Noam Shazeer

17 papers receiving 755 citations

Hit Papers

End-to-end text-dependent speaker verification20162026201920222016100200300

Peers

Noam Shazeer
Comparison fields: 5 of 86
  • Artificial Intelligence 586
  • Signal Processing 413
  • Computer Vision and Pattern Recognition 265
  • Cognitive Neuroscience 56
  • Computer Networks and Communications 48
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Countries citing papers authored by Noam Shazeer

Since Specialization
Citations

This map shows the geographic impact of Noam Shazeer'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 Noam Shazeer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noam Shazeer more than expected).

Fields of papers citing papers by Noam Shazeer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Noam Shazeer. 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 Noam Shazeer. The network helps show where Noam Shazeer may publish in the future.

Co-authorship network of co-authors of Noam Shazeer

This figure shows the co-authorship network connecting the top 25 collaborators of Noam Shazeer. A scholar is included among the top collaborators of Noam Shazeer 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 Noam Shazeer. Noam Shazeer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
#WorkIndexed citations
1 37
2
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
5
3
Searching for Efficient Transformers for Language Modeling
20
4
Music Transformer: Generating Music with Long-Term Structure
119
5 75
6
An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation
21
7 116
8 3
9
End-to-end text-dependent speaker verificationbreakdown →
331
10
Sparse Non-negative Matrix Language Modeling (EMNLP presentation)
1
11 3
12 12
13 2
14 36
15
Solving crossword puzzles as probabilistic constraint satisfaction
13
16
Proverb: the probabilistic cruciverbalist
23
17
Solving crosswords with PROVERB
2

About Noam Shazeer

Noam Shazeer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 17 papers that have together received 819 indexed citations. Recurring topics across this work include Topic Modeling (10 papers), Natural Language Processing Techniques (7 papers) and Speech Recognition and Synthesis (4 papers). The work is most often cited by research in Signal Processing (413 citations), Artificial Intelligence (586 citations) and Computer Vision and Pattern Recognition (265 citations). Noam Shazeer has collaborated with scholars based in United States, Belgium and Israel. Frequent co-authors include Samy Bengio, Georg Heigold, Ignacio López Moreno, Quoc V. Le, William R. Mark, Ravi Teja Mullapudi, Michael L. Littman, Kayvon Fatahalian, Azalia Mirhoseini and Krzysztof Maziarz. Their work appears in journals such as Artificial Intelligence, Transactions of the Association for Computational Linguistics and arXiv (Cornell University).

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.

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