Log Mining
T Fu, G Zampieri, D Hodgson, C Angione, Y Zeng. Cited by 5
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Learning by distillation: a self-supervised learning framework for optical flow estimation
P Liu, MR Lyu, I King, J Xu. Cited by 12
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Online clustering reduction based on parametric and non-parametric correlation for a many-objective vehicle routing problem with demand responsive transport
RS Mendes, V Lush, EF Wanner, FVC Martins, JFM Sarubbi, K Deb. Cited by 16
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Scalable Gaussian process classification with additive noise for non-Gaussian likelihoods
H Liu, YS Ong, Z Yu, J Cai, X Shen. Cited by 19
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Multimodal regularized linear models with flux balance analysis for mechanistic integration of omics data
G Magazzù, G Zampieri, C Angione. Cited by 32
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Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space
JY Kim, SB Cho. Cited by 48
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Evolutionary machine learning with minions: A case study in feature selection
N Zhang, A Gupta, Z Chen, YS Ong. Cited by 54
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Building energy consumption forecasting: A comparison of gradient boosting models
A Bassi, A Shenoy, A Sharma, H Sigurdson, C Glossop, JH Chan. Cited by 52
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Deep character-level anomaly detection based on a convolutional autoencoder for zero-day phishing URL detection
SJ Bu, SB Cho. Cited by 60
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RNA alternative splicing prediction with discrete compositional energy network
A Chan, A Korsakova, YS Ong, FR Winnerdy, KW Lim, AT Phan. Cited by 4
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MD-MTL: An Ensemble Med-Multi-Task Learning Package for DiseaseScores Prediction and Multi-Level Risk Factor Analysis
L Wang, H Jiang, M Chignell. Cited by 1