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Active Learning and Uncertainty Estimation

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Machine Learning Meets Quantum Physics

Part of the book series: Lecture Notes in Physics ((LNP,volume 968))

Abstract

Active learning refers to collections of algorithms of systematically constructing the training dataset. It is closely related to uncertainty estimation—we, generally, do not need to train our model on samples on which our prediction already has low uncertainty. This chapter reviews active learning algorithms in the context of molecular modeling and illustrates their applications on practical problems.

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Notes

  1. 1.

    Small because evaluation of \(\bar {f}(x_i)\) is expensive.

  2. 2.

    Here we have implicitly assumed that the distribution of f(θ, x) has zero mean.

  3. 3.

    To be precise, it could be mathematically proved that only a limited number configurations will be added to the training set if the configurations are sampled from a distribution with a compact support.

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Acknowledgements

The work was supported by the Skoltech NGP Program No. 2016-7/NGP (a Skoltech-MIT joint project). The authors acknowledge the usage of the Skoltech CEST cluster (Magnus) from Prof. Shapeev’s group for obtaining the results presented in this work.

Author information

Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Center for Energy Science and Technology, Moscow, Russia

    Alexander Shapeev, Konstantin Gubaev, Evgenii Tsymbalov & Evgeny Podryabinkin

  2. Present Address: Materials Science and Engineering, Delft University of Technology, Delft, The Netherlands

    Konstantin Gubaev

  3. Skolkovo Institute of Science and Technology, Center for Computational and Data-Intensive Science and Engineering, Moscow, Russia

    Evgenii Tsymbalov

Authors
  1. Alexander Shapeev
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  2. Konstantin Gubaev
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  3. Evgenii Tsymbalov
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  4. Evgeny Podryabinkin
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Corresponding author

Correspondence to Alexander Shapeev .

Editor information

Editors and Affiliations

  1. Machine Learning, Technical University of Berlin, Berlin, Germany

    Kristof T. SchĂĽtt

  2. Machine Learning Group, Technical University of Berlin, Berlin, Germany

    Stefan Chmiela

  3. Institute of Physical Chemistry and MARVEL, University of Basel, Basel, Switzerland

    O. Anatole von Lilienfeld

  4. Department of Physics and Materials Science, University of Luxembourg, Luxembourg, Luxembourg

    Alexandre Tkatchenko

  5. Graduate School of Frontier Sciences, University of Tokyo, Kashiwa, Japan

    Koji Tsuda

  6. Computer Science, Technical University of Berlin, Berlin, Germany

    Klaus-Robert MĂĽller

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Shapeev, A., Gubaev, K., Tsymbalov, E., Podryabinkin, E. (2020). Active Learning and Uncertainty Estimation. In: SchĂĽtt, K., Chmiela, S., von Lilienfeld, O., Tkatchenko, A., Tsuda, K., MĂĽller, KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham. https://doi.org/10.1007/978-3-030-40245-7_15

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