Dino Oglić
Impact in
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- Computational Drug Discovery Methods
Papers in
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- Gaussian Processes and Bayesian Inference 5
- Neural Networks and Applications 3
- Speech Recognition and Synthesis 3
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- Speech and Audio Processing 3
- Music and Audio Processing 3
- Co-authors
- Píetro Lió (3 shared papers)Jon Paul Janet (3 shared papers)Steven John Kiddle (2 shared papers)Thomas Gärtner (6 shared papers)Mihaela van der Schaar (1 shared paper)Steve J. Renals (3 shared papers)Zoran Cvetković (5 shared papers)Peter Bell (2 shared papers)
- Journals
- Nature Communications (2 papers)Journal of Machine Learning Research (2 papers)Communications Chemistry (1 paper)Bioinformatics (1 paper)IEEE Transactions on Biomedical Engineering (1 paper)
- Partner nations
- United KingdomGermanySweden
In The Last Decade
Dino Oglić
19 papers receiving 225 citations
Peers
Comparison fields: 5 of 72
- Computational Mathematics 2
- Computational Theory and Mathematics 51
- Artificial Intelligence 71
- Health Informatics 2
- Computer Vision and Pattern Recognition 29
Countries citing papers authored by Dino Oglić
This map shows the geographic impact of Dino Oglić'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 Dino Oglić with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dino Oglić more than expected).
Fields of papers citing papers by Dino Oglić
This network shows the impact of papers produced by Dino Oglić. 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 Dino Oglić. The network helps show where Dino Oglić may publish in the future.
Co-authors
The 20 scholars most cited alongside Dino Oglić, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2024 | 80 | |
| 2 | 2024 | 39 | |
| 3 | 2018 | 21 | |
| 4 | Towards a Unified Analysis of Random Fourier Features | 2021 | 12 |
| 5 | 2023 | 11 | |
| 6 | 2020 | 10 | |
| 7 | Nyström Method with Kernel K-means++ Samples as Landmarks | 2017 | 8 |
| 8 | 2020 | 7 | |
| 9 | Learning in Reproducing Kernel Kreın Spaces | 2018 | 6 |
| 10 | 2024 | 6 | |
| 11 | 2014 | 5 | |
| 12 | 2018 | 5 | |
| 13 | 2025 | 5 | |
| 14 | 2017 | 4 | |
| 15 | Greedy feature construction | 2016 | 3 |
| 16 | 2022 | 3 | |
| 17 | 2018 | 2 | |
| 18 | 2024 | 2 | |
| 19 | 2024 | 2 |
About Dino Oglić
Dino Oglić is a scholar working on Artificial Intelligence, Signal Processing, Computational Theory and Mathematics, Statistics and Probability and Statistical and Nonlinear Physics, having authored 19 papers that have together received 231 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (5 papers), Computational Drug Discovery Methods (4 papers), Neural Networks and Applications (3 papers), Speech and Audio Processing (3 papers), Speech Recognition and Synthesis (3 papers), Music and Audio Processing (3 papers), Statistical Methods and Inference (2 papers) and Machine Learning in Materials Science (2 papers). The work is most often cited by research in Computational Mathematics (2 citations), Computational Theory and Mathematics (51 citations), Artificial Intelligence (71 citations), Health Informatics (2 citations) and Computer Vision and Pattern Recognition (29 citations). Dino Oglić has collaborated with scholars based in United Kingdom, Germany and Sweden. Frequent co-authors include Píetro Lió, Jon Paul Janet, Steven John Kiddle, Thomas Gärtner, Mihaela van der Schaar, Steve J. Renals, Zoran Cvetković, Peter Bell, Dino Sejdinović and Roman Garnett. Their work appears in journals such as Nature Communications, Journal of Machine Learning Research, Communications Chemistry, Bioinformatics and IEEE Transactions on Biomedical Engineering.
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.