Jeff Dean
- Health Informatics top 0.05%
- Artificial Intelligence top 0.02%
- Domain Adaptation and Few-Shot Learning 4
- Machine Learning in Healthcare 2
- Topic Modeling 2
- Machine Learning and Data Classification 2
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- Advanced Neural Network Applications 4
- Information Systems top 0.2%
- Health Information Management top 0.2%
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- COVID-19 diagnosis using AI 3
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- VLSI and FPGA Design Techniques 2
- Low-power high-performance VLSI design 2
- Co-authors
- Greg S. CorradoTomáš MikolovKai ChenIlya SutskeverKatherine ChouAndre EstevaBharath RamsundarClaire Cui
- Partner nations
- United StatesPolandIsrael
In The Last Decade
Jeff Dean
21 papers receiving 15.6k citations
Hit Papers
Peers
Comparison fields: 5 of 217
- Health Informatics 777
- Artificial Intelligence 11.2k
- Computer Vision and Pattern Recognition 3.7k
- Information Systems 2.2k
- Health Information Management 418
Countries citing papers authored by Jeff Dean
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).
Fields of papers citing papers by Jeff Dean
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
The 25 scholars most cited alongside Jeff Dean, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 3 | |
| 2 | 2023 | 3 | |
| 3 | The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkbreakdown → | 2022 | 164 |
| 4 | 2022 | 25 | |
| 5 | A graph placement methodology for fast chip designbreakdown → | 2021 | 312 |
| 6 | Deep learning-enabled medical computer visionbreakdown → | 2021 | 718 |
| 7 | 2020 | 25 | |
| 8 | 2019 | 3 | |
| 9 | Faster Discovery of Neural Architectures by Searching for Paths in a Large Model | 2018 | 6 |
| 10 | A Hierarchical Model for Device Placement | 2018 | 55 |
| 11 | Efficient Neural Architecture Search via Parameters Sharingbreakdown → | 2018 | 598 |
| 12 | A guide to deep learning in healthcarebreakdown → | 2018 | 2350 |
| 13 | 2017 | 116 | |
| 14 | 2016 | 3 | |
| 15 | 2015 | 3 | |
| 16 | DeViSE: A Deep Visual-Semantic Embedding Modelbreakdown → | 2013 | 1145 |
| 17 | Building high-level features using large scale unsupervised learningbreakdown → | 2012 | 406 |
| 18 | Appendix: Building high-level features using large scale unsupervised learning | 2012 | 22 |
| 19 | 1983 | 0 | |
| 20 | 1982 | 2 |
About Jeff Dean
Jeff Dean is a scholar working on Health Informatics, Artificial Intelligence and Hardware and Architecture, having authored 22 papers that have together received 16.7k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers), COVID-19 diagnosis using AI (3 papers), VLSI and FPGA Design Techniques (2 papers), Machine Learning in Healthcare (2 papers), Topic Modeling (2 papers), Low-power high-performance VLSI design (2 papers) and Machine Learning and Data Classification (2 papers). The work is most often cited by research in Health Informatics (777 citations), Artificial Intelligence (11.2k citations) and Computer Vision and Pattern Recognition (3.7k citations). Jeff Dean has collaborated with scholars based in United States, Poland and Israel. Frequent co-authors include Greg S. Corrado, Tomáš Mikolov, Kai Chen, Ilya Sutskever, Katherine Chou, Andre Esteva, Bharath Ramsundar, Claire Cui, Volodymyr Kuleshov and Sebastian Thrun. Their work appears in journals such as Nature, Computer, Nature Medicine, npj Digital Medicine and BMC Medical Informatics and Decision Making.
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.