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David Uthus

David Uthus has 2 papers in our library, in 2022–2023; often with Joshua Ainslie, Mandy Guo, Santiago Ontañón.

2 papers in our library · 2022–2023

OpenAlex · h-index 9 · 33 works · 527 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 225 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3432

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

🏢GoogleMountain View · United States2013–2023 · 12 papers🔬United States Naval Research LaboratoryWashington · United States2011–2013 · 4 papers🎓University of AucklandAuckland · New Zealand2008–2009 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

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

 Works by year

2020s

20231 paperCoLT5: Faster Long-Range Transformers with Conditional Computation20221 paperLongT5: Efficient Text-To-Text Transformer for Long Sequences

 Papers

The most cited first (OpenAlex and our library).

  1. LongT5: Efficient Text-To-Text Transformer for Long Sequences (2022 · 398 citations)
    Standard Transformers such as T5 scale poorly to long documents because full self-attention grows quadratically with input length.
  2. CoLT5: Faster Long-Range Transformers with Conditional Computation (2023 · 83 citations)
    Encoding long documents with Transformers is costly. Sparse attention reduces the quadratic attention cost, but the feedforward and projection layers still run on every token and dominate compute in larger models. CoLT5 (Conditional LongT5), from Google Research, extends LongT5 with conditional computation. Each…

All works

3 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. LongT5: Efficient Text-To-Text Transformer for Long SequencesMandy Guo, Joshua Ainslie, David Uthus, et al.Findings of the Association for Computational Linguistics · 398 citations
  2. CoLT5: Faster Long-Range Transformers with Conditional ComputationJoshua Ainslie, Tao Lei, Michiel de Jong, et al.Conference on Empirical Methods in Natural Language Processing (EMNLP) · 83 citations
  3. Dr Genre: Reinforcement Learning from Decoupled LLM Feedback for Generic Text Rewriting · original ↗ · no summary yet · arXiv:2503.06781Yufei Li et al.2025 · 3 citations

 Co-authors here

12 of 13 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 225 times (of the 141,840 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/david-uthus/