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Joan Puigcerver

Joan Puigcerver has 3 papers in our library, in 2021–2022, most of them on Multilingual & cross-lingual NLP, Vision & language; often with Basil Mustafa, Neil Houlsby, Alexander Kolesnikov.

3 papers in our library · 2021–2022

OpenAlex · ORCID · h-index 20 · 60 works · 2,582 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 602 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3843

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

🎓Universitat Politècnica de ValènciaValencia · Spain2014–2023 · 17 papers🏢GoogleZurich · Switzerland2020–2024 · 13 papers🏢GoogleMountain View · United States2020–2023 · 13 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.

 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).Computer Science · 3 worksComputer Vision and Pattern Recognition · 3 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 1 workVision & language · 1 work

 Works by year

2020s

20222 papers20211 paper

 Papers

The most cited first (OpenAlex and our library).

  1. PaLI: A Jointly-Scaled Multilingual Language-Image Model (2022 · 371 citations)
    PaLI (Pathways Language and Image model), from Google Research, explores how to scale vision-language models so that one network can handle many image, text and multimodal tasks across many languages.
  2. Scaling Vision with Sparse Mixture of Experts (2021 · 128 citations)
    Large vision networks are usually dense: each parameter touches every input, so growing capacity drives up compute.
  3. Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints (2022 · 98 citations)
    Training large neural networks from random initialization is very expensive, so most practitioners keep reusing a small set of dense models.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. PaLI: A Jointly-Scaled Multilingual Language-Image ModelarXiv:2209.06794Xi Chen, Xiao Wang, Soravit Changpinyo, et al.2022 · 371 citations
  2. Scaling Vision Transformers to 22 Billion Parameters ↗arXiv:2302.05442 · no summary yetMostafa Dehghani et al.2023 · 217 citations
  3. Scaling Vision with Sparse Mixture of ExpertsarXiv:2106.05974Carlos Riquelme, Joan Puigcerver, Basil Mustafa, et al.2021 · 128 citations
  4. Sparse Upcycling: Training Mixture-of-Experts from Dense CheckpointsarXiv:2212.05055Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.2022 · 98 citations

 Co-authors here

12 of 19 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 602 times (of the 149,439 papers whose bibliographies we read); 108 citing papers with a page here →.

Updated

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