Computer Applications and Software, Volume. 42, Issue 4, 27(2025)

CURRICULUM LEARNING STRATEGIES BASED ON POWER SERVICE CENTERS AND SIMILAR VENUES

Li Chenguang1, Zhang Bo2, Zhao Qian2, and Chen Xiaoping1
Author Affiliations
  • 1College of Computer Science and Technology, The University of Science and Technology of China, Hefei 230026, Anhui, China
  • 2State Grid Anhui Electric Power Company Limited, Hefei 230022, Anhui, China
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    References(14)

    [1] [1] Bengio Y, Louradour J, Collobert R, et al. Curriculum learning[C]//26th Annual International Conference on Machine Learning, 2009: 41-48.

    [2] [2] Tsvetkov Y, Faruqui M, Ling W, et al. Learning the curriculum with Bayesian optimization for task-specific word representation learning[C]//54th Annual Meeting of the Association for Computational Linguistics, 2016: 130-139.

    [3] [3] Cirik V, Hovy E, Morency L P. Visualizing and understanding curriculum learning for long short-term memory networks[EB]. arXiv: 1611.06204, 2016.

    [4] [4] Graves A, Bellemare M G, Menick J, et al. Automated curriculum learning for neural networks[C]//34th International Conference on Machine Learning, 2017: 1311-1320.

    [6] [6] Spitkovsky V I, Alshawi H, Jurafsky D. From baby steps to leapfrog: How "less is more" in unsupervised dependency parsing[C]//Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2010: 751-759.

    [7] [7] Bengio Y. Practical recommendations for gradient-based training of deep architectures[M]//Neural Networks: Tricks of the Trade. Heidelberg: Springer, 2012: 437-478.

    [8] [8] Amiri H, Miller T, Savova G. Repeat before forgetting: Spaced repetition for efficient and effective training of neural networks[C]//Conference on Empirical Methods in Natural Language Processing, 2017: 2401-2410.

    [9] [9] Collobert R, Weston J. A unified architecture for natural language processing: Deep neural networks with multitask learning[C]//25th International Conference on Machine Learning, 2008: 160-167.

    [10] [10] Kiperwasser E, Ballesteros M. Scheduled multi-task learning: From syntax to translation[J]. Transactions of the Association for Computational Linguistics, 2018, 6: 225-240.

    [11] [11] Weinshall D, Cohen G, Amir D. Curriculum learning by transfer learning: Theory and experiments with deep networks[C]//35th International Conference on Machine Learning, 2018: 5238-5246.

    [12] [12] Matiisen T, Oliver A, Cohen T, et al. Teacher-student curriculum learning[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(9): 3732-3740.

    [13] [13] Portelas R, Colas C, Hofmann K, et al. Teacher algorithms for curriculum learning of deep RL in continuously parameterized environments[EB]. arXiv: 1910.07224, 2020.

    [14] [14] Fan Y, Tian F, Qin T, et al. Learning to teach[EB]. arX-iv: 1805.03643, 2018.

    [15] [15] Jiang L, Meng D Y, Zhao Q, et al. Self-paced curriculum learning[C]//AAAI Conference on Artificial Intelligence, 2015: 1203-1211.

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    Li Chenguang, Zhang Bo, Zhao Qian, Chen Xiaoping. CURRICULUM LEARNING STRATEGIES BASED ON POWER SERVICE CENTERS AND SIMILAR VENUES[J]. Computer Applications and Software, 2025, 42(4): 27

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    Paper Information

    Category:

    Received: Sep. 12, 2021

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email:

    DOI:10.3969/j.issn.1000-386x.2025.04.005

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