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Dimitris Papailiopoulos

Dimitris Papailiopoulos has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Kangwook Lee, Nayoung Lee, Jaewoong Cho.

2 papers in our library · 2023–2024

OpenAlex · h-index 29 · 124 works · 4,868 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 143 times in our library · ⭐ Paper Stars ranking: #8450

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

🏢MicrosoftRedmond · United States2024–2026 · 13 paperscurrent🎓University of California, BerkeleyBerkeley · United States2015–2026 · 5 paperscurrent🏢Microsoft ResearchCambridge · United Kingdom2026 · 3 paperscurrent🎓University of Wisconsin–MadisonMadison · United States2017–2025 · 41 papers🎓The University of Texas at AustinAustin · United States2012–2016 · 17 papers🎓University of Southern CaliforniaLos Angeles · United States2010–2014 · 17 papers🎓Technical University of CreteChania · Greece2007–2010 · 6 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Teaching Arithmetic to Small Transformers (2023 · 57 citations)
    The paper asks which factors let a next-token predictor acquire arithmetic. It uses a controlled setting: small decoder-only transformers (NanoGPT and GPT-2, from 10.6M up to 124M parameters) trained from random initialization with character-level tokens. Tasks include addition, subtraction, multiplication, square…
  2. Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks (2024 · 41 citations)
    In-context learning (ICL) has mostly been studied in Transformers. This paper asks whether attention-free state-space models (SSMs), chiefly Mamba and also S4, can acquire the same ability. Large-scale ICL is hard to test cheaply, so the authors follow the Garg et al. (2022) setup: small models are trained from…

 Co-authors here

5 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 143 times (of the 149,439 papers whose bibliographies we read); 51 citing papers with a page here →.

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