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Nan Rosemary Ke

Nan Rosemary Ke has 2 papers in our library, in 2024, most of them on Language models & pretraining, Reasoning; often with Anirudh Goyal, Michael C. Mozer, Sanjeev Arora.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 16 · 50 works · 2,281 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 67 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #9194

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

🎓Polytechnique MontréalMontreal · Canada2016–2022 · 9 papers🏢Google DeepMindLondon · United Kingdom2021–2024 · 7 papersEmployment · Senior research scientist🎓Université de MontréalMontreal · Canada2016–2021 · 4 papers🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2019–2021 · 2 papers🎓University of AucklandAuckland · New Zealand2011–2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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 worksApplied Mathematics · 1 workMathematics · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving (2024 · 45 citations)
    The authors ask whether large language models have something like metacognitive knowledge, meaning an awareness of which skills a task calls for, and whether that awareness can be used to improve their performance.
  2. AI-Assisted Generation of Difficult Math Questions (2024 · 23 citations)
    LLM training now treats mathematical reasoning as a core capability, but public sources of hard problems are largely used up.

All works

6 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. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem SolvingarXiv:2405.12205Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, et al.2024 · 45 citations
  2. Task Loss Estimation for Sequence Prediction ↗arXiv (Cornell University) · no summary yetDzmitry Bahdanau et al.2015 · 29 citations
  3. AI-Assisted Generation of Difficult Math QuestionsarXiv:2407.21009Vedant Shah, Dingli Yu, Kaifeng Lyu, et al.2024 · 23 citations
  4. Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future ↗arXiv (Cornell University) · no summary yetNan Rosemary Ke et al.2019 · 15 citations
  5. Modeling the Long Term Future in Model-Based Reinforcement Learning ↗International Conference on Learning Representations · no summary yetNan Rosemary Ke et al.2018 · 14 citations
  6. Task Loss Estimation for Structured Prediction ↗no summary yetDzmitry Bahdanau et al.2016 · 5 citations

 Co-authors here

12 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 67 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

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