1. Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: 34th International Conference on Machine Learning, ICML 2017, vol. 5, pp. 3933–3943. International Machine Learning Society (IMLS) (2017). https://arxiv.org/abs/1703.03400
2. Lux, F., Vu N, T.: Language-agnostic meta-learning for low-resource text-to-speech with articulatory features. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics 2022, vol. 1, pp. 6858–6868. Association for Computational Linguistics (ACL) (2022). https://arxiv.org/abs/2203.03191
3. Gu, J., Wang, Y. Chen, Y.: Meta-learning for low-resource neural machine translation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, pp. 3622–3631. Association for Computational Linguistics (2018). https://arxiv.org/abs/1808.08437
4. Abbas, M., Xiao, Q., Chen, L.: Sharp-MAML: sharpness-aware model-agnostic meta learning (2022)
5. Song, X., Gao W, Yang, Y.: ES-MAML: simple hessian-free meta learning (2019)