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Wonmin Byeon

Wonmin Byeon has 2 papers in our library, in 2024, most of them on Language models & pretraining; often with Albert Gu, Ameya Sunil Mahabaleshwarkar, Brandon Norick.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 17 · 67 works · 2,649 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 243 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #7617

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

🏢NvidiaSanta Clara · United States2018–2025 · 28 papers🏢NvidiaReading · United Kingdom2020–2025 · 12 papers🎓University of KaiserslauternKaiserslautern · Germany2013–2016 · 9 papers🔬German Research Centre for Artificial IntelligenceKaiserslautern · Germany2014–2016 · 6 papers🎓ETH ZurichZurich · Switzerland2018–2019 · 3 papers🔬Dalle Molle Institute for Artificial Intelligence ResearchLugano · Switzerland2015–2018 · 2 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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. An Empirical Study of Mamba-based Language Models (2024 · 67 citations)
    Selective state-space models (SSMs) such as Mamba could make inference cheaper than in Transformers. They avoid attention's quadratic cost and the ever-growing key-value cache. However, earlier comparisons with identical training data covered only small models. This NVIDIA-led report addresses that gap. It trains…
  2. Hymba: A Hybrid-head Architecture for Small Language Models (2024 · 50 citations)
    Small language models must balance accuracy against memory and speed. Transformers recall context well, but their compute cost and KV cache grow quickly with sequence length. State space models such as Mamba run cheaply but recall poorly. Earlier hybrids stacked the two layer types one after another, so a weak layer…

All works

5 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. GroupViT: Semantic Segmentation Emerges from Text Supervision ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetJiarui Xu et al.2022 · 483 citations
  2. Scene labeling with LSTM recurrent neural networks ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetWonmin Byeon et al.2015 · 393 citations
  3. Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetJiarui Xu et al.2023 · 374 citations
  4. An Empirical Study of Mamba-based Language ModelsarXiv:2406.07887Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.2024 · 67 citations
  5. Hymba: A Hybrid-head Architecture for Small Language ModelsarXiv:2411.13676Xin Dong, Yonggan Fu, Shizhe Diao, et al.2024 · 50 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 243 times (of the 149,439 papers whose bibliographies we read); 37 citing papers with a page here →.

Updated

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