Y Gal: h-index, Total Citations, and Citation Map
Y Gal's h-index is 20 (20 i10-index, 73,228+ total citations across 20+ publications) according to Google Scholar as of August 2026. Y Gal is affiliated with Professor of Machine Learning, University of Oxford.
Y Gal is a researcher affiliated with Professor of Machine Learning, University of Oxford, specializing in various fields. Their work has been cited 73,228 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Y Gal's Citation Metrics
Bibliometric impact based on 20 indexed publications.
- H-Index
- 20
- i10-Index
- 20
- Total Citations
- 73,228
- Citing Countries
- 46
As of August 2026.
Y Gal has an h-index of 20 and 73,228 total citations across 20 publications, with research cited by institutions in 46 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Y Gal's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning
20154,095
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.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Y Gal's research
Y Gal is a researcher at Professor of Machine Learning, University of Oxford. Their work has been cited 73,228 times across 20 publications (h-index 20), according to Google Scholar.
Their most-cited work, “Dropout as a Bayesian Approximation: Representing Model Uncertainty in\n Deep Learning” (2015), has accumulated 4,095 citations. Other influential works include “Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics” (2018) with 2,701 citations and “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?” (2017) with 2,528 citations.
Citations of Y Gal's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
