Kilian Weinberger: h-index, Total Citations, and Citation Map
Kilian Weinberger's h-index is 97 (203 i10-index, 159,718+ total citations across 342+ publications) according to Google Scholar as of August 2026. Kilian Weinberger is affiliated with Cornell University.
Kilian Weinberger is a researcher affiliated with Cornell University, specializing in machine learning, deep learning, metric learning. Their work has been cited 159,718 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Kilian Weinberger's Citation Metrics
Bibliometric impact based on 342 indexed publications.
- H-Index
- 97
- i10-Index
- 203
- Total Citations
- 159,718
- Citing Countries
- 73
As of August 2026.
Kilian Weinberger has an h-index of 97 and 159,718 total citations across 342 publications, with research cited by institutions in 73 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Kilian Weinberger'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.
Densely connected convolutional networks
201765,352
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.
5 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed influential methods for measuring semantic similarity in text, progressing from document-level distances to neural evaluation metrics for text generation.
The researcher introduced densely connected convolutional networks, a highly cited architectural innovation that has become a foundational standard in deep learning research.
The researcher developed a kernel-based approach for nonlinear dimensionality reduction, establishing a foundational method for mapping complex data into lower-dimensional spaces while preserving structural relationships.
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 Kilian Weinberger's research
Kilian Weinberger is a researcher in machine learning, deep learning and metric learning at Cornell University. Their work has been cited 159,718 times across 342 publications (h-index 97), according to Google Scholar.
Their most-cited work, “Densely connected convolutional networks” (2017), has accumulated 65,352 citations. Other influential works include “On calibration of modern neural networks” (2017) with 12,402 citations and “Bertscore: Evaluating text generation with bert” (2019) with 12,052 citations.
Citations of Kilian Weinberger's research come primarily from China, United States 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.
