https://github.com/rootlu/MetaLearning-Papers

Papers on meta-learning
https://github.com/rootlu/MetaLearning-Papers

graph-neural-networks meta-learning paper-list shot-classification shot-learning

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Papers on meta-learning

• Host: GitHub
• URL: https://github.com/rootlu/MetaLearning-Papers
• Owner: rootlu
• Created: 2020-02-19T09:44:35.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-02-19T09:48:30.000Z (almost 6 years ago)
• Last Synced: 2024-07-24T03:11:47.842Z (over 1 year ago)
• Topics: graph-neural-networks, meta-learning, paper-list, shot-classification, shot-learning
• Size: 8.79 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Papers on Meta-learning 

1. A perspective view and survey of meta-learning. 

   Vilalta R, Drissi Y. Artificial intelligence. 2002.

   https://link.springer.com/article/10.1023/A:1019956318069

2. Siamese Neural Networks for One-shot Image Recognition

   Gregory Koch, Richard Zemel, Ruslan Salakhutdinov. ICML 2015.

   https://arxiv.org/abs/1712.08036

3. Meta-Learning with Memory-Augmented Neural Networks

   Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap. ICML 2016 

   https://dl.acm.org/citation.cfm?id=3045585

4. Matching Networks for One Shot Learning

   Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, Daan Wierstra. NIPS 2016.

   http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning

5. Model-agnostic meta-learning for fast adaptation of deep networks

   Finn C, Abbeel P, Levine S. ICML 2017.

   https://dl.acm.org/citation.cfm?id=3305498

6. A meta-learning perspective on cold-start recommendations for items

   Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle. NIPS 2017.

   http://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items.pdf

7. Prototypical networks for few-shot learning

   Snell J, Swersky K, Zemel R.  NIPS 2017.

   http://papers.nips.cc/paper/6996-prototypical-networks-for-few-shot-learning.pdf

8. Meta-learning: A survey 

   Vanschoren J. arXiv, 2018.

   https://arxiv.org/pdf/1810.03548

9. Learning to compare: Relation network for few-shot learning

   Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales. CVPR 2018. 

   http://openaccess.thecvf.com/content_cvpr_2018/papers/Sung_Learning_to_Compare_CVPR_2018_paper.pdf

10. FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

    Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, Maosong Sun. EMNLP 2018.

    https://arxiv.org/abs/1810.10147

11. One-Shot Relational Learning for Knowledge Graphs

    Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang. EMNLP 2018.

    https://arxiv.org/abs/1808.09040

12. A Simple Neural Attentive Meta-Learner

    Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel. ICLR 2018.

    https://arxiv.org/abs/1707.03141

13. Few-Shot Learning with Graph Neural Networks

    Victor Garcia, Joan Bruna. ICLR 2018.

    https://arxiv.org/abs/1711.04043

14. Meta-Learning for Semi-Supervised Few-Shot Classification

    Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel. ICLR 2018.

    https://arxiv.org/abs/1803.00676

15. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace

    Yoonho Lee, Seungjin Choi. ICML 2018.

    https://arxiv.org/abs/1801.05558

16. MetaGAN: An Adversarial Approach to Few-Shot Learning

    Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio,Yangqiu Song. NIPS 2018.

    http://papers.nips.cc/paper/7504-metagan-an-adversarial-approach-to-few-shot-learning

17. Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification

    Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun. AAAI 2018.

    https://gaotianyu1350.github.io/assets/aaai2019_hatt_paper.pdf

18. Adversarial Meta-Learning

    Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang. arXiv 2019.

    https://arxiv.org/abs/1806.03316

19. Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning

    Junjie Wang, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenjie Zhang, Hongyuan Zha. arXiv 2019.

    https://arxiv.org/abs/1911.09046

20. Hierarchical Meta Learning

    Yingtian Zou, Jiashi Feng. arXiv 2019.

    https://arxiv.org/abs/1904.09081

21. Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks

    Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos. EMNLP 2019.

    https://arxiv.org/abs/1908.10423

22. Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification

    Jiawei Wu, Wenhan Xiong, William Yang Wang. EMNLP 2019.

    https://arxiv.org/abs/1909.04176

23. Meta-Learning of Neural Architectures for Few-Shot Learning

    Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter. arXiv 2019.

    https://arxiv.org/abs/1911.11090

24. Meta-Learning to Cluster

    Yibo Jiang, Nakul Verma. arXiv 2019.

    https://arxiv.org/abs/1910.14134

25. MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

    Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Tim Kwang-Ting Cheng, Jian Sun. ICCV 2019.

    https://arxiv.org/abs/1903.10258

26. Adversarial Attacks on Graph Neural Networks via Meta Learning

    Daniel Zügner, Stephan Günnemann. ICLR 2019.

    https://arxiv.org/abs/1902.08412

27. Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning

    Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang. ICLR 2019.

    https://arxiv.org/abs/1805.10002

28. Meta-Learning Update Rules for Unsupervised Representation Learning

    Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein. ICLR 2019.

    https://arxiv.org/abs/1804.00222

29. Meta-Learning with Latent Embedding Optimization

    Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell. ICLR 2019.

    https://arxiv.org/abs/1807.05960

30. Unsupervised Learning via Meta-Learning

    Kyle Hsu, Sergey Levine, Chelsea Finn. ICLR 2019.

    https://arxiv.org/abs/1810.02334

31. Hierarchically Structured Meta-learning

    Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li. ICML 2019.

    https://arxiv.org/abs/1905.05301

32. Online Meta-Learning

    Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine. ICML 2019.

    https://arxiv.org/abs/1902.08438

33. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation

Hoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, Sehee Chung. KDD 2019.

    https://dl.acm.org/citation.cfm?id=3330859

34. MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records

    Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge, Jiayu Zhou, Fei Wang. KDD 2019.

    https://arxiv.org/abs/1905.03218

35. Sequential Scenario-Specific Meta Learner for Online Recommendation

    Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang. KDD 2019.

    https://arxiv.org/abs/1906.00391

36. Learning to Propagate for Graph Meta-Learning

    LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang. NIPS 2019.

    http://papers.nips.cc/paper/8389-learning-to-propagate-for-graph-meta-learning

37. Learning to Self-Train for Semi-Supervised Few-Shot Classification

    Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou ,Shibao Zheng, Tat-Seng Chua, Bernt Schiele. NIPS 2019.

    http://papers.nips.cc/paper/9216-learning-to-self-train-for-semi-supervised-few-shot-classification

38. Meta-Learning with Implicit Gradients

    Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine. NIPS 2019.

    http://papers.nips.cc/paper/8306-meta-learning-with-implicit-gradients

39. Ranking architectures using meta-learning

    Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent. NIPS 2019.

    https://arxiv.org/abs/1911.11481

40. Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

    Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He. SIGIR 2019.

    https://arxiv.org/abs/1904.11547

41. Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection

    Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, Huajun Chen. WSDM 2020.

    https://arxiv.org/abs/1910.11621