David S. Rosenberg
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 5%
- Management Information Systems top 5%
- Strategy and Management
- Computational Mechanics
- Co-authors
- Vikas SindhwaniPeter L. BartlettYuntian DengGideon MannPartha NiyogiRobert E. SchapireDan KleinTony Jebara
- Topics
- Domain Adaptation and Few-Shot Learning (4 papers)Sparse and Compressive Sensing Techniques (3 papers)3D Shape Modeling and Analysis (2 papers)
- Cited by
- Management Information SystemsComputer Vision and Pattern RecognitionArtificial Intelligence
- Journals
- IEEE Signal Processing MagazineIIE TransactionsarXiv (Cornell University)
- Partner nations
- United States
In The Last Decade
David S. Rosenberg
11 papers receiving 340 citations
Peers
Comparison fields: 5 of 52
- Artificial Intelligence 178
- Computer Vision and Pattern Recognition 163
- Management Information Systems 100
- Strategy and Management 45
- Computational Mechanics 44
Countries citing papers authored by David S. Rosenberg
This map shows the geographic impact of David S. Rosenberg'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 David S. Rosenberg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David S. Rosenberg more than expected).
Fields of papers citing papers by David S. Rosenberg
This network shows the impact of papers produced by David S. Rosenberg. 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 David S. Rosenberg. The network helps show where David S. Rosenberg may publish in the future.
Co-authorship network of co-authors of David S. Rosenberg
This figure shows the co-authorship network connecting the top 25 collaborators of David S. Rosenberg. A scholar is included among the top collaborators of David S. Rosenberg 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 David S. Rosenberg. David S. Rosenberg is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 20 | |
| 2 | Collaborative place models | 4 |
| 3 | Collaborative Ranking for Local Preferences | 1 |
| 4 | 3 | |
| 5 | Multiview point cloud kernels for semisupervised learning | 3 |
| 6 | 21 | |
| 7 | 157 | |
| 8 | Semi-supervised learning with multiple views | 7 |
| 9 | The Rademacher Complexity of Co-Regularized Kernel Classes | 40 |
| 10 | 14 | |
| 11 | 88 |
About David S. Rosenberg
David S. Rosenberg is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Management Information Systems, having authored 11 papers that have together received 358 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Sparse and Compressive Sensing Techniques (3 papers) and 3D Shape Modeling and Analysis (2 papers). The work is most often cited by research in Management Information Systems (100 citations), Computer Vision and Pattern Recognition (163 citations) and Artificial Intelligence (178 citations). David S. Rosenberg has collaborated with scholars based in United States. Frequent co-authors include Vikas Sindhwani, Peter L. Bartlett, Yuntian Deng, Gideon Mann, Partha Niyogi, Robert E. Schapire, Dan Klein, Tony Jebara and Ben Taskar. Their work appears in journals such as IEEE Signal Processing Magazine, IIE Transactions 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.