Improving Optimization-Based Approximate Inference by Clamping Variables
暂无分享,去创建一个
Andreas Krause | Sebastian Tschiatschek | Josip Djolonga | Junyao Zhao | Andreas Krause | Josip Djolonga | Sebastian Tschiatschek | Junyao Zhao
[1] Olga Veksler,et al. Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[2] Francis R. Bach,et al. Learning with Submodular Functions: A Convex Optimization Perspective , 2011, Found. Trends Mach. Learn..
[3] George Papandreou,et al. Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models , 2011, 2011 International Conference on Computer Vision.
[4] Suvrit Sra,et al. Reflection methods for user-friendly submodular optimization , 2013, NIPS.
[5] Adrian Weller,et al. Clamping Improves TRW and Mean Field Approximations , 2016, AISTATS.
[6] Pushmeet Kohli,et al. Robust Higher Order Potentials for Enforcing Label Consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[7] Tommi S. Jaakkola,et al. On the Partition Function and Random Maximum A-Posteriori Perturbations , 2012, ICML.
[8] Martin Grötschel,et al. The ellipsoid method and its consequences in combinatorial optimization , 1981, Comb..
[9] Andreas Krause,et al. From MAP to Marginals: Variational Inference in Bayesian Submodular Models , 2014, NIPS.
[10] Brendan J. Frey,et al. Fast Exact Inference for Recursive Cardinality Models , 2012, UAI.
[11] Andreas Krause,et al. Scalable Variational Inference in Log-supermodular Models , 2015, ICML.
[12] Zoubin Ghahramani,et al. Lost Relatives of the Gumbel Trick , 2017, ICML.
[13] Andreas Krause,et al. Higher-Order Inference for Multi-class Log-Supermodular Models , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[14] Peter Harremoës,et al. Rényi Divergence and Kullback-Leibler Divergence , 2012, IEEE Transactions on Information Theory.
[15] VekslerOlga,et al. Fast Approximate Energy Minimization via Graph Cuts , 2001 .
[16] Francis R. Bach,et al. Parameter Learning for Log-supermodular Distributions , 2016, NIPS.
[17] Leslie Ann Goldberg,et al. The Complexity of Ferromagnetic Ising with Local Fields , 2006, Combinatorics, Probability and Computing.
[18] Adrian Weller,et al. Clamping Variables and Approximate Inference , 2014, NIPS.
[19] Rishabh K. Iyer,et al. Polyhedral aspects of Submodularity, Convexity and Concavity , 2015, ArXiv.
[20] 藤重 悟. Submodular functions and optimization , 1991 .
[21] Thomas P. Minka,et al. Divergence measures and message passing , 2005 .
[22] S. Karlin,et al. Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions , 1980 .
[23] Vladimir Kolmogorov,et al. An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision , 2001, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[24] Vibhav Vineet,et al. Filter-Based Mean-Field Inference for Random Fields with Higher-Order Terms and Product Label-Spaces , 2012, International Journal of Computer Vision.
[25] Andreas Krause,et al. Efficient Minimization of Decomposable Submodular Functions , 2010, NIPS.
[26] Mark Jerrum,et al. Polynomial-Time Approximation Algorithms for the Ising Model , 1990, SIAM J. Comput..
[27] Michael I. Jordan,et al. Graphical Models, Exponential Families, and Variational Inference , 2008, Found. Trends Mach. Learn..
[28] Mihalis Yannakakis,et al. The Complexity of Multiterminal Cuts , 1994, SIAM J. Comput..
[29] Pushmeet Kohli,et al. Measuring uncertainty in graph cut solutions , 2008, Comput. Vis. Image Underst..