Jure Sokolić

441 total citations
9 papers, 141 citations indexed

About

Jure Sokolić is a scholar working on Computational Mechanics, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Jure Sokolić has authored 9 papers receiving a total of 141 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computational Mechanics, 5 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Jure Sokolić's work include Sparse and Compressive Sensing Techniques (5 papers), Neural Networks and Applications (4 papers) and Blind Source Separation Techniques (4 papers). Jure Sokolić is often cited by papers focused on Sparse and Compressive Sensing Techniques (5 papers), Neural Networks and Applications (4 papers) and Blind Source Separation Techniques (4 papers). Jure Sokolić collaborates with scholars based in United Kingdom, United States and Israel. Jure Sokolić's co-authors include Miguel R. D. Rodrigues, Guillermo Sapiro, Raja Giryes, Francesco Renna, Andreas Demosthenous, Dai Jiang, Robert Calderbank and Qiang Qiu and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Transactions on Biomedical Circuits and Systems and UCL Discovery (University College London).

In The Last Decade

Jure Sokolić

8 papers receiving 136 citations

Peers

Jure Sokolić

AI

CVPR

CM

SP

EEE

Olga Kouropteva Finland

Gintare Karolina Dziugaite United Kingdom

X. Y. Han United States

Apoorv Vyas Switzerland

Angelos Katharopoulos Switzerland

J. Y. Shi United States

Suraj Srinivas India

Edo Collins Switzerland

Hanshu Yan Singapore

Nicholas Watters United States

Olga Kouropteva Finland

Jure Sokolić

67 ×0.8

AI

125 ×2.0

CVPR

17 ×0.7

CM

32 ×2.1

SP

10 ×0.7

EEE

Citations per year, relative to Jure Sokolić Jure Sokolić (= 1×) peers Olga Kouropteva

Countries citing papers authored by Jure Sokolić

Since Specialization

Citations

This map shows the geographic impact of Jure Sokolić'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 Jure Sokolić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jure Sokolić more than expected).

Fields of papers citing papers by Jure Sokolić

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jure Sokolić. 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 Jure Sokolić. The network helps show where Jure Sokolić may publish in the future.

Co-authorship network of co-authors of Jure Sokolić

This figure shows the co-authorship network connecting the top 25 collaborators of Jure Sokolić. A scholar is included among the top collaborators of Jure Sokolić 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 Jure Sokolić. Jure Sokolić is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Sokolić, Jure, et al.. (2020). Accurate, Very Low Computational Complexity Spike Sorting Using Unsupervised Matched Subspace Learning. IEEE Transactions on Biomedical Circuits and Systems. 14(2). 221–231. 18 indexed citations

2.

Sokolić, Jure, et al.. (2018). On Deep Learning for Inverse Problems. 1895–1899. 6 indexed citations

3.

Sokolić, Jure, Raja Giryes, Guillermo Sapiro, & Miguel R. D. Rodrigues. (2017). Generalization error of deep neural networks: Role of classification margin and data structure. 147–151. 3 indexed citations

4.

Sokolić, Jure, Miguel R. D. Rodrigues, Qiang Qiu, & Guillermo Sapiro. (2017). Learning to Identify While Failing to Discriminate. 2. 2537–2544. 1 indexed citations

5.

Sokolić, Jure, Raja Giryes, Guillermo Sapiro, & Miguel R. D. Rodrigues. (2017). Robust Large Margin Deep Neural Networks. IEEE Transactions on Signal Processing. 65(16). 4265–4280. 100 indexed citations

6.

Sokolić, Jure, Raja Giryes, Guillermo Sapiro, & Miguel R. D. Rodrigues. (2016). Generalization Error of Invariant Classifiers.. UCL Discovery (University College London). 1094–1103. 5 indexed citations

7.

Sokolić, Jure, et al.. (2016). Lessons from the Rademacher complexity for deep learning. UCL Discovery (University College London). 4 indexed citations

8.

Sokolić, Jure, Francesco Renna, Robert Calderbank, & Miguel R. D. Rodrigues. (2016). Mismatch in the Classification of Linear Subspaces: Sufficient Conditions for Reliable Classification. IEEE Transactions on Signal Processing. 64(12). 3035–3050. 3 indexed citations

9.

Sokolić, Jure, Francesco Renna, Robert Calderbank, & Miguel R. D. Rodrigues. (2015). Mismatch in the classification of linear subspaces: Upper bound to the probability of error. 7523. 2201–2205. 1 indexed citations

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.

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