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Aidan N. Gomez

Aidan N. Gomez has 2 papers in our library, in 2017–2018, most of them on Machine translation; often with Ashish Vaswani, Llion Jones, Lukasz Kaiser.

2 papers in our library · 2017–2018

OpenAlex · ORCID · h-index 14 · 29 works · 26,375 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 17,908 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #147

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢CohereSan Francisco · United States2020–2025 · 6 paperscurrent🏢GoogleMountain View · United States2018–2025 · 4 paperscurrent🎓University of OxfordOxford · United Kingdom2019–2022 · 11 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Machine translation · 1 work

💡 Concepts 3Key ideas and methods explained on our concept pages.Artificial intelligence · 1 workNatural language processing · 1 workTransformer and attention · 1 work

 Works by year

2010s

20181 paper20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Attention Is All You Need (2017 · 17,458 citations)
    Sequence-to-sequence models of the time relied on recurrent or convolutional encoders and decoders. Recurrence forces step-by-step computation, which makes training slow, and it makes links between distant words hard to learn. The authors propose the Transformer, an encoder-decoder network that drops recurrence and…
  2. Tensor2Tensor for Neural Machine Translation (2018 · 191 citations)
    This paper presents Tensor2Tensor (T2T), an open-source TensorFlow library from Google. It aims to make deep learning research, especially neural machine translation, faster to run and easier to reproduce. The authors first trace NMT from recurrent sequence-to-sequence models with attention, through convolutional…

All works

8 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Attention Is All You NeedarXiv:1706.03762Ashish Vaswani, Noam Shazeer, Niki Parmar, et al.2017 · 17,458 citations
  2. One Model To Learn Them All ↗arXiv (Cornell University) · no summary yetŁukasz Kaiser et al.2017 · 277 citations
  3. Depthwise Separable Convolutions for Neural Machine Translation ↗arXiv (Cornell University) · no summary yetŁukasz Kaiser, Aidan N. Gomez, Chollet, Francois2017 · 260 citations
  4. Tensor2Tensor for Neural Machine TranslationConference of the Association for Machine Translation in the Americas (AMTA)Ashish Vaswani, Samy Bengio, Eugene Brevdo, et al.2018 · 191 citations
  5. Unsupervised Cipher Cracking Using Discrete GANs ↗arXiv (Cornell University) · no summary yetAidan N. Gomez et al.2018 · 30 citations
  6. Exploring Low Rank Training of Deep Neural Networks ↗arXiv (Cornell University) · no summary yetSiddhartha Rao Kamalakara et al.2022 · 18 citations
  7. Interlocking Backpropagation: Improving depthwise model-parallelism ↗arXiv (Cornell University) · no summary yetAidan N. Gomez et al.2020 · 9 citations
  8. Large Scale Multi-Domain Multi-Task Learning with MultiModel ↗no summary yetŁukasz Kaiser et al.2018

 Co-authors here

12 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 17,908 times (of the 149,439 papers whose bibliographies we read); 1,916 citing papers with a page here →.

Updated

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