Melody Y. Guan
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Electrical and Electronic Engineering
- Signal Processing
- Computer Networks and Communications
- Co-authors
- Hieu PhamBarret ZophJeff DeanQuoc V. LeHeinrich JiangMaya R. GuptaBeen KimEmmett D. Goodman
- Topics
- Adversarial Robustness in Machine Learning (2 papers)Machine Learning and Algorithms (2 papers)Statistical Methods and Inference (1 paper)
- Journals
- JAMA SurgeryarXiv (Cornell University)eScholarship (California Digital Library)
- Partner nations
- United States
In The Last Decade
Melody Y. Guan
7 papers receiving 635 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Artificial Intelligence 479
- Computer Vision and Pattern Recognition 428
- Electrical and Electronic Engineering 52
- Signal Processing 31
- Computer Networks and Communications 28
Countries citing papers authored by Melody Y. Guan
This map shows the geographic impact of Melody Y. Guan'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 Melody Y. Guan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Melody Y. Guan more than expected).
Fields of papers citing papers by Melody Y. Guan
This network shows the impact of papers produced by Melody Y. Guan. 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 Melody Y. Guan. The network helps show where Melody Y. Guan may publish in the future.
Co-authorship network of co-authors of Melody Y. Guan
This figure shows the co-authorship network connecting the top 25 collaborators of Melody Y. Guan. A scholar is included among the top collaborators of Melody Y. Guan 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 Melody Y. Guan. Melody Y. Guan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 22 | |
| 3 | A Surprising Density of Illusionable Natural Speech. | 2 |
| 4 | Faster Discovery of Neural Architectures by Searching for Paths in a Large Model | 6 |
| 5 | To Trust Or Not To Trust A Classifier | 35 |
| 6 | Efficient Neural Architecture Search via Parameters Sharingbreakdown → | 598 |
| 7 | 4 |
About Melody Y. Guan
Melody Y. Guan is a scholar working on Health Informatics, Artificial Intelligence and Statistics and Probability, having authored 7 papers that have together received 671 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (2 papers), Machine Learning and Algorithms (2 papers) and Statistical Methods and Inference (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (428 citations), Artificial Intelligence (479 citations) and Health Informatics (11 citations). Melody Y. Guan has collaborated with scholars based in United States. Frequent co-authors include Hieu Pham, Barret Zoph, Jeff Dean, Quoc V. Le, Heinrich Jiang, Maya R. Gupta, Been Kim, Emmett D. Goodman, C.J. Kennedy and Orr Zohar. Their work appears in journals such as JAMA Surgery, arXiv (Cornell University) and eScholarship (California Digital Library).
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.