---
title: "Who cites Linear attention is (maybe) all you — 9 citing papers"
description: "9 papers of the Elamur Research library cite «Linear attention is (maybe) all you need (to understand transformer optimization)», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Linear attention is (maybe) all you need…](https://md.research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/) / cited by

# Who cites «Linear attention is (maybe) all you need (to understand transformer optimization)»

9 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Linear attention is (maybe) all you need (to understand transformer optimization)»](https://md.research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/)

1. **[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*
2. [**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/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/) — Linear attention is (maybe) all you need (to understand transformer optimization)