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- W4289489395 abstract "Syllabuses for curriculum learning have been developed on an ad-hoc, per task basis and little is known about the relative performance of different syllabuses. We identify a number of syllabuses used in the literature. We compare the identified syllabuses based on their effect on the speed of learning and generalization ability of a LSTM network on three sequential learning tasks. We find that the choice of syllabus has limited effect on the generalization ability of a trained network. In terms of speed of learning our results demonstrate that the best syllabus is task dependent but that a recently proposed automated curriculum learning approach - Predictive Gain, performs very competitively against all identified hand-crafted syllabuses. The best performing hand-crafted syllabus which we term Look Back and Forward combines a syllabus which steps through tasks in the order of their difficulty with a uniform distribution over all tasks. Our experimental results provide an empirical basis for the choice of syllabus on a new problem that could benefit from curriculum learning. Additionally, insights derived from our results shed light on how to successfully design new syllabuses." @default.
- W4289489395 created "2022-08-02" @default.
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- W4289489395 date "2018-09-27" @default.
- W4289489395 modified "2023-09-26" @default.
- W4289489395 title "An Empirical Comparison of Syllabuses for Curriculum Learning" @default.
- W4289489395 doi "https://doi.org/10.48550/arxiv.1809.10789" @default.
- W4289489395 hasPublicationYear "2018" @default.
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