🔗 Who cites «Are Transformers universal approximators of sequence-to-sequence functions?»

61 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «Are Transformers universal approximators of sequence-to-sequence functions?»

1. RoFormer: Enhanced Transformer with Rotary Position EmbeddingarXiv:2104.09864Jianlin Su, Yu Lu, Shengfeng Pan, et al.2021 · 3,028 citations
2. Big Bird: Transformers for Longer SequencesConference on Neural Information Processing Systems (NeurIPS)Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.2020 · 1,023 citations
3. What learning algorithm is in-context learning? Investigations with linear modelsarXiv:2211.15661Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.2022 · 236 citations
4. What Can Transformers Learn In-Context? A Case Study of Simple Function ClassesConference on Neural Information Processing Systems (NeurIPS)Shivam Garg, Dimitris Tsipras, Percy Liang, et al.2022 · 209 citations
5. On the Ability and Limitations of Transformers to Recognize Formal LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP)Satwik Bhattamishra, Kabir Ahuja, Navin Goyal2020 · 113 citations
6. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
7. Formal Language Recognition by Hard Attention Transformers: Perspectives from Circuit ComplexityTransactions of the Association for Computational Linguistics (TACL)Yiding Hao, Dana Angluin, Robert Frank2022 · 62 citations
8. Teaching Arithmetic to Small TransformersarXiv:2307.03381Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.2023 · 57 citations
9. Beyond Memorization: Extending Reasoning Depth with Recurrence, Memory and Test-Time Compute ScalingFindings of the Association for Computational LinguisticsIvan Rodkin, Daniil Orel, Konstantin Smirnov, et al.2026
10. Do Transformers Grok Succinct Algorithms? Mechanistic Evidence for Counting CircuitsFindings of the Association for Computational LinguisticsYizheng Zhao2026
11. Language Acquisition Device in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Masato Mita, Taiga Someya, Ryo Yoshida, et al.2026
12. SCOPE: Boosting LLM Efficiency with Scoped Position EncodingAnnual Meeting of the Association for Computational Linguistics (ACL)Qingguo Qi, Hongyang Chen, Zhao Li2026
13. Understanding the Prompt SensitivityAnnual Meeting of the Association for Computational Linguistics (ACL)Yang Liu, Chenhui Chu2026

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/yun-2019-transformers-universal-approximators-sequence-sequence/cited-by/