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Martin Schrimpf

Martin Schrimpf has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Vision & language; often with Badr AlKhamissi, Yingtian Tang, Antoine Bosselut.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 17 · 64 works · 2,747 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 9 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10931

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

🎓École Polytechnique Fédérale de LausanneLausanne · Switzerland2023–2026 · 12 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2017–2025 · 26 papersEducation 2017–2022 · PhD, Brain and Cognitive SciencesEmployment 2022–2023 · Research Scientist, Quest for Intelligence🔬McGovern Institute for Brain ResearchCambridge · United States2019–2024 · 17 papers
🏛️École Polytechnique Fédérale de Lausanne BibliothèqueLausanne · Switzerland2023Employment since 2023 · Tenure-Track Assistant Professor, Schools of Life Science and Computer and Communication Sciences

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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksCognitive Neuroscience · 1 workNeuroscience · 1 workComputer Vision and Pattern Recognition · 1 work

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

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. From Language to Cognition: How LLMs Outgrow the Human Language Network (2025 · 11 citations)
    The paper examines why large language models resemble activity in the human language network (LN), and how this resemblance develops during pretraining.
  2. Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data (2024 · 1 citation)
    Modern large language models are trained on trillions of tokens. A human, by contrast, hears at most about 100M words before adolescence. The authors worked within the BabyLM Challenge and investigated whether a multimodal model can be trained on such a limited budget. To this end, they assembled their own 100M-word…

All works

3 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. A Flexible Approach to Automated RNN Architecture Generation ↗arXiv (Cornell University) · no summary yetMartin Schrimpf et al.2017 · 12 citations
  2. From Language to Cognition: How LLMs Outgrow the Human Language NetworkConference on Empirical Methods in Natural Language Processing (EMNLP)Badr AlKhamissi, Greta Tuckute, Yingtian Tang, et al.2025 · 11 citations
  3. Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible DataConference on Computational Natural Language Learning (CoNLL)Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce, et al.2024 · 1 citation

 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.

 Cited by

Papers of our library cite their works 9 times (of the 149,439 papers whose bibliographies we read); 2 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/martin-schrimpf/