Structure learning in random fields for heart motion abnormality detection

By Mark W. Schmidt, Kevin P. Murphy, glenn. fung, Rómer Rosales. 2008

145 citations recorded by the source corpus.

Subfields: Computational Mechanics, Ocean Engineering, Statistics and Probability

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In Computational Mechanics

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Citations in the corpus

Learning generative models for protein fold families (2011, methodology): “Instead of approximating the true-likelihood using approximate inference techniques, we use a different approximation based on a pseudo-likelihood proposed by [16], and used in [17, 18].”
Beyond spatial pyramids: Receptive field learning for pooled image features (2012, methodology): “To this end, we adopt the idea of structured sparsity [26, 29], and train a multiple-class linear classifier y = f(x) = Wx+b via the following optimization problem:”
Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials (2010, methodology): “This has been shown to give good performance in a variety of related applications (Figueiredo et al., 2007; van den Berg and Friedlander, 2008; Schmidt et al., 2008).”
Feature Selection Based on Structured Sparsity: A Comprehensive Study (2017, background): “Other applications of l∞,1 regularization are simultaneous sparse signal approximation [74], [75], structure learning [77], multiview learning [78], and multitask learning [79].”
Learning Semantic Image Representations at a Large Scale (2014, methodology): “To this end, we adopt the idea of structured sparsity [88, 99], and train a multiple-class linear classifier y = f(x) = Wx + b via the following optimization problem:”
Modeling Discrete Interventional Data using Directed Cyclic Graphical Models (2009, methodology): “Similar to Schmidt et al. (2008), we can convert the continuous and unconstrained but non-differentiable problem (1) into a differentiable problem with secondorder cone constraints”
Multi-Task Learning of Gaussian Graphical Models (2010, background): “, 2007); Conditional random fields by pseudo-likelihood and block regularization in order to penalize all parameters of an edge simultaneously (Schmidt et al., 2008); and Ising models, i.”
Sparse and Locally Constant Gaussian Graphical Models (2009, methodology): “Structure learning through `1-regularization has been also proposed for different types of graphical models: Markov random fields (MRFs) by a clique selection heuristic and approximate inference [15]; Bayesian networks on binary variables by logistic regression [16]; Conditional random fields by pse”
Finding and Leveraging Structure in Learning Problems (2012, methodology): “[Schmidt et al., 2008] apply block-regularized structure learning to the problem of detecting abnormalities in heart motion.”
Spatial Pattern Templates for Recognition of Objects with Regular Structure (2013, methodology): “by block-wise regularization for sparsity [12] (16 segments) and subsequently also for higher-order potentials with a hierarchical constraint [13] (30 segments).”
Quaddirectional 2D-Recurrent Neural Networks For Image Labeling (2015, methodology): “Loopy Belief Propagation [34] is employed to achieve this goal and the implementation of [35] is used.”
Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm (2009, unknown): “If the variables are non-binary, there are multiple parameters per edge, and so one needs to use a group (cid:96) 1 penalty to learn sparse graphs (Schmidt et al., 2008).”
A Complete Analysis of the l_1, p Group-Lasso (2012, methodology): “Algorithms for the 1 , ∞ -variant of the Group-Lasso were studied in (Turlach et al., 2005; Schmidt & Murphy, 2008; Quattoni et al., 2009) and (Vogt & Roth, 2010).”