David Grangier

13.2k total citations · 5 hit papers
43 papers, 4.7k citations indexed

About

David Grangier is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, David Grangier has authored 43 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 9 papers in Signal Processing. Recurrent topics in David Grangier's work include Topic Modeling (21 papers), Natural Language Processing Techniques (21 papers) and Speech Recognition and Synthesis (9 papers). David Grangier is often cited by papers focused on Topic Modeling (21 papers), Natural Language Processing Techniques (21 papers) and Speech Recognition and Synthesis (9 papers). David Grangier collaborates with scholars based in United States, Switzerland and Israel. David Grangier's co-authors include Michael Auli, Yann Dauphin, Myle Ott, Sergey Edunov, Angela Fan, Samy Bengio, Jonas Gehring, Sam Gross, Nathan Ng and Alexei Baevski and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Speech Communication and IEEE/ACM Transactions on Audio Speech and Language Processing.

In The Last Decade

David Grangier

39 papers receiving 4.3k citations

Hit Papers

5 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

2019 1.4k citations Myle Ott, Sergey Edunov et al. profile →
Convolutional Sequence to Sequence Learning

2017 666 citations Jonas Gehring, Michael Auli et al. International Conference on Machine Learning profile →
Understanding Back-Translation at Scale

2018 539 citations Sergey Edunov, Myle Ott et al. profile →
Language modeling with gated convolutional networks

2017 454 citations Yann Dauphin, Angela Fan et al. International Conference on Machine Learning profile →
AudioLM: A Language Modeling Approach to Audio Generation

2023 201 citations Zalán Borsos, Raphaël Marinier et al. IEEE/ACM Transactions on Audio Speech and Language Processing profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Grangier United States	18	3.9k	1.8k	605	281	139	43	4.7k
Michael Auli United States	27	5.9k 1.5×	1.9k 1.1×	827 1.4×	321 1.1×	169 1.2×	59	6.7k
Enrique Vidal Spain	33	2.5k 0.6×	1.9k 1.1×	625 1.0×	270 1.0×	127 0.9×	218	4.0k
Ioannis Tsochantaridis United States	9	2.2k 0.6×	1.9k 1.0×	312 0.5×	349 1.2×	316 2.3×	10	3.7k
Holger Schwenk France	26	3.5k 0.9×	816 0.5×	317 0.5×	264 0.9×	142 1.0×	93	3.8k
Yann Dauphin United States	13	2.4k 0.6×	1.2k 0.7×	343 0.6×	178 0.6×	85 0.6×	24	3.3k
Shiyu Chang United States	34	1.9k 0.5×	2.2k 1.2×	300 0.5×	250 0.9×	110 0.8×	110	3.8k
Bing Xiang United States	31	3.4k 0.9×	655 0.4×	537 0.9×	447 1.6×	210 1.5×	88	3.9k
Benyu Zhang China	18	1.6k 0.4×	2.4k 1.3×	439 0.7×	528 1.9×	205 1.5×	36	3.8k
Phil Blunsom United Kingdom	25	4.2k 1.1×	980 0.5×	227 0.4×	610 2.2×	172 1.2×	60	5.0k
Prem Natarajan United States	29	1.5k 0.4×	3.0k 1.7×	515 0.9×	169 0.6×	94 0.7×	165	4.2k

Countries citing papers authored by David Grangier

Since Specialization

Citations

This map shows the geographic impact of David Grangier's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Grangier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Grangier more than expected).

Fields of papers citing papers by David Grangier

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Grangier. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Grangier. The network helps show where David Grangier may publish in the future.

Co-authorship network of co-authors of David Grangier

This figure shows the co-authorship network connecting the top 25 collaborators of David Grangier. A scholar is included among the top collaborators of David Grangier based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Grangier. David Grangier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Grangier, David, et al.. (2024). Rephrasing the Web: A Recipe for Compute and Data-Efficient Language Modeling. 14044–14072. 4 indexed citations

Borsos, Zalán, Raphaël Marinier, Damien Vincent, et al.. (2023). AudioLM: A Language Modeling Approach to Audio Generation. IEEE/ACM Transactions on Audio Speech and Language Processing. 31. 2523–2533. 201 indexed citations breakdown →

Freitag, Markus, George Foster, David Grangier, et al.. (2021). Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation. Transactions of the Association for Computational Linguistics. 9. 1460–1474. 145 indexed citations

Freitag, Markus, George Foster, David Grangier, & Colin Cherry. (2020). Human-Paraphrased References Improve Neural Machine Translation. arXiv (Cornell University). 1183–1192.

Ott, Myle, Sergey Edunov, Alexei Baevski, et al.. (2019). fairseq: A Fast, Extensible Toolkit for Sequence Modeling. 48–53. 1381 indexed citations breakdown →

Edunov, Sergey, Myle Ott, Michael Auli, & David Grangier. (2018). Understanding Back-Translation at Scale. 489–500. 539 indexed citations breakdown →

Pavllo, Dario, David Grangier, & Michael Auli. (2018). QuaterNet: A Quaternion-based Recurrent Model for Human Motion.. British Machine Vision Conference. 299. 28 indexed citations

Gehring, Jonas, Michael Auli, David Grangier, Denis Yarats, & Yann Dauphin. (2017). Convolutional Sequence to Sequence Learning. International Conference on Machine Learning. 1243–1252. 666 indexed citations breakdown →

Dauphin, Yann, Angela Fan, Michael Auli, & David Grangier. (2017). Language modeling with gated convolutional networks. International Conference on Machine Learning. 933–941. 454 indexed citations breakdown →

10.

Grangier, David & Michael Auli. (2017). QuickEdit: Editing Text & Translations via Simple Delete Actions.. arXiv (Cornell University). 5 indexed citations

11.

Chen, Wenlin, David Grangier, & Michael Auli. (2016). Strategies for Training Large Vocabulary Neural Language Models. 1975–1985. 40 indexed citations

12.

Bengio, Samy, Jason Weston, & David Grangier. (2010). Label Embedding Trees for Large Multi-Class Tasks. 23. 163–171. 177 indexed citations

13.

Bai, Bing, Jason Weston, David Grangier, et al.. (2009). Learning to rank with (a lot of) word features. Information Retrieval. 13(3). 291–314. 85 indexed citations

14.

Bai, Bing, Jason Weston, David Grangier, et al.. (2009). Supervised semantic indexing. 187–196. 60 indexed citations

15.

Grangier, David & Samy Bengio. (2008). A Discriminative Kernel-Based Approach to Rank Images from Text Queries. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(8). 1371–1384. 238 indexed citations

16.

Keshet, Joseph, David Grangier, & Samy Bengio. (2007). Discriminative Keyword Spotting. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 4 indexed citations

17.

Grangier, David & Samy Bengio. (2006). A Neural Network to Retrieve Images from Text Queries. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1 indexed citations

18.

Grangier, David & Samy Bengio. (2005). Exploiting Hyperlinks to Learn a Retrieval Model. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 5 indexed citations

19.

Grangier, David & Samy Bengio. (2005). Inferring document similarity from hyperlinks. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 359–360. 12 indexed citations

20.

Grangier, David, Alessandro Vinciarelli, & Hervé Bourlard. (2003). Information Retrieval on Noisy Text. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact