Yoshua Bengio

Yoshua Bengio is a researcher affiliated with Professor of computer science, University of Montreal, Mila, IVADO, CIFAR, specializing in Deep Learning. Their work has been cited 1,082,146 times. This profile visualizes their global influence, highlighting strong citation networks in GB.

Deep Learning

0h-index

0i10-index

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Professor of computer science, University of Montreal, Mila, IVADO, CIFAR•

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Yoshua Bengio's Citation Metrics

Bibliometric impact based on 1 indexed publication.

H-Index: 1
i10-Index: 1
Total Citations: 1,082,146
Citing Countries: 3

As of April 2026.

Yoshua Bengio has an h-index of 1 and 1,082,146 total citations across 1 publication, with research cited by institutions in 3 countries.

Global Impact Map

Visualizing the geographic distribution of institutions that have cited your work.

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Top Citing Countries(Limited)

United Kingdom(GB)

United States(US)

China(CN)

Top Citing Institutions(Limited)

Stability AI

Massachusetts Institute of Technology

Google DeepMind

Tsinghua University

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.

Citer Influence Network

Visa-ready visualisation: each outer ring node is a country citing this scholar. Node size & edge weight scale with citing-paper count. Showing top 3 of 3 countries (17 total citations).

Scholar

Yoshua Bengio

173

United Kingdom

United States

China

React Flow

Letter of Support — Candidate Recommenders

Ranked by EB-1A / O-1A relevance: independence from your home institution, affiliation with a top-ranked institution, repeat citations of your work, and recency. Reach out to the highest-scoring candidates to request support letters.

#1
Mingyang Deng
8.5
Massachusetts Institute of Technology
United States· Cited your work 1 time
IndependentTop institutionDiversifies geography
#2
Kaiming He
8.5
Massachusetts Institute of Technology
United States· Cited your work 1 time
IndependentTop institutionDiversifies geography
#3
Yonglong Tian
7.0
Google DeepMind
United Kingdom· Cited your work 1 time
IndependentTop institution
#4
He Li
8.5
Tsinghua University
China· Cited your work 1 time
IndependentTop institutionDiversifies geography
#5
Robin Rombach
5.0
Stability AI
United Kingdom· Cited your work 1 time
Independent
#6
Tianhong Li
8.5
Massachusetts Institute of Technology
United States· Cited your work 1 time
IndependentTop institutionDiversifies geography
#7
Yam Levi
5.0
Stability AI
United Kingdom· Cited your work 1 time
Independent
#8
Zion English
5.0
Stability AI
United Kingdom· Cited your work 1 time
Independent
#9
Vikram Voleti
5.0
Stability AI
United Kingdom· Cited your work 1 time
Independent
#10
Adam Letts
5.0
Stability AI
United Kingdom· Cited your work 1 time
Independent

Scores combine 5 signals: independence (+3), prestige institution (+2), repeat citations (+2 per paper, cap 5), recent activity (+1), and geography diversity (+1.5). Identity matching uses name + institution; namesakes at the same org are merged.

Cited by 5 papers

5 / 5 independent

Citations flagged as non-independent share the scholar's home institution (Université de Montréal). EB-1A & O-1A petitions typically quote the independent-citation count as the stronger evidence of outside recognition.

(title pending — citation is still being processed)
· cites “Generative Adversarial Nets”
(title pending — citation is still being processed)
· cites “Generative Adversarial Nets”
(title pending — citation is still being processed)
· cites “Generative Adversarial Nets”
Autoregressive Image Generation without Vector Quantization
Tianhong Li, Mingyang Deng, Kaiming He, Yonglong Tian + 1 more
38th Conference on Neural Information Processing Systems (NeurIPS 2024)· cites “Generative Adversarial Nets”
Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch + 8 more
arXiv (Preprint) / Technical Report· cites “Generative Adversarial Nets”