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.
Distributed Representations of Words and Phrases and their Compositionality
201310.6k citationsTomáš Mikolov, Greg S. Corrado et al.arXiv (Cornell University)profile →
A guide to deep learning in healthcare
20182.4k citationsAndre Esteva, Bharath Ramsundar et al.Nature Medicineprofile →
This map shows the geographic impact of Jeff Dean'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 Jeff Dean with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jeff Dean more than expected).
This network shows the impact of papers produced by Jeff Dean. 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 Jeff Dean. The network helps show where Jeff Dean may publish in the future.
Co-authorship network of co-authors of Jeff Dean
This figure shows the co-authorship network connecting the top 25 collaborators of Jeff Dean.
A scholar is included among the top collaborators of Jeff Dean 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 Jeff Dean. Jeff Dean is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Patterson, David S., Joseph E. Gonzalez, Urs Hölzle, et al.. (2022). The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. Computer. 55(7). 18–28.164 indexed citations breakdown →
Mirhoseini, Azalia, Anna Goldie, Mustafa Ege Yazgan, et al.. (2021). A graph placement methodology for fast chip design. Nature. 594(7862). 207–212.312 indexed citations breakdown →
6.
Esteva, Andre, Katherine Chou, Serena Yeung, et al.. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine. 4(1). 5–5.718 indexed citations breakdown →
Mirhoseini, Azalia, Anna Goldie, Hieu Pham, et al.. (2018). A Hierarchical Model for Device Placement. International Conference on Learning Representations.55 indexed citations
11.
Pham, Hieu, Melody Y. Guan, Barret Zoph, Quoc V. Le, & Jeff Dean. (2018). Faster Discovery of Neural Architectures by Searching for Paths in a Large Model. International Conference on Learning Representations.6 indexed citations
12.
Esteva, Andre, Bharath Ramsundar, Volodymyr Kuleshov, et al.. (2018). A guide to deep learning in healthcare. Nature Medicine. 25(1). 24–29.2350 indexed citations breakdown →
Le, Quoc V., Rajat Monga, Matthieu Devin, et al.. (2012). Appendix: Building high-level features using large scale unsupervised learning.22 indexed citations
19.
Dean, Jeff, et al.. (1983). Photographing Historic Buildings. Bulletin of the Association for Preservation Technology. 15(3). 45–45.
20.
Dean, Jeff. (1982). Photographing Historic Buildings. Bulletin of the Association for Preservation Technology. 14(4). 31–31.2 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.