A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 9
IEEE/CAA Journal of Automatica Sinica
| Citation: | B. X. Weng, J. Sun, G. Huang, F. Deng, G. Wang, and J. Chen, “Competitive meta-learning,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1902–1904, Sept. 2023. doi: 10.1109/JAS.2023.123354
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J. Yin, D. Zhou, L. Zhang, J. Fang, C.-Z. Xu, J. Shen, and W. Wang, “ProposalContrast: Unsupervised pre-training for lidar-based 3D object detection,” in Proc. Eur. Conf. Comput. Vision, 2022, pp. 17–33.
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M. Mazouchi, S. Nageshrao, and H. Modares, “Conflict-aware safe reinforcement learning: A meta-cognitive learning framework,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 466–481, 2021.
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J. Chen, J. Sun, and G. Wang, “From unmanned systems to autonomous intelligent systems,” Eng., vol. 12, pp. 16–19, 2022. doi: 10.1016/j.eng.2021.10.007
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Y. Li, X. Wang, J. Sun, G. Wang, and J. Chen, “Data-driven consensus control of fully distributed event-triggered multi-agent systems,” Sci. China Inf. Sci., vol. 66, no. 5, p. 152202, 2023.
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X. Wang, J. Sun, G. Wang, F. Allgöwer, and J. Chen, “Data-driven control of distributed event-triggered network systems,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 1–14, 2023.
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C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in Proc. Int. Conf. Mach. Learn., 2017, pp. 1126–1135.
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B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” arXiv preprint arXiv:1611.01578, 2016.
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A. Nichol, J. Achiam, and J. Schulman, “On first-order meta-learning algorithms,” arXiv preprint arXiv: 1803.02999, 2018.
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Z. Li, F. Zhou, F. Chen, and H. Li, “Meta-SGD: Learning to learn quickly for few-shot learning,” arXiv preprint arXiv: 1707.09835, 2017.
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A. Antoniou, A. Storkey, and H. Edwards, “How to train your MAML,” in Proc. Int. Conf. Learn. Rep., 2019, pp. 1–11.
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H. Liu, R. Socher, and C. Xiong, “Taming MAML: Efficient unbiased meta-reinforcement learning,” in Proc. Int. Conf. Mach. Learn., 2019, pp. 4061–4071.
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H. S. Behl, A. G. Baydin, and P. H. Torr, “Alpha MAML: Adaptive model-agnostic meta-learning,” arXiv preprint arXiv: 1905.07435, 2019.
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R. Cheng and Y. Jin, “A competitive swarm optimizer for large scale optimization,” IEEE Trans. Cybern., vol. 45, no. 2, pp. 191–204, 2014.
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B. Xin, J. Chen, J. Zhang, H. Fang, and Z.-H. Peng, “Hybridizing differential evolution and particle swarm optimization to design powerful optimizers: A review and taxonomy,” IEEE Trans. Syst. Man Cyb.,Part C, vol. 42, no. 5, pp. 744–767, 2011.
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S. Ravi and H. Larochelle, “Optimization as a model for few-shot learning,” in Proc. Int. Conf. Learn. Rep., 2017, pp. 1–11.
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “OpenAI Gym,” arXiv preprint arXiv: 1606.01540, 2016.
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E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for modelbased control,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst., 2012, pp. 5026–5033.
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J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv: 1707.06347, 2017.
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