The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models
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Citations
Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Thrun, S. (2002) · Communications of the ACM · doi:10.1145/504729.504754 · Cited by 7,994
Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software
Bar‐Shalom, Y., Li, X. ‐., & Kirubarajan, T. (2002) · doi:10.1002/0471221279 · Cited by 6,782
New extension of the Kalman filter to nonlinear systems
Julier, S. J., & Uhlmann, J. K. (1997) · SPIE Proceedings · doi:10.1117/12.280797 · Cited by 5,331
Aircraft Control and Simulation
Stevens, B., & Lewis, F. L. (2004) · Aircraft Engineering and Aerospace Technology · doi:10.1108/aeat.2004.12776eae.001 · Cited by 3,167
(1999) · doi:10.1162/089976600300015565 · Cited by 2,799
A Unifying View of Sparse Approximate Gaussian Process Regression
Quiñonero-Candela, J., & Rasmussen, C. E. (2005) · doi:10.5555/1046920.1194909 · Cited by 1,671
Advances in Neural Information Processing Systems 19 (NIPS 2006)
Simonotto, E., Whalley, H. C., Lawrie, S. M., Murray, L. M., McGonigle, D. J., & Storkey, A. (2006) · Cited by 1,651
Sparse Gaussian Processes using Pseudo-inputs
Snelson, E., & Ghahramani, Z. (2005) · Cited by 1,415
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
Murphy, K., & Russell, S. (2001) · Sequential Monte Carlo Methods in Practice · doi:10.1007/978-1-4757-3437-9_24 · Cited by 1,157
Cited by
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking
Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115
(2014) · International Conference on Learning Representations · Cited by 634
Model learning for robot control: A survey
Nguyen-Tuong, D., & Peters, J. (2011) · Cognitive Processing · doi:10.1007/s10339-011-0404-1 · Cited by 503
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning
Nagabandi, A., Clavera, I., Liu, S., Fearing, R. S., Abbeel, P., Levine, S., & Finn, C. (2018) · arXiv · doi:10.48550/arxiv.1803.11347 · Cited by 370
Sartori, M., Reggiani, M., Farina, D., & Lloyd, D. G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052618 · Cited by 330
Metadata
- Authors
- Ko, J., & Fox, D.
- Venue
- Autonomous Robots
- Published
- 2009-05-15
- Volume
- 27
- Issue
- 1
- Pages
- 75-90
- ISSN
- 0929-5593 (ISSN-L)
- 1573-7527
- OpenAlex
- W2007692097
- Cited by
- 335
- References
- 38
- FWCI
- 15.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ko, J., & Fox, D. (2009). GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models. Autonomous Robots. https://doi.org/10.1007/s10514-009-9119-x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
