---
title: "Who cites Efficient softmax approximation — 66 citing papers"
description: "66 papers of the Elamur Research library cite «Efficient softmax approximation for GPUs», found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/grave-2016-efficient-softmax-approximation-gpus/cited-by/"
---
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# Who cites «Efficient softmax approximation for GPUs»

66 papers of our library cite it (of the 141,840 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Efficient softmax approximation for GPUs»](https://md.research.elamur.ai/free/en/papers/grave-2016-efficient-softmax-approximation-gpus/)

1. **[fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,951 citations*
2. [**Language Modeling with Gated Convolutional Networks arXiv:1612.08083**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,315 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
3. [**FastText.zip: Compressing text classification models arXiv:1612.03651**Armand Joulin, Edouard Grave, Piotr Bojanowski, et al.*2016 · 1,214 citations*](https://md.research.elamur.ai/free/en/papers/joulin-2016-fasttext-zip-compressing-text-classification/)
4. [**Reducing Transformer Depth on Demand with Structured Dropout arXiv:1909.11556**Angela Fan, Edouard Grave, Armand Joulin*2019 · 554 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
5. [**Pay Less Attention with Lightweight and Dynamic Convolutions arXiv:1901.10430**Felix Wu, Angela Fan, Alexei Baevski, et al.*2019 · 424 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
6. **[Learning to Write with Cooperative Discriminators](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 301 citations*
7. **[HAT: Hardware-Aware Transformers for Efficient Natural Language Processing](https://md.research.elamur.ai/free/en/papers/wang-2020-hat-hardware-aware-transformers-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hanrui Wang, Zhanghao Wu, Zhijian Liu, et al.*2020 · 290 citations*
8. **[Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2020-structured-pruning-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ziheng Wang, Jeremy Wohlwend, Tao Lei*2020 · 197 citations*
9. **[XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://md.research.elamur.ai/free/en/papers/liang-2023-xlm-v-overcoming-vocabulary-bottleneck/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Davis Liang, Hila Gonen, Yuning Mao, et al.*2023 · 117 citations*
10. [**Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English arXiv:1810.13088**Yan Yin, Ramon Prieto, Bin Wang, et al.*2018 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-attention-based-sequence-sequence-model/)

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

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