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Adam Atkinson

Adam Atkinson has 2 papers in our library, in 2017–2025, most of them on Language models & pretraining, Reasoning, Vision & language; often with Abdelrahman Abouelenin, Adam Trischler, Akos Kadar.

2 papers in our library · 2017–2025

OpenAlex · h-index 7 · 11 works · 260 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 263 times in our library · ⭐ Paper Stars ranking: #4911

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

🎓University of California, Los AngelesLos Angeles · United States2026 · 1 papercurrent🏢MicrosoftRedmond · United States2020 · 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).Computer Science · 2 worksComputer Vision and Pattern Recognition · 2 worksArts and Humanities · 1 workArtificial Intelligence · 1 workLanguage and Linguistics · 1 work

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 workVision & language · 1 work

 Works by year

2020s

20251 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FigureQA: An Annotated Figure Dataset for Visual Reasoning (2017 · 240 citations)
    FigureQA addresses machine reasoning over scientific-style charts. Instead of reconstructing the underlying numbers, a model should judge relationships directly from the image, much as people do on exam questions. The authors built a synthetic corpus of five chart kinds: line, dot-line, vertical bar, horizontal bar…
  2. Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs (2025 · 152 citations)
    Small language models usually trail larger ones at reasoning. Adding vision or audio to them typically means fine-tuning the backbone, which weakens its text skills and forces teams to run several separate models. In this report, Microsoft presents Phi-4-Mini, a 3.8B-parameter decoder-only model. It was trained on…

 Co-authors here

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

 Cited by

Papers of our library cite their works 263 times (of the 149,439 papers whose bibliographies we read); 86 citing papers with a page here →.

Updated

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