Geoffrey Hinton: h-index, Total Citations, and Citation Map
Geoffrey Hinton's h-index is 190 (527 i10-index, 1,035,072+ total citations across 776+ publications) according to Google Scholar as of May 2026. Geoffrey Hinton is affiliated with Emeritus Prof. Computer Science, University of Toronto.
Geoffrey Hinton is a researcher affiliated with Emeritus Prof. Computer Science, University of Toronto, specializing in machine learning, psychology, artificial intelligence. Their work has been cited 1,035,072 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Geoffrey Hinton's Citation Metrics
Bibliometric impact based on 776 indexed publications.
- H-Index
- 190
- i10-Index
- 527
- Total Citations
- 1,035,072
- Citing Countries
- 78
As of May 2026.
Geoffrey Hinton has an h-index of 190 and 1,035,072 total citations across 776 publications, with research cited by institutions in 78 countries.
Global Impact Map
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Top Cited Works
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ImageNet classification with deep convolutional neural networks
2012194,363
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
3289 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher introduced Dropout as a foundational regularization technique for neural networks, subsequently advancing the field through seminal work on knowledge distillation and comprehensive deep learning synthesis.
The researcher pioneered deep convolutional neural networks for image classification and advanced visual representation learning through contrastive methods and normalization techniques.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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