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Chitwan Saharia

Chitwan Saharia has 2 papers in our library, in 2020–2022, most of them on Machine translation, Vision & language; often with Saurabh Saxena, William Chan, Burcu Karagol Ayan.

2 papers in our library · 2020–2022

OpenAlex · h-index 17 · 23 works · 7,725 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 478 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #1059

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 States2020–2023 · 10 papers🎓Indian Institute of Technology BombayMumbai · India2018–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 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 worksComputer Vision and Pattern Recognition · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Vision & language · 1 workMachine translation · 1 work

 Works by year

2020s

20221 paperPhotorealistic Text-to-Image Diffusion Models with Deep Language Understanding20201 paperNon-Autoregressive Machine Translation with Latent Alignments

 Papers

The most cited first (OpenAlex and our library).

  1. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (2022 · 2,423 citations)
    Most text-to-image systems learn their text encoders from paired image-caption data, which may limit how well they handle complex or compositional prompts.
  2. Non-Autoregressive Machine Translation with Latent Alignments (2020 · 176 citations)
    The paper addresses two common weaknesses of non-autoregressive machine translation: duplicated tokens caused by conditionally independent predictions, and the need to predict the output length in advance and often re-score several length candidates.

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. Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingarXiv:2205.11487Chitwan Saharia, William Chan, Saurabh Saxena, et al.2022 · 2,423 citations
  2. Non-Autoregressive Machine Translation with Latent AlignmentsConference on Empirical Methods in Natural Language Processing (EMNLP)Chitwan Saharia, William Chan, Saurabh Saxena, et al.2020 · 176 citations
  3. BabyAI: A Platform to Study the Sample Efficiency of Grounded Language LearningarXiv:1810.08272 · original ↗ · no summary yetMaxime Chevalier-Boisvert et al.2018 · 113 citations

 Co-authors here

8 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 478 times (of the 141,840 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated

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