Utku Evci
Impact in
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- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Generative Adversarial Networks and Image Synthesis
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- Domain Adaptation and Few-Shot Learning
- Neural Networks and Applications
- Adversarial Robustness in Machine Learning
- Machine Learning and ELM
- Topic Modeling
Papers in
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- Domain Adaptation and Few-Shot Learning 3
- Machine Learning and Data Classification 1
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- Advanced Neural Network Applications 3
- Human Pose and Action Recognition 1
- Co-authors
- Jacob Menick (2 shared papers)Yann Dauphin (2 shared papers)Erich Elsen (2 shared papers)Pablo Samuel Castro (1 shared paper)Trevor Gale (1 shared paper)Cem Keskin (1 shared paper)Yani Ioannou (1 shared paper)Levent Sagun (1 shared paper)
- Journals
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (1 paper)Neural Information Processing Systems (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)International Conference on Learning Representations (2 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Utku Evci
6 papers receiving 43 citations
Peers
Comparison fields: 5 of 15
- Computer Vision and Pattern Recognition 29
- Artificial Intelligence 38
- Condensed Matter Physics 2
- Urban Studies 1
- Statistical and Nonlinear Physics 2
Countries citing papers authored by Utku Evci
This map shows the geographic impact of Utku Evci'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 Utku Evci with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Utku Evci more than expected).
Fields of papers citing papers by Utku Evci
This network shows the impact of papers produced by Utku Evci. 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 Utku Evci. The network helps show where Utku Evci may publish in the future.
Co-authors
The 20 scholars most cited alongside Utku Evci, 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 | Rigging the Lottery: Making All Tickets Winners | 2020 | 20 |
| 2 | 2022 | 13 | |
| 3 | Empirical Analysis of the Hessian of Over-Parametrized Neural Networks | 2018 | 8 |
| 4 | Practical Real Time Recurrent Learning with a Sparse Approximation | 2021 | 3 |
| 5 | A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches | 2021 | 2 |
| 6 | 2016 | 1 |
About Utku Evci
Utku Evci is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Control and Systems Engineering and Computational Mechanics, having authored 6 papers that have together received 47 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Medical Imaging Techniques and Applications (1 paper), Sparse and Compressive Sensing Techniques (1 paper), Machine Learning and Data Classification (1 paper), Human Pose and Action Recognition (1 paper), Control and Dynamics of Mobile Robots (1 paper) and Human Motion and Animation (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (29 citations), Artificial Intelligence (38 citations), Condensed Matter Physics (2 citations), Urban Studies (1 citation) and Statistical and Nonlinear Physics (2 citations). Utku Evci has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Jacob Menick, Yann Dauphin, Erich Elsen, Pablo Samuel Castro, Trevor Gale, Cem Keskin, Yani Ioannou, Levent Sagun, Léon Bottou and V. Uğur Güney. Their work appears in journals such as Infoscience (Ecole Polytechnique Fédérale de Lausanne), Neural Information Processing Systems, Proceedings of the AAAI Conference on Artificial Intelligence, International Conference on Learning Representations and International Conference on Machine Learning.
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.