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Machine Learning of Reactive Potentials.
Annual review of physical chemistry (Print)In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and ...
Yinuo Yang +4 more
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Constructing machine learning potentials with active learning
2023Cheng Shang, Zhi-Pan Liu
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Uncertainty for Active Learning on Graphs
International Conference on Machine LearningUncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models by iteratively acquiring labels of data points with the highest uncertainty.
Dominik Fuchsgruber +4 more
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AL-ELM: One uncertainty-based active learning algorithm using extreme learning machine
Neurocomputing, 2015Hualong Yu +4 more
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Machine learning for microbiologists
Nature Reviews Microbiology, 2023Francesco Asnicar +2 more
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A guide to machine learning for biologists
Nature Reviews Molecular Cell Biology, 2021Joe G Greener +2 more
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Machine learning methods to model multicellular complexity and tissue specificity
Nature Reviews Materials, 2021Aaron K Wong, Olga G Troyanskaya
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Practical Secure Aggregation for Privacy-Preserving Machine Learning
IACR Cryptology ePrint Archive, 2017Keith Bonawitz +8 more
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Machine learning sheds light on microbial dark proteins
Nature Reviews Microbiology, 2023Aaron T Hammack, Crysten E Blaby-Haas
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