Integrative, Rapid, Data Analysis
Methods that bring together data from many sources and turn it into timely, usable insight.
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Generalized category discovery with clustering assignment consistency
X Yang, X Pan, I King, Z Xu. Cited by 4
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Heterogeneous multi-party learning with data-driven network sampling
M Gong, Y Gao, Y Wu, Y Zhang, AK Qin, YS Ong. Cited by 11
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Disentangling motives behind item consumption and social connection for mutually-enhanced joint prediction
Y Sun, Z Sun, X Sha, J Zhang, YS Ong. Cited by 8
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Granger causality using Jacobian in neural networks
LY Chew, YS Ong. Cited by 11
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An elitist artificial electric field algorithm based random vector functional link network for cryptocurrency prices forecasting
SC Nayak, S Das, S Dehuri, SB Cho. Cited by 11
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A graph convolution network with subgraph embedding for mutagenic prediction in aromatic hydrocarbons
HJ Moon, SJ Bu, SB Cho. Cited by 11
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Doubly stochastic graph-based non-autoregressive reaction prediction
Z Meng, P Zhao, Y Yu, I King. Cited by 12
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Transfer-recursive-ensemble learning for multi-day COVID-19 prediction in India using recurrent neural networks
D Chakraborty, D Goswami, S Ghosh, A Ghosh, JH Chan, L Wang. Cited by 21
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Drug synergistic combinations predictions via large-scale pre-training and graph structure learning
Z Hu, Q Yu, YX Gao, L Guo, T Song, Y Li, I King. Cited by 15
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Meta-learning with motif-based task augmentation for few-shot molecular property prediction
Z Meng, Y Li, P Zhao, Y Yu, I King. Cited by 16
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Machine learning based prediction of new pareto-optimal solutions from pseudo-weights
A Suresh, K Deb. Cited by 16
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A unified view of deep learning for reaction and retrosynthesis prediction: Current status and future challenges
Z Meng, P Zhao, Y Yu, I King. Cited by 19