rootlu / MetaLearning-Papers

Papers on meta-learning

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