Neelu Madan
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Industrial and Manufacturing Engineering top 10%
- Control and Systems Engineering
- Co-authors
- Kamal NasrollahiThomas B. MoeslundNicolae-Cătălin RisteaRadu Tudor IonescuFahad Shahbaz KhanMubarak ShahSérgio EscaleraI. Nikolov
- Topics
- Anomaly Detection Techniques and Applications (3 papers)Generative Adversarial Networks and Image Synthesis (2 papers)Adversarial Robustness in Machine Learning (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputer Networks and Communications
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceApplied Sciences2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- DenmarkRomaniaUnited Arab Emirates
In The Last Decade
Neelu Madan
6 papers receiving 252 citations
Hit Papers
Peers
Comparison fields: 5 of 47
- Artificial Intelligence 216
- Computer Vision and Pattern Recognition 99
- Computer Networks and Communications 97
- Industrial and Manufacturing Engineering 39
- Control and Systems Engineering 27
Countries citing papers authored by Neelu Madan
This map shows the geographic impact of Neelu Madan'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 Neelu Madan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Neelu Madan more than expected).
Fields of papers citing papers by Neelu Madan
This network shows the impact of papers produced by Neelu Madan. 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 Neelu Madan. The network helps show where Neelu Madan may publish in the future.
Co-authorship network of co-authors of Neelu Madan
This figure shows the co-authorship network connecting the top 25 collaborators of Neelu Madan. A scholar is included among the top collaborators of Neelu Madan 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 Neelu Madan. Neelu Madan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 8 | |
| 2 | 59 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detectionbreakdown → | 177 |
| 6 | 9 |
About Neelu Madan
Neelu Madan is a scholar working on Computer Vision and Pattern Recognition, Biophysics and Artificial Intelligence, having authored 6 papers that have together received 257 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Artificial Intelligence (216 citations), Computer Vision and Pattern Recognition (99 citations) and Computer Networks and Communications (97 citations). Neelu Madan has collaborated with scholars based in Denmark, Romania and United Arab Emirates. Frequent co-authors include Kamal Nasrollahi, Thomas B. Moeslund, Nicolae-Cătălin Ristea, Radu Tudor Ionescu, Fahad Shahbaz Khan, Mubarak Shah, Mubarak Shah, Sérgio Escalera, I. Nikolov and Dirk Söffker. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Sciences and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.