---
title: "Who cites Matching Networks for One Shot — 343 citing papers"
description: "343 papers of the Elamur Research library cite «Matching Networks for One Shot Learning», found in their bibliographies; the most cited first."
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---
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# Who cites «Matching Networks for One Shot Learning»

343 papers of our library cite it (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← back to «Matching Networks for One Shot Learning»](https://md.research.elamur.ai/free/en/papers/vinyals-2016-matching-networks-one-shot-learning/)

1. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
2. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
3. [**Continual Lifelong Learning with Neural Networks: A Review arXiv:1802.07569**German I. Parisi, Ronald Kemker, Jose L. Part, et al.*2018 · 3,224 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/)
4. [**Flamingo: a Visual Language Model for Few-Shot Learning arXiv:2204.14198**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,246 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
5. [**Atlas: Few-shot Learning with Retrieval Augmented Language Models arXiv:2208.03299**Gautier Izacard, Patrick Lewis, Maria Lomeli, et al.*2022 · 673 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2022-atlas-few-shot-learning-retrieval/)
6. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges arXiv:1907.05019**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 564 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
7. [**RL²: Fast Reinforcement Learning via Slow Reinforcement Learning arXiv:1611.02779**Yan Duan, John Schulman, Xi Chen, et al.*2016 · 524 citations*](https://md.research.elamur.ai/free/en/papers/duan-2016-rl-2-fast-reinforcement-learning/)
8. **[Meta-Learning for Low-Resource Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gu-2018-meta-learning-low-resource-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiatao Gu, Yong Wang, Yun Chen, et al.*2018 · 456 citations*
9. **[Security and Privacy Challenges of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 424 citations*
10. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
11. [**Progress & Compress: A scalable framework for continual learning arXiv:1805.06370**Jonathan Schwarz, Jelena Luketina, Wojciech M. Czarnecki, et al.*2018 · 338 citations*](https://md.research.elamur.ai/free/en/papers/schwarz-2018-progress-compress-scalable-framework-continual/)
12. **[Diverse Few-Shot Text Classification with Multiple Metrics](https://md.research.elamur.ai/free/en/papers/yu-2018-diverse-few-shot-text-classification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mo Yu, Xiaoxiao Guo, Jinfeng Yi, et al.*2018 · 291 citations*
13. **[Induction Networks for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/geng-2019-induction-networks-few-shot-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruiying Geng, Binhua Li, Yongbin Li, et al.*2019 · 263 citations*
14. [**Multimodal Few-Shot Learning with Frozen Language Models arXiv:2106.13884**Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, et al.*2021 · 258 citations*](https://md.research.elamur.ai/free/en/papers/tsimpoukelli-2021-multimodal-few-shot-learning-frozen/)
15. **[SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking](https://md.research.elamur.ai/free/en/papers/lee-2019-sumbt-slot-utterance-matching-universal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hwaran Lee, Jinsik Lee, Tae-Yoon Kim*2019 · 258 citations*
16. **[Personalizing Dialogue Agents via Meta-Learning](https://md.research.elamur.ai/free/en/papers/madotto-2019-personalizing-dialogue-agents-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu, et al.*2019 · 246 citations*
17. **[Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces](https://md.research.elamur.ai/free/en/papers/rios-2018-few-shot-zero-shot-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anthony Rios, Ramakanth Kavuluru*2018 · 246 citations*
18. **[CONTaiNER: Few-Shot Named Entity Recognition via Contrastive Learning](https://md.research.elamur.ai/free/en/papers/das-2022-container-few-shot-named-entity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca Passonneau, et al.*2022 · 234 citations*
19. **[Zero-Shot Cross-Lingual Transfer with Meta Learning](https://md.research.elamur.ai/free/en/papers/nooralahzadeh-2020-zero-shot-cross-lingual-transfer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, et al.*2020 · 171 citations*
20. **[Dataset Condensation with Gradient Matching](https://md.research.elamur.ai/free/en/papers/zhao-2020-dataset-condensation-gradient-matching/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Bo Zhao, Konda Reddy Mopuri, Hakan Bilen*2020 · 159 citations*
21. [**AutoML-GPT: Automatic Machine Learning with GPT arXiv:2305.02499**Shujian Zhang, Chengyue Gong, Lemeng Wu, et al.*2023 · 52 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-automl-gpt-automatic-machine-learning/)
22. **[Template-assisted Contrastive Learning of Task-oriented Dialogue Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/oh-2026-template-assisted-contrastive-learning-task/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minsik Oh, Jiwei Li, Guoyin Wang*2026 · 1 citation*
23. **[Decoupling Generalization and Adaptation in Meta-Learning for Large Language Models](https://md.research.elamur.ai/free/en/papers/vetcha-2026-decoupling-generalization-adaptation-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nitin Vetcha, Binqian Xu, Dianbo Liu*2026*
24. **[Dual-Axis Compositional Contrastive Few-Shot Learning using Prototypes Across Linguistic and Semantic Dimensions for Indic Low-Resource Multilingual NLU](https://md.research.elamur.ai/free/en/papers/mitra-2026-dual-axis-compositional-contrastive-few/) [Workshop on Language Technology for Equality, Diversity, Inclusion](https://md.research.elamur.ai/free/en/venues/ltedi/)**Kathakali Mitra, Sakshi Singh, Sree Nithish Reddy Gunapati, et al.*2026*

319 more papers of our library cite it; they have no page here yet.

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