Shiv Shankar
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Information Systems
- Biomedical Engineering
- Cancer Research
- Co-authors
- Sunita SarawagiSiddhartha ChaudhuriVihari PiratlaPreethi JyothiSoumen ChakrabartiPhilip S. Thomas
- Topics
- Multimodal Machine Learning Applications (4 papers)Domain Adaptation and Few-Shot Learning (3 papers)Natural Language Processing Techniques (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceComputer Graphics and Computer-Aided Design
- Journals
- DSpace (IIT Bombay)arXiv (Cornell University)International Conference on Learning Representations
- Partner nations
- India
In The Last Decade
Shiv Shankar
7 papers receiving 74 citations
Peers
Comparison fields: 5 of 23
- Artificial Intelligence 59
- Computer Vision and Pattern Recognition 48
- Information Systems 6
- Biomedical Engineering 5
- Cancer Research 4
Countries citing papers authored by Shiv Shankar
This map shows the geographic impact of Shiv 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 Shiv Shankar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shiv Shankar more than expected).
Fields of papers citing papers by Shiv Shankar
This network shows the impact of papers produced by Shiv 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 Shiv Shankar. The network helps show where Shiv Shankar may publish in the future.
Co-authorship network of co-authors of Shiv Shankar
This figure shows the co-authorship network connecting the top 25 collaborators of Shiv Shankar. A scholar is included among the top collaborators of Shiv 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 Shiv Shankar. Shiv 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 | 1 | |
| 2 | Posterior Attention Models for Sequence to Sequence Learning | 10 |
| 3 | Generalizing Across Domains via Cross-Gradient Training | 38 |
| 4 | A Comparative Performance Analysis of Cloud, Cluster and Grid Computing over Network | 2 |
| 5 | 23 | |
| 6 | 4 | |
| 7 | 2 |
About Shiv Shankar
Shiv Shankar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Management Science and Operations Research, having authored 7 papers that have together received 80 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Natural Language Processing Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (48 citations), Artificial Intelligence (59 citations) and Computer Graphics and Computer-Aided Design (1 citation). Shiv Shankar has collaborated with scholars based in India. Frequent co-authors include Sunita Sarawagi, Siddhartha Chaudhuri, Vihari Piratla, Preethi Jyothi, Soumen Chakrabarti and Philip S. Thomas. Their work appears in journals such as DSpace (IIT Bombay), 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.