Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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Detecting and quantifying crowd-level abnormal behaviors in crowd events
L Luo, S Xie, H Yin, C Peng, YS Ong. Cited by 22
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Mechanism-aware and multimodal AI: beyond model-agnostic interpretation
A Occhipinti, S Verma, C Angione. Cited by 24
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Deep structural knowledge exploitation and synergy for estimating node importance value on heterogeneous information networks
Y Chen, Y Fang, Q Wang, X Cao, I King. Cited by 16
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A diffusion-based pre-training framework for crystal property prediction
Z Song, Z Meng, I King. Cited by 20
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Deep generative design of RNA aptamers using structural predictions
F Wong, D He, A Krishnan, L Hong, AZ Wang, J Wang, Z Hu, S Omori, A Li, et al. Cited by 84
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Mome: Mixture of multimodal experts for cancer survival prediction
C Xiong, H Chen, H Zheng, D Wei, Y Zheng, JJY Sung, I King. Cited by 36
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Differentiable clustering for graph attention
H Zhou, T He, YS Ong, G Cong, Q Chen. Cited by 46
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A survey of trustworthy federated learning: Issues, solutions, and challenges
Y Zhang, D Zeng, J Luo, X Fu, G Chen, Z Xu, I King. Cited by 81
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Accurate RNA 3D structure prediction using a language model-based deep learning approach
T Shen, Z Hu, S Sun, D Liu, F Wong, J Wang, J Chen, Y Wang, L Hong, et al. Cited by 284
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A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
M Jin, HY Koh, Q Wen, D Zambon, C Alippi, GI Webb, I King, S Pan. Cited by 506
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An Uncertainty Estimation Model for Health Signal Prediction
LR Wang, TC Henderson, YS Ong, YY Ng, X Fan. Cited by 3
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Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection
J Kongmanee, M Chignell, K Jerath, A Raman. Cited by 1