Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation
2011440 citationsAndrew Wagner, Arvind Ganesh et al.IEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Hossein Mobahi
Since
Specialization
Citations
This map shows the geographic impact of Hossein Mobahi'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 Hossein Mobahi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hossein Mobahi more than expected).
This network shows the impact of papers produced by Hossein Mobahi. 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 Hossein Mobahi. The network helps show where Hossein Mobahi may publish in the future.
Co-authorship network of co-authors of Hossein Mobahi
This figure shows the co-authorship network connecting the top 25 collaborators of Hossein Mobahi.
A scholar is included among the top collaborators of Hossein Mobahi based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Hossein Mobahi. Hossein Mobahi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yun, Chulhee, Shankar Krishnan, & Hossein Mobahi. (2021). A unifying view on implicit bias in training linear neural networks. International Conference on Learning Representations.2 indexed citations
4.
Jiang, Yiding, Behnam Neyshabur, Dilip Krishnan, Hossein Mobahi, & Samy Bengio. (2020). Fantastic Generalization Measures and Where to Find Them. International Conference on Learning Representations.8 indexed citations
5.
Mobahi, Hossein, Mehrdad Farajtabar, & Peter L. Bartlett. (2020). Self-Distillation Amplifies Regularization in Hilbert Space. Neural Information Processing Systems. 33. 3351–3361.16 indexed citations
6.
Jiang, Yiding, Manik Sharma, Carlos Lassance, et al.. (2020). Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. 170–190.1 indexed citations
7.
Jiang, Yiding, Dilip Krishnan, Hossein Mobahi, & Samy Bengio. (2019). A Margin-Based Measure of Generalization for Deep Networks.1 indexed citations
8.
Jiang, Yiding, Dilip Krishnan, Hossein Mobahi, & Samy Bengio. (2018). Predicting the Generalization Gap in Deep Networks with Margin Distributions. International Conference on Learning Representations.7 indexed citations
Mobahi, Hossein & Yi Ma. (2012). Gaussian Smoothing and Asymptotic Convexity. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign).1 indexed citations
Wagner, Andrew, et al.. (2011). Toward a Practical Face Recognition System: Robust Alignment and Illumination by Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(2). 372–386.440 indexed citations breakdown →
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.