Prasoon Goyal
Impact in
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- Face and Expression Recognition
- Generative Adversarial Networks and Image Synthesis
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- Natural Language Processing Techniques
- Topic Modeling
- Machine Learning and Data Classification
- Domain Adaptation and Few-Shot Learning
- Machine Learning and ELM
- Anomaly Detection Techniques and Applications
Papers in
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- Domain Adaptation and Few-Shot Learning 3
- Machine Learning and ELM 1
- Natural Language Processing Techniques 1
- Semantic Web and Ontologies 1
- Bayesian Modeling and Causal Inference 1
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- Face and Expression Recognition 1
- Co-authors
- Cijo Jose (1 shared paper)Manik Varma (1 shared paper)Xiaodan Liang (1 shared paper)Zhiting Hu (1 shared paper)Chenyu Wang (1 shared paper)Eric P. Xing (1 shared paper)Brian Roark (1 shared paper)Françoise Beaufays (1 shared paper)
- Journals
- Contemporary Clinical Dentistry (1 paper)International Journal of Health Sciences (1 paper)Neural Information Processing Systems (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- IndiaUnited States
In The Last Decade
Prasoon Goyal
6 papers receiving 111 citations
Peers
Comparison fields: 5 of 42
- Computer Vision and Pattern Recognition 56
- Artificial Intelligence 81
- Computational Mathematics 1
- Signal Processing 13
- Media Technology 8
Countries citing papers authored by Prasoon Goyal
This map shows the geographic impact of Prasoon Goyal'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 Prasoon Goyal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prasoon Goyal more than expected).
Fields of papers citing papers by Prasoon Goyal
This network shows the impact of papers produced by Prasoon Goyal. 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 Prasoon Goyal. The network helps show where Prasoon Goyal may publish in the future.
Co-authors
The 19 scholars most cited alongside Prasoon Goyal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Local Deep Kernel Learning for Efficient Non-linear SVM Prediction | 2013 | 53 |
| 2 | 2017 | 41 | |
| 3 | 2017 | 13 | |
| 4 | New Rules for Domain Independent Lifted MAP Inference | 2014 | 6 |
| 5 | Kernel Extraction via Voted Risk Minimization | 2015 | 2 |
| 6 | 2017 | 1 | |
| 7 | 2022 | 0 |
About Prasoon Goyal
Prasoon Goyal is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Physiology, General Health Professions and Periodontics, having authored 7 papers that have together received 116 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (3 papers), Smoking Behavior and Cessation (1 paper), Machine Learning and ELM (1 paper), Natural Language Processing Techniques (1 paper), Face and Expression Recognition (1 paper), Semantic Web and Ontologies (1 paper), Food Security and Health in Diverse Populations (1 paper) and Bayesian Modeling and Causal Inference (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (56 citations), Artificial Intelligence (81 citations), Computational Mathematics (1 citation), Signal Processing (13 citations) and Media Technology (8 citations). Prasoon Goyal has collaborated with scholars based in India and United States. Frequent co-authors include Cijo Jose, Manik Varma, Xiaodan Liang, Zhiting Hu, Chenyu Wang, Eric P. Xing, Brian Roark, Françoise Beaufays, Cyril Allauzen and Parag Singla. Their work appears in journals such as Contemporary Clinical Dentistry, International Journal of Health Sciences, Neural Information Processing Systems and International Conference on Machine Learning.
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.