Justin Domke
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Aerospace Engineering
- Media Technology top 10%
- Computational Mechanics
- Co-authors
- Yiannis AloimonosDaniel SheldonAbhinav AgrawalK. Tittelbach‐HelmrichHarsh BhatiaValerio PascucciEhsan AbbasnejadMy V. T. Phan
- Topics
- Gaussian Processes and Bayesian Inference (6 papers)Advanced Vision and Imaging (6 papers)Machine Learning and Algorithms (5 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceMachine LearningComputer Graphics Forum
- Partner nations
- United StatesAustraliaJapan
In The Last Decade
Justin Domke
23 papers receiving 283 citations
Peers
Comparison fields: 5 of 63
- Computer Vision and Pattern Recognition 157
- Artificial Intelligence 128
- Aerospace Engineering 35
- Media Technology 27
- Computational Mechanics 25
Countries citing papers authored by Justin Domke
This map shows the geographic impact of Justin Domke'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 Justin Domke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Justin Domke more than expected).
Fields of papers citing papers by Justin Domke
This network shows the impact of papers produced by Justin Domke. 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 Justin Domke. The network helps show where Justin Domke may publish in the future.
Co-authorship network of co-authors of Justin Domke
This figure shows the co-authorship network connecting the top 25 collaborators of Justin Domke. A scholar is included among the top collaborators of Justin Domke 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 Justin Domke. Justin Domke is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Thompson Sampling and Approximate Inference | 4 |
| 2 | Sparse Covariance Modeling in High Dimensions with Gaussian Processes. | 1 |
| 3 | 8 | |
| 4 | 1 | |
| 5 | Reflection, refraction, and Hamiltonian Monte Carlo | 13 |
| 6 | 2 | |
| 7 | 0 | |
| 8 | Projecting Ising Model Parameters for Fast Mixing | 3 |
| 9 | 77 | |
| 10 | Generic Methods for Optimization-Based Modeling | 71 |
| 11 | 4 | |
| 12 | 2 | |
| 13 | 21 | |
| 14 | Implicit Differentiation by Perturbation | 12 |
| 15 | 4 | |
| 16 | 17 | |
| 17 | 4 | |
| 18 | 5 | |
| 19 | 23 | |
| 20 | 18 |
About Justin Domke
Justin Domke is a scholar working on Computer Vision and Pattern Recognition, Statistics and Probability and Artificial Intelligence, having authored 24 papers that have together received 306 indexed citations. Recurring topics across this work include Gaussian Processes and Bayesian Inference (6 papers), Advanced Vision and Imaging (6 papers) and Machine Learning and Algorithms (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (157 citations), Artificial Intelligence (128 citations) and Computational Mathematics (2 citations). Justin Domke has collaborated with scholars based in United States, Australia and Japan. Frequent co-authors include Yiannis Aloimonos, Daniel Sheldon, Abhinav Agrawal, K. Tittelbach‐Helmrich, Harsh Bhatia, Valerio Pascucci, Ehsan Abbasnejad, My V. T. Phan, Zoran Stamenković and Yarden Livnat. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Machine Learning and Computer Graphics Forum.
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.