Abstract Extremal graphical models are sparse statistical for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation of the complex extremal dependence structure. For important case tree models, we develop data-driven methodology learning We show that sample versions correlation new summary statistic, which call variogram, can be...

A framework for learning the structure of textures is introduced in this work. A Bayesian network, whose structure and parameters are learned from a small texture sample, is used to characterize the high-dimensional probability density function (PDF) that models the given texture. The underlying network learning framework is based on the combination of tools such as kernel density estimation an...

Human learning is highly efficient and flexible. A key contributor to this learning flexibility is our ability to generalize new information across contexts that we know require the same behavior and to transfer rules to new contexts we encounter. To do this, we structure the information we learn and represent it hierarchically as abstract, context-dependent rules that constrain lower-level sti...

Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is superexponentially large. As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and tree-augmented Bayesian networks, which involves no search at all. There is substantial evidence in the literature that the perf...

Hierarchical and recursive structure is commonly found in inputs from the richest sensory modalities, including natural language sentences and scene images. But such hierarchical structure has traditionally been a strong point of both structured and supervised models (whether symbolic of probabilistic) and a weak point of both neural networks and unsupervised learning. I will present some of ou...

We present an approach for learning stochastic geometric models of object categories from single view images. We focus here on models expressible as a spatially contiguous assemblage of blocks. Model topologies are learned across groups of images, and one or more such topologies is linked to an object category (e.g. chairs). Fitting learned topologies to an image can be used to identify the obj...

On-line learning and adaptation capabilities are important for robot systems which operate in the real world and have to react to changes in the environment and the task requirements. Such learning schemes, however, often suffer from problems of complexity, rendering them intractable for on-line learning in complex domains. To address these problems, the approach presented here introduces prior...

‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...