Vihari Piratla
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition
- Information Systems
- Modeling and Simulation
- Epidemiology
- Co-authors
- Sunita SarawagiSiddhartha ChaudhuriShiv ShankarPreethi JyothiSoumen ChakrabartiPíetro LióAnkit BansalSarita Azad
- Topics
- Domain Adaptation and Few-Shot Learning (2 papers)COVID-19 epidemiological studies (1 paper)Personal Information Management and User Behavior (1 paper)
- Journals
- Infection Disease & HealtharXiv (Cornell University)International Conference on Learning Representations
- Partner nations
- IndiaUnited KingdomUnited States
In The Last Decade
Vihari Piratla
5 papers receiving 125 citations
Peers
Comparison fields: 5 of 35
- Artificial Intelligence 104
- Computer Vision and Pattern Recognition 48
- Information Systems 10
- Modeling and Simulation 9
- Epidemiology 5
Countries citing papers authored by Vihari Piratla
This map shows the geographic impact of Vihari Piratla'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 Vihari Piratla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vihari Piratla more than expected).
Fields of papers citing papers by Vihari Piratla
This network shows the impact of papers produced by Vihari Piratla. 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 Vihari Piratla. The network helps show where Vihari Piratla may publish in the future.
Co-authorship network of co-authors of Vihari Piratla
This figure shows the co-authorship network connecting the top 25 collaborators of Vihari Piratla. A scholar is included among the top collaborators of Vihari Piratla 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 Vihari Piratla. Vihari Piratla is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Efficient Domain Generalization via Common-Specific Low-Rank Decomposition | 5 |
| 2 | 75 | |
| 3 | Generalizing Across Domains via Cross-Gradient Training | 38 |
| 4 | 10 | |
| 5 | 5 |
About Vihari Piratla
Vihari Piratla is a scholar working on Modeling and Simulation, Human-Computer Interaction and Information Systems and Management, having authored 5 papers that have together received 133 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (2 papers), COVID-19 epidemiological studies (1 paper) and Personal Information Management and User Behavior (1 paper). The work is most often cited by research in Artificial Intelligence (104 citations), Computer Vision and Pattern Recognition (48 citations) and Modeling and Simulation (9 citations). Vihari Piratla has collaborated with scholars based in India, United Kingdom and United States. Frequent co-authors include Sunita Sarawagi, Siddhartha Chaudhuri, Shiv Shankar, Preethi Jyothi, Soumen Chakrabarti, Píetro Lió, Ankit Bansal, Sarita Azad, Praneeth Netrapalli and Sudheendra Hangal. Their work appears in journals such as Infection Disease & Health, arXiv (Cornell University) and International Conference on Learning Representations.
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.