Umut Şimşekli

41 papers receiving 300 citations

Peers

Umut Şimşekli
Comparison fields: 5 of 51
  • Signal Processing 158
  • Computer Vision and Pattern Recognition 105
  • Artificial Intelligence 70
  • Computational Mathematics 68
  • Electrical and Electronic Engineering 43
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Citations per field
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Citations per year

Countries citing papers authored by Umut Şimşekli

Since Specialization
Citations

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

Fields of papers citing papers by Umut Şimşekli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Umut Şimşekli

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 11
3
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
1
4 22
5
Fractional Underdamped Langevin Dynamics: Retargeting SGD with Momentum under Heavy-Tailed Gradient Noise
0
6 2
7
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
2
8
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
1
9
Fractional Langevin Monte Carlo: exploring levy driven stochastic differential equations for Markov Chain Monte Carlo
6
10 8
11
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
6
12 14
13 12
14
Learning the beta-Divergence in Tweedie Compound Poisson Matrix Factorization Models
12
15
Large scale polyphonic music transcription using randomized matrix decompositions
5
16
Score guided musical source separation using Generalized Coupled Tensor Factorization
18
17 10
18
Generalised Coupled Tensor Factorisation
46
19 7
20
BAYESIAN METHODS FOR REAL-TIME PITCH TRACKING
5

About Umut Şimşekli

Umut Şimşekli is a scholar working on Computational Mathematics, Signal Processing and Computer Vision and Pattern Recognition, having authored 42 papers that have together received 319 indexed citations. Recurring topics across this work include Speech and Audio Processing (22 papers), Music and Audio Processing (17 papers) and Music Technology and Sound Studies (10 papers). The work is most often cited by research in Computational Mathematics (68 citations), Signal Processing (158 citations) and Computer Vision and Pattern Recognition (105 citations). Umut Şimşekli has collaborated with scholars based in Türkiye, France and United States. Frequent co-authors include Ali Taylan Cemgil, Gaël Richard, Cumhur Erkut, Antti Jylhä, Beyza Ermiş, Antoine Liutkus, John R. Hershey, Jonathan Le Roux, Alexey Ozerov and Evrim Acar. Their work appears in journals such as Energy and Buildings, IEEE Signal Processing Letters and IEEE Journal of Selected Topics in Signal Processing.

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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