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Shohei Taniguchi

Shohei Taniguchi has 2 papers in our library, in 2026; often with Yutaka Matsuo, Takeshi Kojima, Yusuke Iwasawa.

2 papers in our library · 2026

OpenAlex · h-index 10 · 46 works · 241 citations OpenAlex, 2026-10-14

📄 2 works in our library · ⭐ Paper Stars ranking: #12220

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

🎓The University of TokyoTokyo · Japan2020–2026 · 16 paperscurrent🏢PanasonicKadoma · Japan2009–2024 · 5 papers🎓Hokkaido UniversitySapporo · Japan2014–2021 · 5 papers🔬Tokyo Metropolitan Industrial Technology Research InstituteTokyo · Japan2001–2008 · 4 papers🏢Panasonic Electric Works Co., Ltd.Kadoma · Japan2009–2011 · 2 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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Infinity-MoE: Generalizing Mixture of Experts to Infinite Experts (2026)
    Mixture of Experts (MoE) layers grow model capacity without a matching rise in per-token compute. However, the experts are independent, discrete units, which makes training harder as their number increases. The authors propose ∞-MoE, which replaces the discrete expert set with a continuous expert space. For each…
  2. Position Encoding with Random Float Sampling Enhances Length Generalization of Transformers (2026)
    Transformers often fail on inputs longer than their training context. Standard integer position indices beyond the trained range are out-of-distribution (OOD), and training on longer sequences is expensive. The authors propose Random Float Sampling (RFS), a position indexing scheme that needs no fine-tuning. During…

 Co-authors here

3 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.

Updated

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