🔗 Who cites «One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention»

20 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention»

1. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
2. Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning TasksarXiv:2402.04248Jongho Park, Jaeseung Park, Zheyang Xiong, et al.2024 · 41 citations
3. Transformers learn to implement preconditioned gradient descent for in-context learningConference on Neural Information Processing Systems (NeurIPS)Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, et al.2023 · 37 citations
4. Understanding Emergent In-Context Learning from a Kernel Regression PerspectivearXiv:2305.12766Chi Han, Ziqi Wang, Han Zhao, et al.2023 · 21 citations
5. Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear RegressionConference on Neural Information Processing Systems (NeurIPS)Deqing Fu, Tian-Qi Chen, Robin Jia, et al.2023 · 14 citations
6. Linear attention is (maybe) all you need (to understand transformer optimization)arXiv:2310.01082Kwangjun Ahn, Xiang Cheng, Minhak Song, et al.2023 · 10 citations
7. Learning to (Learn at Test Time)arXiv:2310.13807Yu Sun, Xinhao Li, Karan Dalal, et al.2023 · 6 citations
8. Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic TasksarXiv:2406.01895Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.2024 · 5 citations

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

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