Gabriel Goh
- Artificial Intelligence top 5%
- Modeling and Simulation top 2%
- Computer Vision and Pattern Recognition top 10%
- Economics and Econometrics top 10%
- Infectious Diseases
- Co-authors
- Nick CammarataChris OlahLudwig SchubertMichael PetrovChelsea VossShan CarterAlec RadfordChristian L. Althaus
- Topics
- Adversarial Robustness in Machine Learning (4 papers)Anomaly Detection Techniques and Applications (3 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Journals
- European Journal of EpidemiologyEducational Research for Policy and PracticeNeural Information Processing Systems
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Gabriel Goh
16 papers receiving 532 citations
Peers
Comparison fields: 5 of 116
- Artificial Intelligence 257
- Modeling and Simulation 134
- Computer Vision and Pattern Recognition 112
- Economics and Econometrics 72
- Infectious Diseases 58
Countries citing papers authored by Gabriel Goh
This map shows the geographic impact of Gabriel Goh'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 Gabriel Goh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gabriel Goh more than expected).
Fields of papers citing papers by Gabriel Goh
This network shows the impact of papers produced by Gabriel Goh. 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 Gabriel Goh. The network helps show where Gabriel Goh may publish in the future.
Co-authorship network of co-authors of Gabriel Goh
This figure shows the co-authorship network connecting the top 25 collaborators of Gabriel Goh. A scholar is included among the top collaborators of Gabriel Goh 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 Gabriel Goh. Gabriel Goh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | Zero-Shot Text-to-Image Generation | 6 |
| 3 | 5 | |
| 4 | 115 | |
| 5 | 8 | |
| 6 | 8 | |
| 7 | 21 | |
| 8 | 177 | |
| 9 | 92 | |
| 10 | 11 | |
| 11 | 12 | |
| 12 | 12 | |
| 13 | 23 | |
| 14 | 2 | |
| 15 | 12 | |
| 16 | 51 | |
| 17 | Satisfying real-world goals with dataset constraints | 9 |
About Gabriel Goh
Gabriel Goh is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Modeling and Simulation, having authored 17 papers that have together received 564 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (4 papers), Anomaly Detection Techniques and Applications (3 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Modeling and Simulation (134 citations), Health Informatics (21 citations) and Artificial Intelligence (257 citations). Gabriel Goh has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Nick Cammarata, Chris Olah, Ludwig Schubert, Michael Petrov, Chelsea Voss, Shan Carter, Alec Radford, Christian L. Althaus, Kevin Heng and Daniel D. Reidpath. Their work appears in journals such as European Journal of Epidemiology, Educational Research for Policy and Practice 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.