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Maxwell Nye

Maxwell Nye has 5 papers in our library, in 2019–2021, most of them on Code & programming languages, Reasoning; often with Joshua B. Tenenbaum, Armando Solar-Lezama, Catherine Wong.

5 papers in our library · 2019–2021

OpenAlex · h-index 9 · 21 works · 523 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 1,354 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #2187

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

🎓Massachusetts Institute of TechnologyCambridge · United States2018–2023 · 15 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 6The field of science this work belongs to (OpenAlex fields).Computer Science · 5 worksArts and Humanities · 1 workArtificial Intelligence · 4 worksComputer Science Applications · 4 worksInformation Systems · 3 worksLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Code & programming languages · 3 worksReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Large language models · 1 work

 Works by year

2020s

20212 papers20201 paper

2010s

20192 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Program Synthesis with Large Language Models (2021 · 1,192 citations)
    The paper asks how far general-purpose large Transformer language models can go at writing short programs in an ordinary language like Python, rather than in a narrow DSL.
  2. Write, Execute, Assess: Program Synthesis with a REPL (2019 · 86 citations)
    Program synthesis is difficult because a small syntactic edit can drastically change what a program does, and neural networks struggle to learn this mapping.
  3. Learning to Infer Program Sketches (2019 · 53 citations)
    The paper addresses program synthesis from specifications that people find easy to give, namely input-output examples and natural-language descriptions.
  4. Communicating Natural Programs to Humans and Machines (2021 · 22 citations)
    ARC (Abstraction and Reasoning Corpus) is a set of puzzles on grids of colored cells. Given a few input–output examples, one has to infer the rule and apply it to a new input. Humans solve most of these tasks easily, while the best AI systems solve only a small fraction. The authors argue that the reason is language.…
  5. DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning (2020 · 22 citations)
    Program induction treats learning a task as finding a program that produces the desired behavior. It generalizes well from a few examples and yields knowledge humans can read. In practice, though, it has relied on hand-crafted domain-specific languages (DSLs) and search procedures tuned to them. DreamCoder learns…

All works

6 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. Program Synthesis with Large Language ModelsarXiv:2108.07732Jacob Austin, Augustus Odena, Maxwell Nye, et al.2021 · 1,192 citations
  2. Implicit Representations of Meaning in Neural Language Models ↗Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · no summary yetBelinda Z. Li, Maxwell Nye, Jacob Andreas2021 · 175 citations
  3. Write, Execute, Assess: Program Synthesis with a REPLarXiv:1906.04604Kevin Ellis, Maxwell Nye, Yewen Pu, et al.2019 · 86 citations
  4. Learning to Infer Program SketchesarXiv:1902.06349Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, et al.2019 · 53 citations
  5. Communicating Natural Programs to Humans and MachinesConference on Neural Information Processing Systems (NeurIPS)Samuel Acquaviva, Yewen Pu, Marta Kryven, et al.2021 · 22 citations
  6. DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningarXiv:2006.08381Kevin Ellis, Catherine Wong, Maxwell Nye, et al.2020 · 22 citations

 Co-authors here

12 of 15 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 1,354 times (of the 149,439 papers whose bibliographies we read); 471 citing papers with a page here →.

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