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Georg Heigold

Georg Heigold has 2 papers in our library, in 2017–2020, most of them on Multilingual & cross-lingual NLP, Morphology; often with Alexander Kolesnikov, Jakob Uszkoreit, Lucas Beyer.

2 papers in our library · 2017–2020

OpenAlex · h-index 29 · 89 works · 27,591 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,661 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #92

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

🎓RWTH Aachen UniversityAachen · Germany2005–2013 · 28 papers🏢GoogleMountain View · United States2010–2025 · 23 papers🔬German Research Centre for Artificial IntelligenceKaiserslautern · Germany2015–2021 · 9 papers🎓Saarland UniversitySaarbrücken · Germany2015–2017 · 5 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).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Morphology · 1 workMultilingual & cross-lingual NLP · 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. Cross-lingual Character-Level Neural Morphological Tagging (2017 · 98 citations)
    Most languages lack the thousands of annotated sentences that strong morphological taggers need. Cotterell and Heigold address this with transfer learning. They train a character-level recurrent tagger for a low-resource language together with one for a related high-resource language, such as Catalan paired with…

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. 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. ViViT: A Video Vision Transformer ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetAnurag Arnab et al.2021 · 2,738 citations
  3. Object-Centric Learning with Slot Attention ↗arXiv (Cornell University) · no summary yetFrancesco Locatello et al.2020 · 255 citations
  4. Sequence discriminative distributed training of long short-term memory recurrent neural networks ↗Interspeech (USB) · no summary yetHaşim Sak et al.2014 · 137 citations
  5. Cross-lingual Character-Level Neural Morphological TaggingConference on Empirical Methods in Natural Language Processing (EMNLP)Ryan Cotterell, Georg Heigold2017 · 98 citations
  6. Large Vocabulary Continuous Speech Recognition with Long Short-Term Recurrent Networks ↗no summary yetOriol Vinyals, Georg Heigold, Erik McDermott2014

 Co-authors here

7 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 2,661 times (of the 149,439 papers whose bibliographies we read); 188 citing papers with a page here →.

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