{"id":33158595,"url":"https://github.com/rootlu/MetaLearning-Papers","last_synced_at":"2025-11-20T14:02:34.122Z","repository":{"id":106515066,"uuid":"241584848","full_name":"rootlu/MetaLearning-Papers","owner":"rootlu","description":"Papers on meta-learning","archived":false,"fork":false,"pushed_at":"2020-02-19T09:48:30.000Z","size":9,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-07-24T03:11:47.842Z","etag":null,"topics":["graph-neural-networks","meta-learning","paper-list","shot-classification","shot-learning"],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rootlu.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-02-19T09:44:35.000Z","updated_at":"2021-10-28T03:22:45.000Z","dependencies_parsed_at":"2024-01-15T03:26:41.580Z","dependency_job_id":null,"html_url":"https://github.com/rootlu/MetaLearning-Papers","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rootlu/MetaLearning-Papers","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootlu%2FMetaLearning-Papers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootlu%2FMetaLearning-Papers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootlu%2FMetaLearning-Papers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootlu%2FMetaLearning-Papers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rootlu","download_url":"https://codeload.github.com/rootlu/MetaLearning-Papers/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rootlu%2FMetaLearning-Papers/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":285447937,"owners_count":27173436,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-11-20T02:00:05.334Z","response_time":54,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["graph-neural-networks","meta-learning","paper-list","shot-classification","shot-learning"],"created_at":"2025-11-15T21:00:27.010Z","updated_at":"2025-11-20T14:02:34.117Z","avatar_url":"https://github.com/rootlu.png","language":null,"funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"readme":"# Papers on Meta-learning \n\n1. A perspective view and survey of meta-learning. \n   Vilalta R, Drissi Y. Artificial intelligence. 2002.\n   https://link.springer.com/article/10.1023/A:1019956318069\n2. Siamese Neural Networks for One-shot Image Recognition\n   Gregory Koch, Richard Zemel, Ruslan Salakhutdinov. ICML 2015.\n   https://arxiv.org/abs/1712.08036\n3. Meta-Learning with Memory-Augmented Neural Networks\n   Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap. ICML 2016 \n   https://dl.acm.org/citation.cfm?id=3045585\n4. Matching Networks for One Shot Learning\n   Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, Daan Wierstra. NIPS 2016.\n   http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning\n5. Model-agnostic meta-learning for fast adaptation of deep networks\n   Finn C, Abbeel P, Levine S. ICML 2017.\n   https://dl.acm.org/citation.cfm?id=3305498\n6. A meta-learning perspective on cold-start recommendations for items\n   Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle. NIPS 2017.\n   http://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items.pdf\n7. Prototypical networks for few-shot learning\n   Snell J, Swersky K, Zemel R.  NIPS 2017.\n   http://papers.nips.cc/paper/6996-prototypical-networks-for-few-shot-learning.pdf\n8. Meta-learning: A survey \n   Vanschoren J. arXiv, 2018.\n   https://arxiv.org/pdf/1810.03548\n9. Learning to compare: Relation network for few-shot learning\n   Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, Timothy M. Hospedales. CVPR 2018. \n   http://openaccess.thecvf.com/content_cvpr_2018/papers/Sung_Learning_to_Compare_CVPR_2018_paper.pdf\n10. FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation\n    Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, Maosong Sun. EMNLP 2018.\n    https://arxiv.org/abs/1810.10147\n11. One-Shot Relational Learning for Knowledge Graphs\n    Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang. EMNLP 2018.\n    https://arxiv.org/abs/1808.09040\n12. A Simple Neural Attentive Meta-Learner\n    Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, Pieter Abbeel. ICLR 2018.\n    https://arxiv.org/abs/1707.03141\n13. Few-Shot Learning with Graph Neural Networks\n    Victor Garcia, Joan Bruna. ICLR 2018.\n    https://arxiv.org/abs/1711.04043\n14. Meta-Learning for Semi-Supervised Few-Shot Classification\n    Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel. ICLR 2018.\n    https://arxiv.org/abs/1803.00676\n15. Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace\n    Yoonho Lee, Seungjin Choi. ICML 2018.\n    https://arxiv.org/abs/1801.05558\n16. MetaGAN: An Adversarial Approach to Few-Shot Learning\n    Ruixiang ZHANG, Tong Che, Zoubin Ghahramani, Yoshua Bengio,Yangqiu Song. NIPS 2018.\n    http://papers.nips.cc/paper/7504-metagan-an-adversarial-approach-to-few-shot-learning\n17. Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification\n    Tianyu Gao, Xu Han, Zhiyuan Liu, Maosong Sun. AAAI 2018.\n    https://gaotianyu1350.github.io/assets/aaai2019_hatt_paper.pdf\n18. Adversarial Meta-Learning\n    Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang. arXiv 2019.\n    https://arxiv.org/abs/1806.03316\n19. Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning\n    Junjie Wang, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenjie Zhang, Hongyuan Zha. arXiv 2019.\n    https://arxiv.org/abs/1911.09046\n20. Hierarchical Meta Learning\n    Yingtian Zou, Jiashi Feng. arXiv 2019.\n    https://arxiv.org/abs/1904.09081\n21. Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks\n    Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos. EMNLP 2019.\n    https://arxiv.org/abs/1908.10423\n22. Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification\n    Jiawei Wu, Wenhan Xiong, William Yang Wang. EMNLP 2019.\n    https://arxiv.org/abs/1909.04176\n23. Meta-Learning of Neural Architectures for Few-Shot Learning\n    Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, Frank Hutter. arXiv 2019.\n    https://arxiv.org/abs/1911.11090\n24. Meta-Learning to Cluster\n    Yibo Jiang, Nakul Verma. arXiv 2019.\n    https://arxiv.org/abs/1910.14134\n25. MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning\n    Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Tim Kwang-Ting Cheng, Jian Sun. ICCV 2019.\n    https://arxiv.org/abs/1903.10258\n26. Adversarial Attacks on Graph Neural Networks via Meta Learning\n    Daniel Zügner, Stephan Günnemann. ICLR 2019.\n    https://arxiv.org/abs/1902.08412\n27. Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning\n    Yanbin Liu, Juho Lee, Minseop Park, Saehoon Kim, Eunho Yang, Sung Ju Hwang, Yi Yang. ICLR 2019.\n    https://arxiv.org/abs/1805.10002\n28. Meta-Learning Update Rules for Unsupervised Representation Learning\n    Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein. ICLR 2019.\n    https://arxiv.org/abs/1804.00222\n29. Meta-Learning with Latent Embedding Optimization\n    Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell. ICLR 2019.\n    https://arxiv.org/abs/1807.05960\n30. Unsupervised Learning via Meta-Learning\n    Kyle Hsu, Sergey Levine, Chelsea Finn. ICLR 2019.\n    https://arxiv.org/abs/1810.02334\n31. Hierarchically Structured Meta-learning\n    Huaxiu Yao, Ying Wei, Junzhou Huang, Zhenhui Li. ICML 2019.\n    https://arxiv.org/abs/1905.05301\n32. Online Meta-Learning\n    Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine. ICML 2019.\n    https://arxiv.org/abs/1902.08438\n33. MeLU: Meta-Learned User Preference Estimator for Cold-Start Recommendation\nHoyeop Lee, Jinbae Im, Seongwon Jang, Hyunsouk Cho, Sehee Chung. KDD 2019.\n    https://dl.acm.org/citation.cfm?id=3330859\n34. MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records\n    Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge, Jiayu Zhou, Fei Wang. KDD 2019.\n    https://arxiv.org/abs/1905.03218\n35. Sequential Scenario-Specific Meta Learner for Online Recommendation\n    Zhengxiao Du, Xiaowei Wang, Hongxia Yang, Jingren Zhou, Jie Tang. KDD 2019.\n    https://arxiv.org/abs/1906.00391\n36. Learning to Propagate for Graph Meta-Learning\n    LU LIU, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang. NIPS 2019.\n    http://papers.nips.cc/paper/8389-learning-to-propagate-for-graph-meta-learning\n37. Learning to Self-Train for Semi-Supervised Few-Shot Classification\n    Xinzhe Li, Qianru Sun, Yaoyao Liu, Qin Zhou ,Shibao Zheng, Tat-Seng Chua, Bernt Schiele. NIPS 2019.\n    http://papers.nips.cc/paper/9216-learning-to-self-train-for-semi-supervised-few-shot-classification\n38. Meta-Learning with Implicit Gradients\n    Aravind Rajeswaran, Chelsea Finn, Sham M. Kakade, Sergey Levine. NIPS 2019.\n    http://papers.nips.cc/paper/8306-meta-learning-with-implicit-gradients\n39. Ranking architectures using meta-learning\n    Alina Dubatovka, Efi Kokiopoulou, Luciano Sbaiz, Andrea Gesmundo, Gabor Bartok, Jesse Berent. NIPS 2019.\n    https://arxiv.org/abs/1911.11481\n40. Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings\n    Feiyang Pan, Shuokai Li, Xiang Ao, Pingzhong Tang, Qing He. SIGIR 2019.\n    https://arxiv.org/abs/1904.11547\n41. Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection\n    Shumin Deng, Ningyu Zhang, Jiaojian Kang, Yichi Zhang, Wei Zhang, Huajun Chen. WSDM 2020.\n    https://arxiv.org/abs/1910.11621\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frootlu%2FMetaLearning-Papers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frootlu%2FMetaLearning-Papers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frootlu%2FMetaLearning-Papers/lists"}