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.
|Title of host publication||Lecture Notes in Physics|
|Number of pages||21|
|Publication status||Published - 2020|
|Name||Lecture Notes in Physics|