Erik B. Sudderth
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Signal Processing top 2%
- Aerospace Engineering top 5%
- Control and Systems Engineering top 5%
- Co-authors
- Alan S. WillskyWilliam T. FreemanMichael I. JordanEmily B. FoxAntonio TorralbaAlexander IhlerDeqing SunZhile Ren
- Topics
- Bayesian Methods and Mixture Models (30 papers)Gaussian Processes and Bayesian Inference (19 papers)Bayesian Modeling and Causal Inference (10 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceCommunications of the ACMIEEE Transactions on Signal Processing
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
Erik B. Sudderth
73 papers receiving 2.7k citations
Peers
Comparison fields: 5 of 134
- Computer Vision and Pattern Recognition 1.4k
- Artificial Intelligence 1.3k
- Signal Processing 327
- Aerospace Engineering 268
- Control and Systems Engineering 239
Countries citing papers authored by Erik B. Sudderth
This map shows the geographic impact of Erik B. Sudderth'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 Erik B. Sudderth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erik B. Sudderth more than expected).
Fields of papers citing papers by Erik B. Sudderth
This network shows the impact of papers produced by Erik B. Sudderth. 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 Erik B. Sudderth. The network helps show where Erik B. Sudderth may publish in the future.
Co-authorship network of co-authors of Erik B. Sudderth
This figure shows the co-authorship network connecting the top 25 collaborators of Erik B. Sudderth. A scholar is included among the top collaborators of Erik B. Sudderth 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 Erik B. Sudderth. Erik B. Sudderth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints | 2 |
| 2 | Variational Training for Large-Scale Noisy-OR Bayesian Networks. | 1 |
| 3 | 9 | |
| 4 | Semi-Supervised Prediction-Constrained Topic Models | 6 |
| 5 | Refinery: an open source topic modeling web platform | 1 |
| 6 | From Patches to Images: A Nonparametric Generative Model. | 2 |
| 7 | Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process | 12 |
| 8 | Scalable adaptation of state complexity for nonparametric hidden Markov models | 11 |
| 9 | Nonparametric clustering with distance dependent hierarchies | 7 |
| 10 | Preserving Modes and Messages via Diverse Particle Selection | 12 |
| 11 | Memoized Online Variational Inference for Dirichlet Process Mixture Models | 44 |
| 12 | Efficient Online Inference for Bayesian Nonparametric Relational Models | 7 |
| 13 | Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data | 22 |
| 14 | Spatial distance dependent Chinese restaurant processes for image segmentation | 40 |
| 15 | Global seismic monitoring as probabilistic inference | 7 |
| 16 | Layered image motion with explicit occlusions, temporal consistency, and depth ordering | 68 |
| 17 | Vertically Integrated Seismological Analysis I : Modeling | 1 |
| 18 | Nonparametric Bayesian Learning of Switching Linear Dynamical Systems | 98 |
| 19 | Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes | 97 |
| 20 | Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation | 60 |
About Erik B. Sudderth
Erik B. Sudderth is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 75 papers that have together received 2.9k indexed citations. Recurring topics across this work include Bayesian Methods and Mixture Models (30 papers), Gaussian Processes and Bayesian Inference (19 papers) and Bayesian Modeling and Causal Inference (10 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Artificial Intelligence (1.3k citations) and Signal Processing (327 citations). Erik B. Sudderth has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Alan S. Willsky, William T. Freeman, Michael I. Jordan, Emily B. Fox, Antonio Torralba, Alexander Ihler, Deqing Sun, Zhile Ren, Michael C. Hughes and Michael J. Black. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Communications of the ACM and IEEE Transactions on Signal Processing.
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.