---
title: "Who cites Sepp Hochreiter — 231 papers of our library"
description: "231 papers of the Elamur Research library cite the works of Sepp Hochreiter, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/sepp-hochreiter/cited-by/"
---
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# Who cites Sepp Hochreiter

232 citations of their works from 231 papers of our library (of the 149,439 whose bibliographies we read) · 26 with a page here, the most cited first · [← Sepp Hochreiter](https://md.research.elamur.ai/free/en/authors/sepp-hochreiter/)

1. [**Graph Attention Networks arXiv:1710.10903**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 9,035 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
2. **[Learning Transferable Architectures for Scalable Image Recognition](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,583 citations*
3. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
4. **[DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding](https://md.research.elamur.ai/free/en/papers/shen-2017-disan-directional-self-attention-network/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Tao Shen, Tianyi Zhou, Guodong Long, et al.*2017 · 854 citations*
5. **[SoundStream: An End-to-End Neural Audio Codec](https://md.research.elamur.ai/free/en/papers/zeghidour-2021-soundstream-end-end-neural-audio/) [IEEE/ACM Transactions on Audio Speech and Language Processing](https://md.research.elamur.ai/free/en/venues/ieee-acm-transactions-on-audio-speech-and-language-processing/)**Neil Zeghidour, Alejandro Luebs, Ahmed Omran, et al.*2021 · 733 citations*
6. [**Rethinking Attention with Performers arXiv:2009.14794**Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, et al.*2020 · 491 citations*](https://md.research.elamur.ai/free/en/papers/choromanski-2020-rethinking-attention-performers/)
7. [**High Fidelity Neural Audio Compression arXiv:2210.13438**Alexandre Defossez, Jade Copet, Gabriel Synnaeve, et al.*2022 · 414 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2022-high-fidelity-neural-audio-compression/)
8. **[FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision](https://md.research.elamur.ai/free/en/papers/shah-2024-flashattention-3-fast-accurate-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jay Shah, Ganesh Bikshandi, Ying Zhang, et al.*2024 · 400 citations*
9. [**RULER: What's the Real Context Size of Your Long-Context Language Models? arXiv:2404.06654**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 400 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/)
10. [**Learning Latent Dynamics for Planning from Pixels arXiv:1811.04551**Danijar Hafner, Timothy Lillicrap, Ian Fischer, et al.*2018 · 394 citations*](https://md.research.elamur.ai/free/en/papers/hafner-2018-learning-latent-dynamics-planning-pixels/)
11. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/)
12. [**Thinking Fast and Slow with Deep Learning and Tree Search arXiv:1705.08439**Thomas Anthony, Zheng Tian, David Barber*2017 · 281 citations*](https://md.research.elamur.ai/free/en/papers/anthony-2017-thinking-fast-slow-deep-learning/)
13. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
14. [**Moshi: a speech-text foundation model for real-time dialogue arXiv:2410.00037**Alexandre Defossez, Laurent Mazare, Manu Orsini, et al.*2024 · 157 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2024-moshi-speech-text-foundation-model/)
15. [**Efficient Processing of Deep Neural Networks: A Tutorial and Survey Proceedings of the IEEE 2017**Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, et al.*2017 · 146 citations*](https://md.research.elamur.ai/free/en/papers/sze-2017-efficient-processing-deep-neural-networks/)
16. **[GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems](https://md.research.elamur.ai/free/en/papers/huang-2020-grade-automatic-graph-enhanced-coherence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lishan Huang, Zheng Ye, Jinghui Qin, et al.*2020 · 144 citations*
17. [**Linear Transformers Are Secretly Fast Weight Programmers arXiv:2102.11174**Imanol Schlag, Kazuki Irie, Jurgen Schmidhuber*2021 · 111 citations*](https://md.research.elamur.ai/free/en/papers/schlag-2021-linear-transformers-secretly-fast-weight/)
18. [**Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention arXiv:2404.07143**Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/munkhdalai-2024-leave-no-context-behind-efficient/)
19. [**SpeechTokenizer: Unified Speech Tokenizer for Speech Large Language Models arXiv:2308.16692**Xin Zhang, Dong Zhang, Shimin Li, et al.*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-speechtokenizer-unified-speech-tokenizer-speech/)
20. [**A Mention-Ranking Model for Abstract Anaphora Resolution**Ana Marasović, Leo Born, Juri Opitz, et al.*2017 · 43 citations*](https://md.research.elamur.ai/free/en/papers/marasovic-2017-mention-ranking-model-abstract-anaphora/)
21. [**Baichuan-M1: Pushing the Medical Capability of Large Language Models arXiv:2502.12671**Bingning Wang, Haizhou Zhao, Huozhi Zhou, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-baichuan-m1-pushing-medical/)
22. [**MoM: Linear Sequence Modeling with Mixture-of-Memories arXiv:2502.13685**Jusen Du, Weigao Sun, Disen Lan, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/du-2025-mom-linear-sequence-modeling-mixture/)
23. **[AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling](https://md.research.elamur.ai/free/en/papers/shi-2026-affectcodec-emotion-preserving-neural-speech/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiacheng Shi, Hongfei Du, Xinyuan Song, et al.*2026*
24. **[Efficient Learned Data Compression via Dual-Stream Feature Decoupling](https://md.research.elamur.ai/free/en/papers/ma-2026-efficient-learned-data-compression-dual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huidong Ma, Xinyan Shi, Sun Hui, et al.*2026*
25. **[Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach](https://md.research.elamur.ai/free/en/papers/nishide-2026-mitigating-degree-bias-hypergraphs-attribute/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ryusei Nishide, Makoto Miwa*2026*
26. **[Similarity-Distance-Magnitude Activations](https://md.research.elamur.ai/free/en/papers/schmaltz-2026-similarity-distance-magnitude-activations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Allen Schmaltz*2026*

205 more citing papers of our library have no page here yet.

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