Active Learning and Uncertainty Estimation

Alexander Shapeev, Konstantin Gubaev, Evgenii Tsymbalov, Evgeny Podryabinkin

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Physics
PublisherSpringer
Pages309-329
Number of pages21
DOIs
Publication statusPublished - 2020

Publication series

NameLecture Notes in Physics
Volume968
ISSN (Print)0075-8450
ISSN (Electronic)1616-6361

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