---
title: "Who cites One Step of Gradient Descent is — 20 citing papers"
description: "20 papers of the Elamur Research library cite «One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear…"
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---
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# 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»](https://md.research.elamur.ai/free/en/papers/mahankali-2023-one-step-gradient-descent-provably/)

1. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
2. [**Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks arXiv:2402.04248**Jongho Park, Jaeseung Park, Zheyang Xiong, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-mamba-learn-learn-comparative-study/)
3. **[Transformers learn to implement preconditioned gradient descent for in-context learning](https://md.research.elamur.ai/free/en/papers/ahn-2023-transformers-learn-implement-preconditioned-gradient/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, et al.*2023 · 37 citations*
4. [**Understanding Emergent In-Context Learning from a Kernel Regression Perspective arXiv:2305.12766**Chi Han, Ziqi Wang, Han Zhao, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-understanding-emergent-context-learning-kernel/)
5. **[Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression](https://md.research.elamur.ai/free/en/papers/fu-2023-transformers-learn-achieve-second-order/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**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:2310.01082**Kwangjun Ahn, Xiang Cheng, Minhak Song, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/)
7. [**Learning to (Learn at Test Time) arXiv:2310.13807**Yu Sun, Xinhao Li, Karan Dalal, et al.*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-learning-learn-test-time/)
8. [**Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks arXiv:2406.01895**Mahdi Sabbaghi, George Pappas, Hamed Hassani, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/sabbaghi-2024-explicitly-encoding-structural-symmetry-key/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/mahankali-2023-one-step-gradient-descent-provably/) — One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention