Felipe Petroski Such
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- Advanced Neural Network Applications 2
- Handwritten Text Recognition Techniques 1
- Image Processing and 3D Reconstruction 1
- Media Technology top 10%
- Artificial Intelligence top 10%
- Reinforcement Learning in Robotics 2
- Machine Learning and Data Classification 1
- Metaheuristic Optimization Algorithms Research 1
- Neural Networks and Applications 1
- Explainable Artificial Intelligence (XAI) 1
- Co-authors
- Raymond PtuchaShagan SahChao ZhangAndrew MichaelNathan D. CahillJoel LehmanKenneth O. StanleyVashisht Madhavan
- Journals
- IEEE Journal of Selected Topics in Signal Processing (1 paper)Pattern Recognition (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesDenmark
In The Last Decade
Felipe Petroski Such
7 papers receiving 263 citations
Peers
Comparison fields: 5 of 75
- Computer Vision and Pattern Recognition 123
- Media Technology 43
- Artificial Intelligence 123
- Human-Computer Interaction 21
- Computer Graphics and Computer-Aided Design 6
Countries citing papers authored by Felipe Petroski Such
This map shows the geographic impact of Felipe Petroski Such'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 Felipe Petroski Such with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Petroski Such more than expected).
Fields of papers citing papers by Felipe Petroski Such
This network shows the impact of papers produced by Felipe Petroski Such. 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 Felipe Petroski Such. The network helps show where Felipe Petroski Such may publish in the future.
Co-authorship network
The 13 scholars most cited alongside Felipe Petroski Such, 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 | 2020 | 10 | |
| 2 | Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data | 2020 | 7 |
| 3 | Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents | 2018 | 51 |
| 4 | 2018 | 105 | |
| 5 | 2017 | 2 | |
| 6 | 2017 | 95 | |
| 7 | 2017 | 9 |
About Felipe Petroski Such
Felipe Petroski Such is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Environmental Engineering, having authored 7 papers that have together received 279 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (2 papers), Advanced Neural Network Applications (2 papers), Machine Learning and Data Classification (1 paper), Handwritten Text Recognition Techniques (1 paper), Metaheuristic Optimization Algorithms Research (1 paper), Neural Networks and Applications (1 paper), Image Processing and 3D Reconstruction (1 paper) and Explainable Artificial Intelligence (XAI) (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (123 citations), Media Technology (43 citations) and Artificial Intelligence (123 citations). Felipe Petroski Such has collaborated with scholars based in United States and Denmark. Frequent co-authors include Raymond Ptucha, Shagan Sah, Chao Zhang, Andrew Michael, Nathan D. Cahill, Joel Lehman, Kenneth O. Stanley, Vashisht Madhavan, Jeff Clune and Edoardo Conti. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, Pattern Recognition, Proceedings of the AAAI Conference on Artificial Intelligence and Neural Information Processing Systems.
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.