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Michael Noukhovitch

Michael Noukhovitch has 2 papers in our library, in 2018–2024, most of them on Summarization; often with Aaron Courville, Arian Hosseini, Dzmitry Bahdanau.

2 papers in our library · 2018–2024

OpenAlex · h-index 5 · 17 works · 100 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 201 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8702

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

🎓Université de MontréalMontreal · Canada2018–2026 · 9 paperscurrent🔬Allen Institute for Artificial IntelligenceSeattle · United States2025–2026 · 2 paperscurrent🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2018–2024 · 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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Summarization · 1 work

 Works by year

2020s

20241 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Systematic Generalization: What Is Required and Can It Be Learned? (2018 · 73 citations)
    The authors test whether neural models of grounded language understanding can generalize systematically, that is, correctly understand new combinations of words they have already seen.
  2. The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR Summarization (2024 · 14 citations)
    RLHF pipelines like OpenAI's have been hard to replicate in open source. Subtle engineering choices affect stability, instruction-following outputs are difficult to judge, and training cycles are slow. The authors address this by revisiting the earlier TL;DR summarization study of Stiennon et al. (2020), where…

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. Systematic Generalization: What Is Required and Can It Be Learned?arXiv:1811.12889Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, et al.2018 · 73 citations
  2. Commonsense mining as knowledge base completion? A study on the impact of novelty ↗Proceedings of the Workshop on Generalization in the Age of Deep Learning · no summary yetStanislaw Jastrzębski et al.2018 · 41 citations
  3. The N+ Implementation Details of RLHF with PPO: A Case Study on TL;DR SummarizationarXiv:2403.17031Shengyi Huang, Michael Noukhovitch, Arian Hosseini, et al.2024 · 14 citations

 Co-authors here

10 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 201 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated

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