Videos tagged with Graphical Models

In the "structural" paradigm for visual pattern recognition, or what some call "strong" pattern recognition, one is not satisfied with simply assigning a class label to an input object, but instead we aim at finding exactly which parts of the template object correspond to which parts of the scene. This is a much harder problem in principle, because it is inherently combinatorial on the number o...

We introduce a new framework for feature grouping based on factor graphs, which are graphical models that encode interactions among arbitrary numbers of random variables. The ability of factor graphs to express interactions higher than pairwise order (the highest order encountered in most graphical models used in computer vision) is useful for modeling a variety of pattern recognition problems....

Lecture slides: Learning CRFs with Hierarchical Features: An Application to Go The Game of Go Territory Prediction Talk Outline Hierarchical Patterns Models Independent Pattern-based Classifiers Inference and Training Bayesian Model Averaging Hierarchical Tree Models CRF & Pattern CRF Inference and Training Pseudolikelihood Local Training Evaluation Models & Algorithms Training Time Inf...

Lecture slides: Probabilistic Graphical Models Building Intelligent Computers Uncertainty and Artificial Intelligence (UAI) Applications of Probabilistic Learning Canonical Tasks Representation Using random variables to represent the world Structure of Learning Machines Loss Functions for Tuning Parameters Training vs. Testing Sampling Assumption Generalization and Overfitting Capacity: Complex...

Recent investigations indicate the use of a probabilistic ”learning” perspective of tasks defined on a single graph, as opposed to the traditional algorithmical ”computational” point of view. This note discusses the use of Rademacher complexities in this setting, and illustrates the use of Kruskal’s algorithm for transductive inference based on a nearest neighbor r...

Belief Propagation (BP) has become a popular method for inference on graphical models. Accurate approximations for intractable quantities (e.g. single-node marginals) can be obtained within rather modest computation times. However, for large interaction strengths (i.e. potentials that are highly dependent on their arguments) or densely connected graphs, BP can fail to converge. This can be reme...

The max-product algorithm can be used to obtain approximate MAP-assignments of the probability distribution defined by a graphical model. On tree-structured graphical models the MAP-assignment is exact and the max-product algorithm is equivalent to the Viterbi-algorithm. On general models one may run into the following problems: The algorithm does not converge; The single node marginals are not...

The problem of estimating a sparse signal embedded in noise arises in various contexts, including signal denoising and approximation, as well as graphical model selection. The natural optimization-theoretic formulation of such problems involves "norm" constraints (i.e., penalties on the number of non-zero coefficients), which leads to NP-hard problems in general. A natural approach is...

Lecture slides: Learning Causal Graphical Models with Latent Variables Introduction Problem Overview pt 3 Bayesian Networks (BN) Causal Bayesian networks (CBN) Modeling Latent Variables Probabilistic vs Causal Inference With latent variables Overview pt 4 Our assumptions Representation for causal inference Modeling Latent Variables 1 Representation for causal inference 1 Inference in SMCMs Repr...

Lecture slides: Protein Quaternary Fold Recognition Using Conditional Graphical Models Snapshot of Cell Biology Example Protein Structures Predicting Protein Structures Quaternary Folds and Alignments Related Work Conditional Random Fields Our Solution: Conditional Graphical Models Linked Segmentation CRF Linked Segmentation CRF (II) Approximate Inference - Learning Approximate Inference - Infe...