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Curriculum learning (CL) posits that machine learning models -- similar to humans -- may learn more efficiently from data that match their current learning progress.
Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Numerical continuation methods: an introduction , volume 13
Allgower, E. L.; and Georg, K. 1980 · 1980
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Learning and development in neural networks: The importance of starting small
Elman, J. L. 1993 · 1993
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Language acquisition in the absence of explicit negative evidence: How important is starting small?
Rohde, D. L.; and Plaut, D. C. 1999 · 1999
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Automatically constructing a corpus of sentential paraphrases
Dolan, B.; and Brockett, C. 2005 · 2005
Earlier work this paper cites.
Curriculum learning
Bengio, Y.; Louradour, J.; Collobert, R.; and Weston, J. 2009 · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Flexible shaping: How learning in small steps helps
Krueger, K. A.; and Dayan, P. 2009 · 2009
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Baby Steps: How “Less is More” in unsupervised dependency parsing
Spitkovsky, V. I.; Alshawi, H.; and Jurafsky, D. 2009 · 2009
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Self-paced learning for latent variable models
Kumar, M.; Packer, B.; and Koller, D. 2010 · 2010
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From baby steps to leapfrog: How “less is more” in unsupervised dependency parsing
Spitkovsky, V. I.; Alshawi, H.; and Jurafsky, D. 2010 · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
Cited alongside, same era.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Cited alongside, same era.
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
Kocmi, T.; and Bojar, O. 2017 · 2017
Cited alongside, same era.
Parsing Universal Dependencies without training
Martínez Alonso, H.; Agić, Ž.; Plank, B.; and Søgaard, A. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Learning to Teach
Fan, Y.; Tian, F.; Qin, T.; Li, X.-Y.; and Liu, T.-Y. 2018 · 2018
Cited alongside, same era.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Ott, M.; Edunov, S.; Baevski, A.; Fan, A.; Gross, S.; Ng, N.; Grangier, D.; and Auli, M. 2019 · 2019
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Competence-based Curriculum Learning for Neural Machine Translation
Platanios, E. A.; Stretcu, O.; Neubig, G.; Póczos, B.; and Mitchell, T. 2019 · 2019
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Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives
Tay, Y.; Wang, S.; Luu, A. T.; Fu, J.; Phan, M. C.; Yuan, X.; Rao, J.; Hui, S. C.; and Zhang, A. 2019 · 2019
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Curriculum learning for reinforcement learning domains: A framework and survey
Narvekar, S.; Peng, B.; Leonetti, M.; Sinapov, J.; Taylor, M. E.; and Stone, P. 2020 · 2020
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Curriculum learning strategies for ir
Penha, G.; and Hauff, C. 2020 · 2020
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Teaching with Commentaries
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Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Jiang, L.; Zhou, Z.; Leung, T.; Li, L.-J.; and Fei-Fei, L. 2018 · 2018
Cited alongside, same era.
Screenernet: Learning self-paced curriculum for deep neural networks
Kim, T.-H.; and Choi, J. 2018 · 2018
Cited alongside, same era.
Curriculum Learning for Natural Answer Generation
Liu, C.; He, S.; Liu, K.; Zhao, J.; et al. 2018 · 2018
Cited alongside, same era.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. R. 2018 · 2018
Cited alongside, same era.
On the power of curriculum learning in training deep networks
Hacohen, G.; and Weinshall, D. 2019 · 2019
Cited alongside, same era.
Raghu, A.; Raghu, M.; Kornblith, S.; Duvenaud, D.; and Hinton, G. 2020 · 2020
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Curriculum learning for natural language understanding
Xu, B.; Zhang, L.; Mao, Z.; Wang, Q.; Xie, H.; and Zhang, Y. 2020 · 2020
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Curriculum learning for language modeling
Campos, D. 2021 · 2021
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A survey on curriculum learning
Wang, X.; Chen, Y.; and Zhu, W. 2021 · 2021
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Curriculum learning: A survey
Soviany, P.; Ionescu, R. T.; Rota, P.; and Sebe, N. 2022 · 2022
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Do Data-based Curricula Work?
Surkov, M.; Mosin, V.; and Yamshchikov, I. 2022 · 2022
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