Vashisht Madhavan
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 2%
- Automotive Engineering top 2%
- Aerospace Engineering top 10%
- Electrical and Electronic Engineering
- Topics
- Advanced Neural Network Applications (2 papers)Domain Adaptation and Few-Shot Learning (2 papers)Multimodal Machine Learning Applications (2 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)Neural Information Processing Systems
- Partner nations
- United StatesDenmark
In The Last Decade
Vashisht Madhavan
3 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 91
- Computer Vision and Pattern Recognition 1.1k
- Artificial Intelligence 521
- Automotive Engineering 337
- Aerospace Engineering 109
- Electrical and Electronic Engineering 93
Countries citing papers authored by Vashisht Madhavan
This map shows the geographic impact of Vashisht Madhavan'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 Vashisht Madhavan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vashisht Madhavan more than expected).
Fields of papers citing papers by Vashisht Madhavan
This network shows the impact of papers produced by Vashisht Madhavan. 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 Vashisht Madhavan. The network helps show where Vashisht Madhavan may publish in the future.
Co-authorship network of co-authors of Vashisht Madhavan
This figure shows the co-authorship network connecting the top 25 collaborators of Vashisht Madhavan. A scholar is included among the top collaborators of Vashisht Madhavan 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 Vashisht Madhavan. Vashisht Madhavan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 25 | |
| 2 | BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningbreakdown → | 1374 |
| 3 | Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents | 51 |
About Vashisht Madhavan
Vashisht Madhavan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases, having authored 3 papers that have together received 1.4k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Automotive Engineering (337 citations) and Artificial Intelligence (521 citations). Vashisht Madhavan has collaborated with scholars based in United States and Denmark. Frequent co-authors include Wenqi Xian, Fisher Yu, Haofeng Chen, Yingying Chen, Fangchen Liu, Trevor Darrell, Xin Wang, Jeff Clune, Felipe Petroski Such and Edoardo Conti. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 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.