Bogdan Savchynskyy
-
- Graph Theory and Algorithms 4
- Advanced Neural Network Applications 4
- Geology top 10%
- Artificial Intelligence top 10%
- Machine Learning and Algorithms 10
- Bayesian Modeling and Causal Inference 7
- Machine Learning and Data Classification 5
- Aerospace Engineering top 10%
-
- Error Correcting Code Techniques 4
- Constraint Satisfaction and Optimization 3
-
- Complexity and Algorithms in Graphs 3
- Co-authors
- Carsten RotherAlexander KirillovBjoern AndresEvgeny LevinkovChristoph SchnörrJörg Hendrik KappesPaul SwobodaStefan Gumhold
In The Last Decade
Bogdan Savchynskyy
21 papers receiving 534 citations
Peers
Comparison fields: 5 of 77
- Computer Vision and Pattern Recognition 366
- Geology 36
- Artificial Intelligence 200
- Computer Graphics and Computer-Aided Design 16
- Aerospace Engineering 88
Countries citing papers authored by Bogdan Savchynskyy
This map shows the geographic impact of Bogdan Savchynskyy'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 Bogdan Savchynskyy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bogdan Savchynskyy more than expected).
Fields of papers citing papers by Bogdan Savchynskyy
This network shows the impact of papers produced by Bogdan Savchynskyy. 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 Bogdan Savchynskyy. The network helps show where Bogdan Savchynskyy may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Bogdan Savchynskyy, 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 | 2019 | 2 | |
| 2 | 2018 | 81 | |
| 3 | 2018 | 1 | |
| 4 | 2017 | 10 | |
| 5 | 2017 | 26 | |
| 6 | 2017 | 143 | |
| 7 | 2017 | 0 | |
| 8 | 2017 | 67 | |
| 9 | Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization | 2016 | 6 |
| 10 | 2016 | 1 | |
| 11 | 2015 | 7 | |
| 12 | M-Best-Diverse labelings for submodular energies and beyond | 2015 | 7 |
| 13 | 2015 | 77 | |
| 14 | 2015 | 15 | |
| 15 | Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation | 2013 | 7 |
| 16 | 2013 | 4 | |
| 17 | 2012 | 23 | |
| 18 | 2011 | 26 | |
| 19 | Discriminative Learning of Max-Sum Classifiers | 2008 | 13 |
| 20 | 2006 | 2 |
About Bogdan Savchynskyy
Bogdan Savchynskyy is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications, having authored 23 papers that have together received 552 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (10 papers), Bayesian Modeling and Causal Inference (7 papers), Machine Learning and Data Classification (5 papers), Graph Theory and Algorithms (4 papers), Error Correcting Code Techniques (4 papers), Advanced Neural Network Applications (4 papers), Constraint Satisfaction and Optimization (3 papers) and Complexity and Algorithms in Graphs (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (366 citations), Geology (36 citations) and Artificial Intelligence (200 citations). Bogdan Savchynskyy has collaborated with scholars based in Germany, Austria and Russia. Frequent co-authors include Carsten Rother, Alexander Kirillov, Bjoern Andres, Evgeny Levinkov, Christoph Schnörr, Jörg Hendrik Kappes, Paul Swoboda, Stefan Gumhold, Alexander Krull and Eric Brachmann. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and IEEE Signal Processing Magazine.
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.