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Anselm Levskaya

Anselm Levskaya has 2 papers in our library, in 2020–2022, most of them on Efficient models & compression; often with Adam Roberts, Afroz Mohiuddin, Aitor Lewkowycz.

2 papers in our library · 2020–2022

OpenAlex · h-index 14 · 18 works · 6,346 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 7,233 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #2791

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

🎓University of California, San FranciscoSan Francisco · United States2005–2010 · 6 papers🏢GoogleMountain View · United States2019–2022 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

 Works by year

2020s

20221 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Reformer: The Efficient Transformer (2020 · 829 citations)
    Large Transformers become very costly on long inputs. The authors identify three memory sinks: activations saved at every layer for backpropagation, wide intermediate feed-forward layers, and attention whose cost grows quadratically with sequence length. The Reformer addresses each one. Attention is approximated with…
  2. Scaling Up Models and Data with t5x and seqio (2022 · 130 citations)
    Training language models with hundreds of billions of parameters usually requires custom, hand-tuned infrastructure. That slows down experimentation and makes fair comparisons between models difficult. This paper presents two open-source Google libraries meant to lower that barrier.

All works

6 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. Reformer: The Efficient TransformerarXiv:2001.04451Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya2020 · 829 citations
  2. Efficiently Scaling Transformer Inference ↗arXiv:2211.05102 · no summary yetReiner Pope et al.2022 · 368 citations
  3. Stand-Alone Self-Attention in Vision Models ↗arXiv (Cornell University) · no summary yetPrajit Ramachandran et al.2019 · 267 citations
  4. Scaling Up Models and Data with t5x and seqioarXiv:2203.17189Adam Roberts, Hyung Won Chung, Anselm Levskaya, et al.2022 · 130 citations
  5. Exploring the limits of Concurrency in ML Training on Google TPUs ↗arXiv (Cornell University) · no summary yetSameer Kumar et al.2020 · 14 citations
  6. Studying Stand-Alone Self-Attention in Vision Models ↗no summary yetPrajit Ramachandran et al.2019 · 3 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 7,233 times (of the 149,439 papers whose bibliographies we read); 1,541 citing papers with a page here →.

Updated

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