Max Vladymyrov
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Molecular Biology
- Computational Mechanics
- Media Technology
- Topics
- Face and Expression Recognition (4 papers)Stochastic Gradient Optimization Techniques (3 papers)Remote-Sensing Image Classification (3 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)Neural Information Processing Systems
- Partner nations
- United StatesSpain
In The Last Decade
Max Vladymyrov
9 papers receiving 87 citations
Peers
Comparison fields: 5 of 34
- Computer Vision and Pattern Recognition 59
- Artificial Intelligence 48
- Molecular Biology 13
- Computational Mechanics 10
- Media Technology 10
Countries citing papers authored by Max Vladymyrov
This map shows the geographic impact of Max Vladymyrov'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 Max Vladymyrov with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Vladymyrov more than expected).
Fields of papers citing papers by Max Vladymyrov
This network shows the impact of papers produced by Max Vladymyrov. 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 Max Vladymyrov. The network helps show where Max Vladymyrov may publish in the future.
Co-authorship network of co-authors of Max Vladymyrov
This figure shows the co-authorship network connecting the top 25 collaborators of Max Vladymyrov. A scholar is included among the top collaborators of Max Vladymyrov 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 Max Vladymyrov. Max Vladymyrov is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 32 | |
| 3 | 1 | |
| 4 | The variational Nyström method for large-scale spectral problems | 7 |
| 5 | A fast, universal algorithm to learn parametric nonlinear embeddings | 4 |
| 6 | Linear-time Training of Nonlinear Low-Dimensional Embeddings | 9 |
| 7 | Entropic Affinities: Properties and Efficient Numerical Computation | 21 |
| 8 | Fast Training of Nonlinear Embedding Algorithms. | 1 |
| 9 | 15 |
About Max Vladymyrov
Max Vladymyrov is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Computer Science Applications, having authored 9 papers that have together received 91 indexed citations. Recurring topics across this work include Face and Expression Recognition (4 papers), Stochastic Gradient Optimization Techniques (3 papers) and Remote-Sensing Image Classification (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (59 citations), Artificial Intelligence (48 citations) and Media Technology (10 citations). Max Vladymyrov has collaborated with scholars based in United States and Spain. Frequent co-authors include Miguel Á. Carreira-Perpiñán, Andrey Zhmoginov, M. Sandler, Andrew Jackson and Nolan Miller. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and Neural Information Processing Systems.
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.