Maya R. Gupta
-
- Face and Expression Recognition 12
- Signal Processing top 2%
- Speech and Audio Processing 7
- Blind Source Separation Techniques 7
- Artificial Intelligence top 2%
- Bayesian Methods and Mixture Models 10
- Machine Learning and Data Classification 9
- Neural Networks and Applications 9
- Media Technology top 2%
- Statistics and Probability top 2%
- Statistical Methods and Inference 9
-
- Color Science and Applications 9
Maya R. Gupta
88 papers receiving 1.8k citations
Peers
Comparison fields: 5 of 156
- Computer Vision and Pattern Recognition 618
- Signal Processing 275
- Artificial Intelligence 811
- Media Technology 172
- Statistics and Probability 144
Countries citing papers authored by Maya R. Gupta
This map shows the geographic impact of Maya R. Gupta'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 Maya R. Gupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya R. Gupta more than expected).
Fields of papers citing papers by Maya R. Gupta
This network shows the impact of papers produced by Maya R. Gupta. 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 Maya R. Gupta. The network helps show where Maya R. Gupta may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Maya R. Gupta, 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 | Deontological Ethics By Monotonicity Shape Constraints | 2020 | 1 |
| 2 | On Making Stochastic Classifiers Deterministic | 2019 | 1 |
| 3 | Optimizing Generalized Rate Metrics with Three Players | 2019 | 4 |
| 4 | Shape Constraints for Set Functions | 2019 | 5 |
| 5 | Constrained Interacting Submodular Groupings | 2018 | 2 |
| 6 | 2017 | 1 | |
| 7 | Deep Lattice Networks and Partial Monotonic Functions | 2017 | 13 |
| 8 | Fast and Flexible Monotonic Functions with Ensembles of Lattices | 2016 | 10 |
| 9 | 2016 | 37 | |
| 10 | Satisfying real-world goals with dataset constraints | 2016 | 9 |
| 11 | 2014 | 52 | |
| 12 | Contact clustering and fusion for preprocessing multistatic active sonar data | 2013 | 2 |
| 13 | Clutter rejection by clustering likelihood-based similarities | 2011 | 6 |
| 14 | Clustering by Left-Stochastic Matrix Factorization | 2011 | 18 |
| 15 | Shadow Dirichlet for Restricted Probability Modeling | 2010 | 4 |
| 16 | 2009 | 193 | |
| 17 | Fusing similarities and Euclidean features with generative classifiers | 2009 | 2 |
| 18 | Sequential Bayesian estimation of the probability of detection for tracking | 2009 | 13 |
| 19 | Lattice Regression | 2009 | 6 |
| 20 | 2007 | 163 |
About Maya R. Gupta
Maya R. Gupta is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Statistics and Probability, having authored 93 papers that have together received 1.9k indexed citations. Recurring topics across this work include Face and Expression Recognition (12 papers), Bayesian Methods and Mixture Models (10 papers), Statistical Methods and Inference (9 papers), Machine Learning and Data Classification (9 papers), Color Science and Applications (9 papers), Neural Networks and Applications (9 papers), Speech and Audio Processing (7 papers) and Blind Source Separation Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (618 citations), Signal Processing (275 citations) and Artificial Intelligence (811 citations). Maya R. Gupta has collaborated with scholars based in United States, Canada and Japan. Frequent co-authors include Santosh Kumar Srivastava, Eric Garcia, Béla A. Frigyik, Yihua Chen, Luca Cazzanti, Nathaniel Jacobson, Ali Rahimi, Hyrum S. Anderson, Andrew Cotter and Jill Nelson. Their work appears in journals such as Bioinformatics, PLoS ONE and IEEE Transactions on Information Theory.
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.