Graphical Models

Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers NeurIPS 2014 Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation NeurIPS 2014 Just-In-Time Learning for Fast and Flexible Inference NeurIPS 2014 Learning Chordal Markov Networks by Dynamic Programming NeurIPS 2014 Learning convolution filters for inverse covariance estimation of neural network connectivity NeurIPS 2014 Learning Latent Variable Gaussian Graphical Models ICML 2014 Learning Sum-Product Networks with Direct and Indirect Variable Interactions ICML 2014 Linear and Parallel Learning of Markov Random Fields ICML 2014 Making Pairwise Binary Graphical Models Attractive NeurIPS 2014 Message Passing Inference for Large Scale Graphical Models with High Order Potentials NeurIPS 2014 Min-Max Problems on Factor Graphs ICML 2014 Mode Estimation for High Dimensional Discrete Tree Graphical Models NeurIPS 2014 Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks NeurIPS 2014 Multiple Testing under Dependence via Semiparametric Graphical Models ICML 2014 Multi-scale Graphical Models for Spatio-Temporal Processes NeurIPS 2014 New Rules for Domain Independent Lifted MAP Inference NeurIPS 2014 Non-Asymptotic Analysis of Relational Learning with One Network AISTATS 2014 Object-based Multiple Foreground Video Co-segmentation CVPR 2014 On Sparse Gaussian Chain Graph Models NeurIPS 2014 Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection NeurIPS 2014 Partial Optimality by Pruning for MAP-inference with General Graphical Models CVPR 2014 Projecting Markov Random Field Parameters for Fast Mixing NeurIPS 2014 Restricted Boltzmann machines modeling human choice NeurIPS 2014 Rounding-based Moves for Metric Labeling NeurIPS 2014 Scalable Inference for Neuronal Connectivity from Calcium Imaging NeurIPS 2014