David Szepesvári
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Advanced Vision and Imaging
- Image Processing and 3D Reconstruction
- Advanced Neural Network Applications
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- Domain Adaptation and Few-Shot Learning
Papers in
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- Advanced Image and Video Retrieval Techniques 1
- Generative Adversarial Networks and Image Synthesis 1
- Image Retrieval and Classification Techniques 1
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- Explainable Artificial Intelligence (XAI) 1
- Reinforcement Learning in Robotics 1
- Co-authors
- Théophane Weber (1 shared paper)Nicolas Heess (1 shared paper)Yuval Tassa (1 shared paper)S. M. Ali Eslami (1 shared paper)Koray Kavukcuoglu (1 shared paper)Geoffrey E. Hinton (1 shared paper)Marlos C. Machado (1 shared paper)Adam White (1 shared paper)
- Journals
- Artificial Intelligence (1 paper)
- Partner nations
- United KingdomCanada
In The Last Decade
David Szepesvári
2 papers receiving 33 citations
Peers
Comparison fields: 5 of 18
- Computer Vision and Pattern Recognition 25
- Artificial Intelligence 13
- Computer Graphics and Computer-Aided Design 1
- Computational Mechanics 5
- Industrial and Manufacturing Engineering 2
Countries citing papers authored by David Szepesvári
This map shows the geographic impact of David Szepesvári'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 David Szepesvári with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Szepesvári more than expected).
Fields of papers citing papers by David Szepesvári
This network shows the impact of papers produced by David Szepesvári. 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 David Szepesvári. The network helps show where David Szepesvári may publish in the future.
Co-authors
The 10 scholars most cited alongside David Szepesvári, 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 | Attend, infer, repeat: fast scene understanding with generative models | 2016 | 31 |
| 2 | 2023 | 2 |
About David Szepesvári
David Szepesvári is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 2 papers that have together received 33 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (1 paper), Explainable Artificial Intelligence (XAI) (1 paper), Reinforcement Learning in Robotics (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Image Retrieval and Classification Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (25 citations), Artificial Intelligence (13 citations), Computer Graphics and Computer-Aided Design (1 citation), Computational Mechanics (5 citations) and Industrial and Manufacturing Engineering (2 citations). David Szepesvári has collaborated with scholars based in United Kingdom and Canada. Frequent co-authors include Théophane Weber, Nicolas Heess, Yuval Tassa, S. M. Ali Eslami, Koray Kavukcuoglu, Geoffrey E. Hinton, Marlos C. Machado, Adam White, Richard S. Sutton and B. K. Tanner. Their work appears in journals such as Artificial Intelligence.
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.