Matthias Seeger
About
In The Last Decade
Matthias Seeger
60 papers receiving 5.3k citations
Hit Papers
Peers
Comparison fields: 5 of 175
- Artificial Intelligence 3.0k
- Control and Systems Engineering 1.1k
- Computer Vision and Pattern Recognition 1.1k
- Computational Theory and Mathematics 774
- Management Science and Operations Research 662
Countries citing papers authored by Matthias Seeger
This map shows the geographic impact of Matthias Seeger'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 Matthias Seeger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Seeger more than expected).
Fields of papers citing papers by Matthias Seeger
This network shows the impact of papers produced by Matthias Seeger. 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 Matthias Seeger. The network helps show where Matthias Seeger may publish in the future.
Co-authorship network of co-authors of Matthias Seeger
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Seeger. A scholar is included among the top collaborators of Matthias Seeger 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 Matthias Seeger. Matthias Seeger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | LEEP: A New Measure to Evaluate Transferability of Learned Representations | 8 |
| 2 | Deep State Space Models for Time Series Forecasting breakdown → | 247 |
| 3 | Scalable Hyperparameter Transfer Learning | 25 |
| 4 | Expectation Propagation for Rectified Linear Poisson Regression | 7 |
| 5 | Scalable Collaborative Bayesian Preference Learning | 4 |
| 6 | Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models | 12 |
| 7 | Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models | 24 |
| 8 | Gaussian Process Bandits without Regret: An Experimental Design Approach | 21 |
| 9 | 102 | |
| 10 | Learning Inverse Dynamics: A Comparison | 38 |
| 11 | 15 | |
| 12 | 151 | |
| 13 | Fast Gaussian Process Regression using KD-Trees | 61 |
| 14 | Worst-Case Bounds for Gaussian Process Models | 10 |
| 15 | Fast Forward Selection to Speed Up Sparse Gaussian Process Regression | 209 |
| 16 | Fast Sparse Gaussian Process Methods: The Informative Vector Machine | 318 |
| 17 | Covariance Kernels from Bayesian Generative Models | 46 |
| 18 | An Improved Predictive Accuracy Bound for Averaging Classifiers | 14 |
| 19 | The Effect of the Input Density Distribution on Kernel-based Classifiers | 95 |
| 20 | Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers | 69 |
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.