Anastasios Kyrillidis

1.8k citations
58 papers · 717 · 1 hit paper · h-index 13

Impact in

Papers in

    • Stochastic Gradient Optimization Techniques 9
    • Machine Learning and Algorithms 7
    • Quantum Information and Cryptography 6
    • Domain Adaptation and Few-Shot Learning 5
    • Sparse and Compressive Sensing Techniques 27

Anastasios Kyrillidis

53 papers receiving 691 citations

Anastasios Kyrillidis's Hit Papers

Current progress and open challenges for applying deep learning across the biosciences 2022 · 171 citations
1710+1+2Years since publication50100150

Peers

Anastasios Kyrillidis
Comparison fields: 5 of 108
  • Computational Mathematics 46
  • Computational Mechanics 272
  • Signal Processing 118
  • Acoustics and Ultrasonics 8
  • Numerical Analysis 44
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Citations per field
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Citations per year

Countries citing papers authored by Anastasios Kyrillidis

Since Specialization
Citations

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

Fields of papers citing papers by Anastasios Kyrillidis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Anastasios Kyrillidis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Anastasios Kyrillidis Line = papers co-authored together Anastasios Kyrillidis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Current progress and open challenges for applying deep learning across the biosciences
Hit paper breakdown →
2022171
2 201299
3 201349
4 201238
5 201833
6 201130
7 201329
8 201219
9 202218
10 201216
11 201615
12 202312
13 201412
14 201411
15 201110
16 201410
17 202210
18 20189
19 20239
20 20139

About Anastasios Kyrillidis

Anastasios Kyrillidis is a scholar working on Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing and Computational Theory and Mathematics, having authored 58 papers that have together received 717 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (27 papers), Stochastic Gradient Optimization Techniques (9 papers), Advanced Neural Network Applications (8 papers), Machine Learning and Algorithms (7 papers), Image and Signal Denoising Methods (7 papers), Quantum Information and Cryptography (6 papers), Statistical Methods and Inference (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). The work is most often cited by research in Computational Mathematics (46 citations), Computational Mechanics (272 citations), Signal Processing (118 citations), Acoustics and Ultrasonics (8 citations) and Numerical Analysis (44 citations). Anastasios Kyrillidis has collaborated with scholars based in United States, Switzerland and Greece. Frequent co-authors include Volkan Cevher, Nicholas D. Sidiropoulos, C. Wolfe, Chen Dun, Dinler A. Antunes, Mohammadamin Edrisi, Ruth Dannenfelser, R. A. Leo Elworth, Zhi Yan and Vicky Yao. Their work appears in journals such as Quantum Science and Technology, Nature Communications, Journal of Mathematical Imaging and Vision, Machine Learning and Mechanical Systems and 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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