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Marc Brockschmidt

Marc Brockschmidt has 2 papers in our library, in 2017–2020, most of them on Code & programming languages; often with Miltiadis Allamanis, Mahmoud Khademi.

2 papers in our library · 2017–2020

OpenAlex · ORCID · h-index 29 · 78 works · 3,318 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 642 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #3187

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

🏢GoogleMountain View · United States2026 · 1 papercurrent🏢GoogleLondon · United Kingdom2026 · 1 papercurrent🏢Microsoft ResearchCambridge · United Kingdom2014–2022 · 28 papers🏢MicrosoftRedmond · United States2015–2022 · 20 papers🎓RWTH Aachen UniversityAachen · Germany2010–2013 · 8 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksInformation Systems · 2 worksComputational Theory and Mathematics · 1 workComputer Science Applications · 1 workSoftware · 1 work

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

 Works by year

2020s

20201 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. CodeSearchNet Challenge: Evaluating the State of Semantic Code Search (2020 · 656 citations)
    Finding code from a natural-language request is difficult. Programmers name things with terse, technical identifiers, while queries describe intent loosely, so the two often share few words. The field also lacked a sizeable benchmark built for this task. The authors address both gaps with two resources.
  2. Learning to Represent Programs with Graphs (2017 · 127 citations)
    The authors argue that most neural networks for code analysis treat code like ordinary text, either as a sequence of tokens or as a syntax tree.

All works

8 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. CodeSearchNet Challenge: Evaluating the State of Semantic Code SearcharXiv:1909.09436Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, et al.2020 · 656 citations
  2. Gated Graph Sequence Neural Networks ↗arXiv:1511.05493 · no summary yetYujia Li et al.2015 · 576 citations
  3. DeepCoder: Learning to Write Programs ↗arXiv:1611.01989 · no summary yetMatej Balog et al.2017 · 284 citations
  4. Learning to Represent Programs with GraphsarXiv:1711.00740Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi2017 · 127 citations
  5. Learning to Represent Edits ↗arXiv:1810.13337 · no summary yetPengcheng Yin et al.2018 · 91 citations
  6. Graph Partition Neural Networks for Semi-Supervised Classification ↗arXiv (Cornell University) · no summary yetRenjie Liao et al.2018 · 55 citations
  7. Self-Supervised Bug Detection and Repair ↗arXiv:2105.12787 · no summary yetMiltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt2021 · 50 citations
  8. Under review as a conference paper at ICLR 2016 GATED GRAPH SEQUENCE NEURAL NETWORKS ↗no summary yetYujia Li et al.2016

 Co-authors here

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

 Cited by

Papers of our library cite their works 642 times (of the 149,439 papers whose bibliographies we read); 95 citing papers with a page here →.

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