Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification-Reference-Cited by-同舟云学术

Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification

Published:2020-10-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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Author:

Ma Ning¹,Bu Jiajun¹,Yang Jieyu¹,Zhang Zhen¹,Yao Chengwei¹,Yu Zhi¹,Zhou Sheng¹,Yan Xifeng²

Affiliation:

1. Zhejiang University & Alibaba-Zhejiang University Joint Institute of Frontier Technologies, Hangzhou, UNK, China

2. University of California, Santa Barbara, Santa Barbara, CA, USA

Funder

National Natural Science Foundation of China

Alibaba-Zhejiang University Joint Institute of Frontier Technologies, The National Key R&D Program of China

Zhejiang Provincial Natural Science Foundation of China

National Key Technology R&D Program of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3340531.3411951

Reference58 articles.

1. Antreas Antoniou Harrison Edwards and Amos Storkey. 2019. How to train your MAML. In ICLR. Antreas Antoniou Harrison Edwards and Amos Storkey. 2019. How to train your MAML. In ICLR.

2. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results;Bartlett Peter L.;J. Mach. Learn. Res.,2003

3. Harkirat Singh Behl and Philip H. S. Torr. 2019. Alpha MAML: Adaptive Model-Agnostic Meta-Learning. arxiv: cs.LG/1905.07435 Harkirat Singh Behl and Philip H. S. Torr. 2019. Alpha MAML: Adaptive Model-Agnostic Meta-Learning. arxiv: cs.LG/1905.07435

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