🧑‍🔬 Who cites Brenden M. Lake

225 citations of their works from 221 papers of our library (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← Brenden M. Lake

1. Neural Architecture Search with Reinforcement LearningarXiv:1611.01578Barret Zoph, Quoc V. Le2016 · 4,413 citations
2. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,160 citations
3. Relational inductive biases, deep learning, and graph networksarXiv:1806.01261Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.2018 · 2,335 citations
4. Calibrate Before Use: Improving Few-Shot Performance of Language ModelsarXiv:2102.09690Tony Z. Zhao, Eric Wallace, Shi Feng, et al.2021 · 825 citations
5. Learning multiple visual domains with residual adaptersarXiv:1705.08045Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi2017 · 694 citations
6. Harnessing Deep Neural Networks with Logic RulesAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiting Hu, Xuezhe Ma, Zhengzhong Liu, et al.2016 · 627 citations
7. Meta-Learning for Low-Resource Neural Machine TranslationConference on Empirical Methods in Natural Language Processing (EMNLP)Jiatao Gu, Yong Wang, Yun Chen, et al.2018 · 456 citations
8. Deep Learning: A Critical AppraisalarXiv:1801.00631Gary Marcus2018 · 417 citations
9. Are Emergent Abilities of Large Language Models a Mirage?Conference on Neural Information Processing Systems (NeurIPS)Rylan Schaeffer, Brando Miranda, Sanmi Koyejo2023 · 342 citations
10. Diverse Few-Shot Text Classification with Multiple MetricsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Mo Yu, Xiaoxiao Guo, Jinfeng Yi, et al.2018 · 291 citations
11. Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection NetworkAnnual Meeting of the Association for Computational Linguistics (ACL)Yutai Hou, Wanxiang Che, Yongkui Lai, et al.2020 · 265 citations
12. Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language ModelsFindings of the Association for Computational LinguisticsRobert Logan IV, Ivana Balazevic, Eric Wallace, et al.2022 · 216 citations
13. Compositionality decomposed: how do neural networks generalise?arXiv:1908.08351Dieuwke Hupkes, Verna Dankers, Mathijs Mul, et al.2019 · 162 citations
14. Abstraction and analogy-making in artificial intelligenceMelanie Mitchell2021 · 161 citations
15. Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding TasksConference on Empirical Methods in Natural Language Processing (EMNLP)Zi-Yi Dou, Keyi Yu, Antonios Anastasopoulos2019 · 150 citations
16. Scaling Laws for TransferarXiv:2102.01293Danny Hernandez, Jared Kaplan, Tom Henighan, et al.2021 · 118 citations
17. GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoningZilong Zheng, Shuwen Qiu, Lifeng Fan, et al.2021 · 36 citations
18. Language Models as Models of LanguagearXiv:2408.07144Raphael Milliere2024 · 21 citations
19. Is Child-Directed Speech Effective Training Data for Language Models?Conference on Empirical Methods in Natural Language Processing (EMNLP)Steven Y. Feng, Noah D. Goodman, Michael C. Frank2024 · 18 citations
20. Child-Directed Language Does Not Consistently Boost Syntax Learning in Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, et al.2025 · 9 citations
21. Automated detection of pronunciation errors in non-native English speech employing deep learningarXiv:2209.06265Daniel Korzekwa2022 · 1 citation
22. Child-directed speech facilitates production, not comprehension, in BabyLMsConference on Computational Natural Language Learning (CoNLL)Bastian Bunzeck, Sina Zarrieß2026
23. Examining Algebraic Recombination for Compositional GeneralisationWorkshop on Cognitive Modeling and Computational Linguistics (CMCL)Joaquin Cardona Ruiz, Antske Fokkens, Lucia Donatelli2026

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