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Chapter

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Title

Curriculum Learning Revisited: Incremental Batch Learning with Instance Typicality Ranking

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2021

Chapter type

chapter in monograph / paper

Publication language

english

Keywords
EN
  • curriculum learning
  • typicality
  • batch training
Abstract

EN The technique of curriculum learning mimics cognitive mechanisms observed in human learning, where simpler concepts are presented prior to gradual introduction of more difficult concepts. Until now, the major obstacle for curriculum methods was the lack of a reliable method for estimating the difficulty of training instances. In this paper we show that, instead of trying to assess the difficulty of learning instances, a simple graph-based method of computing the typicality of instances can be used in conjunction with curriculum methods. We design new batch schedulers which organize ordered instances into batches of varying size and learning difficulty. Our method does not require any changes to the architecture of trained models, we improve the training merely by manipulating the order and frequency of instance presentation to the model.

Pages (from - to)

279 - 291

DOI

10.1007/978-3-030-86380-7_23

URL

https://link.springer.com/chapter/10.1007/978-3-030-86380-7_23

Book

Artificial Neural Networks and Machine Learning – ICANN 2021 : 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV

Presented on

30th International Conference on Artificial Neural Networks and Machine Learning (ICANN 2021), 14-17.09.2021, Bratislava, Slovac Republic

Ministry points / chapter

20

Ministry points / conference (CORE)

70

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