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Moustapha Cisse

Moustapha Cisse has 2 papers in our library, in 2016–2017, most of them on GPU & parallel computing; often with Armand Joulin, David Grangier, David Lopez-Paz.

2 papers in our library · 2016–2017

OpenAlex · ORCID · h-index 23 · 82 works · 7,027 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 392 times in our library · ⭐ Paper Stars ranking: #567

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

🏢MetaMenlo Park · United States2016–2018 · 9 papers🏥Hôpital Aristide Le DantecDakar · Senegal2022–2023 · 3 papers🏛️Ministère de la Santé Publique et Action SocialeDakar · Senegal2018–2020 · 3 papers🏢GoogleMountain View · United States2018–2020 · 2 papers🏢MetaTel Aviv · Israel2017 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workHardware and Architecture · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.GPU & parallel computing · 1 work

 Works by year

2010s

20171 papermixup: Beyond Empirical Risk Minimization20161 paperEfficient softmax approximation for GPUs

 Papers

The most cited first (OpenAlex and our library).

  1. mixup: Beyond Empirical Risk Minimization (2017 · 4,811 citations)
    Deep networks trained under Empirical Risk Minimization (ERM) can memorize their training sets, even with random labels, and react unpredictably to inputs slightly outside the training distribution, such as adversarial examples.
  2. Efficient softmax approximation for GPUs (2016 · 198 citations)
    Neural language models with huge vocabularies spend most of their compute on the output softmax, because its cost grows with the number of words.

 Co-authors here

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

 Cited by

Papers of our library cite their works 392 times (of the 141,840 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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