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.
End-to-end text-dependent speaker verification
2016331 citationsGeorg Heigold, Ignacio López Moreno et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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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).
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
1.
Lepikhin, Dmitry, HyoukJoong Lee, Yuanzhong Xu, et al.. (2021). GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. International Conference on Learning Representations.5 indexed citations
So, David R., et al.. (2021). Searching for Efficient Transformers for Language Modeling. Neural Information Processing Systems. 34.20 indexed citations
4.
Huang, Cheng-Zhi Anna, Ashish Vaswani, Jakob Uszkoreit, et al.. (2019). Music Transformer: Generating Music with Long-Term Structure. International Conference on Learning Representations.119 indexed citations
Huang, Cheng-Zhi Anna, Ashish Vaswani, Jakob Uszkoreit, et al.. (2018). An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation. arXiv (Cornell University).21 indexed citations
Littman, Michael L., et al.. (1999). Solving crosswords with PROVERB. National Conference on Artificial Intelligence. 914–915.2 indexed citations
16.
Shazeer, Noam, et al.. (1999). Solving crossword puzzles as probabilistic constraint satisfaction. National Conference on Artificial Intelligence. 156–162.13 indexed citations
17.
Shazeer, Noam, et al.. (1999). Proverb: the probabilistic cruciverbalist. National Conference on Artificial Intelligence. 710–717.23 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.