Felipe Petroski Such
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Media Technology top 10%
- Cognitive Neuroscience
- Computational Theory and Mathematics
- Co-authors
- Raymond PtuchaShagan SahChao ZhangAndrew MichaelNathan D. CahillJoel LehmanKenneth O. StanleyVashisht Madhavan
- Topics
- Reinforcement Learning in Robotics (2 papers)Advanced Neural Network Applications (2 papers)Machine Learning and Data Classification (1 paper)
- Journals
- Pattern RecognitionIEEE Journal of Selected Topics in Signal ProcessingNeural Information Processing Systems
- 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
- Artificial Intelligence 123
- Media Technology 43
- Cognitive Neuroscience 31
- Computational Theory and Mathematics 21
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 of co-authors of Felipe Petroski Such
This figure shows the co-authorship network connecting the top 25 collaborators of Felipe Petroski Such. A scholar is included among the top collaborators of Felipe Petroski Such based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Felipe Petroski Such. Felipe Petroski Such is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 10 | |
| 2 | Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data | 7 |
| 3 | Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents | 51 |
| 4 | 105 | |
| 5 | 2 | |
| 6 | 95 | |
| 7 | 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) and Machine Learning and Data Classification (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 Pattern Recognition, IEEE Journal of Selected Topics in Signal Processing 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.