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Neel Sundaresan

Neel Sundaresan has 2 papers in our library, in 2020–2023; often with Alexey Svyatkovskiy, Dawn Drain, Daxin Jiang.

2 papers in our library · 2020–2023

OpenAlex · ORCID · Wikidata · h-index 34 · 161 works · 5,739 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 223 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6786

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

🏢MicrosoftRedmond · United States2014–2026 · 44 paperscurrent🏢eBaySan Jose · United States2007–2015 · 45 papers🏢eBayDublin · Ireland2008–2015 · 8 papers🔬IBM Research - AlmadenSan Jose · United States1999–2002 · 8 papers🎓Stanford UniversityStanford · United States2012–2020 · 3 papers🏢EndoGastric SolutionsRedmond · United States2020–2023 · 2 papers🏢OFM ResearchRedmond · United States2020–2023 · 2 papers🤝National Bureau of Economic ResearchCambridge · United States2012–2013 · 2 papers🏢IBMArmonk · United States2000–2002 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksComputer Science Applications · 1 work

 Works by year

2020s

20231 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. PyMT5: multi-mode translation of natural language and Python code with transformers (2020 · 158 citations)
    The paper tackles joint modeling of Python source code and its natural-language documentation. The authors propose a single sequence-to-sequence transformer, PyMT5, that converts in both directions: it writes method bodies from signatures or docstrings, and it summarizes code into docstrings. Code and text are…
  2. Code Execution with Pre-trained Language Models (2023 · 38 citations)
    Most pre-trained code models learn from source text and static structure such as ASTs or data flow. They do not see how a program actually behaves at runtime. This paper asks whether a Transformer can learn to emulate execution: given a Python program, it must generate the full execution trace, meaning the order in…

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. Large-Scale Video Summarization Using Web-Image Priors ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetAditya Khosla et al.2013 · 287 citations
  2. PyMT5: multi-mode translation of natural language and Python code with transformersConference on Empirical Methods in Natural Language Processing (EMNLP)Colin Clement, Dawn Drain, Jonathan Timcheck, et al.2020 · 158 citations
  3. Code Execution with Pre-trained Language ModelsFindings of the Association for Computational LinguisticsChenxiao Liu, Shuai Lu, Weizhu Chen, et al.2023 · 38 citations

 Co-authors here

7 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 223 times (of the 149,439 papers whose bibliographies we read); 50 citing papers with a page here →.

Updated

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