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Dirk Weissenborn

Dirk Weissenborn has 2 papers in our library, in 2017–2020, most of them on Question answering; often with Alexander Kolesnikov, Georg Heigold, Jakob Uszkoreit.

2 papers in our library · 2017–2020

OpenAlex · h-index 19 · 36 works · 23,921 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,618 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #101

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 States2019–2022 · 7 paperscurrent🔬German Research Centre for Artificial IntelligenceKaiserslautern · Germany2015–2021 · 13 papers🎓Technische Universität DresdenDresden · Germany2013–2015 · 4 papers🔬Biotechnologisches ZentrumDresden · Germany2014–2015 · 3 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 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workArts and Humanities · 1 workInformation Systems · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Question answering · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Transformer and attention · 1 work

 Works by year

2020s

20201 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (2020 · 23,726 citations)
    In 2020, image recognition still relied mainly on convolutional networks. Earlier efforts to use self-attention in vision either kept a CNN backbone or used custom attention patterns that ran slowly on modern accelerators. The authors ask whether an almost unchanged NLP-style Transformer encoder can classify images…
  2. Making Neural QA as Simple as Possible but not Simpler (2017 · 122 citations)
    Large extractive QA datasets such as SQuAD and NewsQA prompted many neural reading models. These models grew steadily more elaborate, especially in their layers for word-by-word interaction between question and context, yet they were seldom compared against a strong simple baseline. Weissenborn, Wiese and Seiffe…

All works

7 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. An Image is Worth 16x16 Words: Transformers for Image Recognition at ScalearXiv:2010.11929Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, et al.2020 · 23,726 citations
  2. Object-Centric Learning with Slot Attention ↗arXiv (Cornell University) · no summary yetFrancesco Locatello et al.2020 · 255 citations
  3. Neural Domain Adaptation for Biomedical Question Answering ↗Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · no summary yetGeorg Wiese, Dirk Weissenborn, Mariana Neves2017 · 138 citations
  4. Making Neural QA as Simple as Possible but not SimplerarXiv:1703.04816Dirk Weissenborn, Georg Wiese, Laura Seiffe2017 · 122 citations
  5. Differentiable Patch Selection for Image Recognition ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetJean-Baptiste Cordonnier et al.2021 · 110 citations
  6. Scaling Autoregressive Video Models ↗arXiv (Cornell University) · no summary yetDirk Weissenborn, Oscar Täckström, Jakob Uszkoreit2019 · 35 citations
  7. Contextualized Role Interaction for Neural Machine Translation ↗no summary yetDirk Weissenborn et al.2018 · 2 citations

 Co-authors here

8 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 2,618 times (of the 149,439 papers whose bibliographies we read); 256 citing papers with a page here →.

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