Funder
National Natural Science Foundation of China
Reference58 articles.
1. Meta-learning in neural networks: A survey;Hospedales;IEEE Trans. Pattern Anal. Mach. Intell.,2022
2. Meta learning with graph attention networks for low-data drug discovery;Lv;IEEE Trans. Neural Netw. Learn. Syst.,2023
3. Z. Zhan, X. Zhang, Computation-Effective Personalized Federated Learning: A Meta Learning Approach, in: 2023 IEEE 43rd International Conference on Distributed Computing Systems, ICDCS, 2023, pp. 957–958.
4. A.I. Griva, A.D. Boursianis, L.A. Iliadis, P. Sarigiannidis, G. Karagiannidis, S.K. Goudos, Model-Agnostic Meta-Learning Techniques: A State-of-The-Art Short Review, in: 2023 12th International Conference on Modern Circuits and Systems Technologies, MOCAST, 2023, pp. 1–4.
5. T. Stark, M. Wurm, X.X. Zhu, H. Taubenböck, Detecting challenging urban environments using a few-shot meta-learning approach, in: 2023 Joint Urban Remote Sensing Event, JURSE, 2023, pp. 1–4.