conference paper
Computing the M most probable modes of a graphical model
JMLR: W&CP
published
yes
Chao
Chen
author 3E92416E-F248-11E8-B48F-1D18A9856A87
Vladimir
Kolmogorov
author 3D50B0BA-F248-11E8-B48F-1D18A9856A87
Zhu
Yan
author
Dimitris
Metaxas
author
Christoph
Lampert
author 40C20FD2-F248-11E8-B48F-1D18A9856A870000-0001-8622-7887
HeEd
department
VlKo
department
ChLa
department
AISTATS: Conference on Uncertainty in Artificial Intelligence
We introduce the M-modes problem for graphical models: predicting the M label configurations of highest probability that are at the same time local maxima of the probability landscape. M-modes have multiple possible applications: because they are intrinsically diverse, they provide a principled alternative to non-maximum suppression techniques for structured prediction, they can act as codebook vectors for quantizing the configuration space, or they can form component centers for mixture model approximation. We present two algorithms for solving the M-modes problem. The first algorithm solves the problem in polynomial time when the underlying graphical model is a simple chain. The second algorithm solves the problem for junction chains. In synthetic and real dataset, we demonstrate how M-modes can improve the performance of prediction. We also use the generated modes as a tool to understand the topography of the probability distribution of configurations, for example with relation to the training set size and amount of noise in the data.
JMLR2013Scottsdale, AZ, United States
eng
31161 - 169
Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. 2013. Computing the M most probable modes of a graphical model. AISTATS: Conference on Uncertainty in Artificial Intelligence, JMLR: W&CP, vol. 31, 161–169.
Chen, Chao, et al. <i>Computing the M Most Probable Modes of a Graphical Model</i>. Vol. 31, JMLR, 2013, pp. 161–69.
Chen C, Kolmogorov V, Yan Z, Metaxas D, Lampert C. Computing the M most probable modes of a graphical model. In: Vol 31. JMLR; 2013:161-169.
Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., & Lampert, C. (2013). Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169). Presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.
C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, C. Lampert, in:, JMLR, 2013, pp. 161–169.
C. Chen, V. Kolmogorov, Z. Yan, D. Metaxas, and C. Lampert, “Computing the M most probable modes of a graphical model,” presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States, 2013, vol. 31, pp. 161–169.
Chen, Chao, Vladimir Kolmogorov, Zhu Yan, Dimitris Metaxas, and Christoph Lampert. “Computing the M Most Probable Modes of a Graphical Model,” 31:161–69. JMLR, 2013.
29012018-12-11T12:00:14Z2021-01-12T07:00:35Z