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Accelerated training of conditional random fields with stochastic gradient methods
Abstract
We apply Stochastic Meta-Descent (SMD), a stochastic gradient optimization method with gain vector adaptation, to the training of Conditional Random Fields (CRFs). On several large data sets, the resulting optimizer converges to the same quality of solution over an order of magnitude faster than limited-memory BFGS, the leading method reported to date. We report results for both exact and inexact inference techniques.
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Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
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Learning Conditional Random Fields for Stereo
Scharstein, D., & Pal, C. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383191 · Cited by 851
Sarawagi, S. (2008) · Foundations and Trends in Databases · doi:10.1561/1900000003 · Cited by 707
Contextual classification of lidar data and building object detection in urban areas
Niemeyer, J., Rottensteiner, F., & Soergel, U. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.11.001 · Cited by 577
Sutton, C., Rohanimanesh, K., & McCallum, A. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015422 · Cited by 284
Metadata
- Authors
- Vishwanathan, S. V. N., Schraudolph, N. N., Schmidt, M. W., & Murphy, K. P.
- Venue
- Proceedings of the 23rd international conference on Machine learning - ICML '06
- Publisher
- ACM Press
- Published
- 2006
- Pages
- 969-976
- OpenAlex
- W2109189215
- Cited by
- 278
- References
- 1
- FWCI
- 17.53
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Vishwanathan, S. V. N., Schraudolph, N. N., Schmidt, M. W., & Murphy, K. P. (2006). Accelerated training of conditional random fields with stochastic gradient methods. Proceedings of the 23rd international conference on Machine learning - ICML '06. https://doi.org/10.1145/1143844.1143966
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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.
