Vikas Verma
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- Generative Adversarial Networks and Image Synthesis 3
- Advanced Neural Network Applications 2
- Artificial Intelligence top 10%
- Adversarial Robustness in Machine Learning 8
- Anomaly Detection Techniques and Applications 6
- Domain Adaptation and Few-Shot Learning 6
- Neural Networks and Applications 2
- Advanced Graph Neural Networks 2
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- COVID-19 epidemiological studies 1
- Journals
- Neural Networks (1 paper)Seoul National University Open Repository (Seoul National University) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- FinlandUnited StatesAlgeria
In The Last Decade
Vikas Verma
13 papers receiving 230 citations
Peers
Comparison fields: 5 of 50
- Computer Vision and Pattern Recognition 119
- Artificial Intelligence 181
- Signal Processing 12
- Media Technology 9
- Statistical and Nonlinear Physics 12
Countries citing papers authored by Vikas Verma
This map shows the geographic impact of Vikas Verma'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 Vikas Verma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vikas Verma more than expected).
Fields of papers citing papers by Vikas Verma
This network shows the impact of papers produced by Vikas Verma. 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 Vikas Verma. The network helps show where Vikas Verma may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Vikas Verma, 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 | 2022 | 11 | |
| 2 | 2022 | 19 | |
| 3 | 2021 | 61 | |
| 4 | 2021 | 48 | |
| 5 | 2020 | 5 | |
| 6 | Adversarial Mixup Resynthesizers | 2019 | 9 |
| 7 | GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning | 2019 | 22 |
| 8 | 2019 | 5 | |
| 9 | 2019 | 26 | |
| 10 | Towards Understanding Generalization via Analytical Learning Theory | 2018 | 2 |
| 11 | Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer. | 2018 | 19 |
| 12 | Manifold Mixup: Learning Better Representations by Interpolating Hidden States | 2018 | 11 |
| 13 | TIME AND PLACE DISTRIBUTION OFACUTE ENCEPHALITIS SYNDROME (AES) JAPANESE ENCEPHALITIS (JE) CASES IN GORAKHPUR | 2013 | 3 |
About Vikas Verma
Vikas Verma is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Modeling and Simulation, having authored 13 papers that have together received 241 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (8 papers), Anomaly Detection Techniques and Applications (6 papers), Domain Adaptation and Few-Shot Learning (6 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Neural Networks and Applications (2 papers), Advanced Graph Neural Networks (2 papers), Advanced Neural Network Applications (2 papers) and COVID-19 epidemiological studies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (119 citations), Artificial Intelligence (181 citations) and Signal Processing (12 citations). Vikas Verma has collaborated with scholars based in Finland, United States and Algeria. Frequent co-authors include Juho Kannala, Alex Lamb, Yoshua Bengio, Meng Qu, Jian Tang, Kenji Kawaguchi, Jisoo Jeong, Nojun Kwak, Christopher Beckham and Aaron Courville. Their work appears in journals such as Neural Networks, Seoul National University Open Repository (Seoul National University) 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.