Vaishaal Shankar
- Artificial Intelligence top 10%
- Computer Networks and Communications top 10%
- Information Systems top 10%
- Computer Vision and Pattern Recognition top 10%
- Astronomy and Astrophysics top 10%
- Co-authors
- Benjamin RechtLudwig SchmidtRebecca RoelofsEric JonasMonica BobraJ. T. HoeksemaIan BolligerSolomon Hsiang
- Topics
- Domain Adaptation and Few-Shot Learning (4 papers)COVID-19 diagnosis using AI (3 papers)Topic Modeling (2 papers)
- Journals
- Systematic BiologySolar PhysicsarXiv (Cornell University)
- Partner nations
- United StatesIsraelSwitzerland
In The Last Decade
Vaishaal Shankar
14 papers receiving 377 citations
Peers
Comparison fields: 5 of 92
- Artificial Intelligence 168
- Computer Networks and Communications 98
- Information Systems 84
- Computer Vision and Pattern Recognition 82
- Astronomy and Astrophysics 63
Countries citing papers authored by Vaishaal Shankar
This map shows the geographic impact of Vaishaal Shankar'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 Vaishaal Shankar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vaishaal Shankar more than expected).
Fields of papers citing papers by Vaishaal Shankar
This network shows the impact of papers produced by Vaishaal Shankar. 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 Vaishaal Shankar. The network helps show where Vaishaal Shankar may publish in the future.
Co-authorship network of co-authors of Vaishaal Shankar
This figure shows the co-authorship network connecting the top 25 collaborators of Vaishaal Shankar. A scholar is included among the top collaborators of Vaishaal Shankar 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 Vaishaal Shankar. Vaishaal Shankar 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 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 13 | |
| 6 | Measuring Robustness to Natural Distribution Shifts in Image Classification | 11 |
| 7 | 7 | |
| 8 | 74 | |
| 9 | Evaluating Machine Accuracy on ImageNet | 21 |
| 10 | 57 | |
| 11 | Do ImageNet Classifiers Generalize to ImageNet | 38 |
| 12 | A Meta-Analysis of Overfitting in Machine Learning | 53 |
| 13 | When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet | 2 |
| 14 | 66 | |
| 15 | 2 | |
| 16 | 53 |
About Vaishaal Shankar
Vaishaal Shankar is a scholar working on Health Informatics, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 16 papers that have together received 401 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), COVID-19 diagnosis using AI (3 papers) and Topic Modeling (2 papers). The work is most often cited by research in Artificial Intelligence (168 citations), Signal Processing (54 citations) and Computer Networks and Communications (98 citations). Vaishaal Shankar has collaborated with scholars based in United States, Israel and Switzerland. Frequent co-authors include Benjamin Recht, Ludwig Schmidt, Rebecca Roelofs, Eric Jonas, Monica Bobra, J. T. Hoeksema, Ian Bolliger, Solomon Hsiang, Tamma Carleton and Jonathan Proctor. Their work appears in journals such as Systematic Biology, Solar Physics and arXiv (Cornell University).
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.