CORR
2010
Springer
13 years 5 months ago
2010
Springer
In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
AMC
2007
13 years 5 months ago
2007
This paper presents a new dynamic generating graphical model for point-sets matching. The existing algorithms on graphical models proved to be quite robust to noise but are suscep...
AMAI
2008
Springer
13 years 5 months ago
2008
Springer
Abstract The paper introduces mixed networks, a new graphical model framework for expressing and reasoning with probabilistic and deterministic information. The motivation to devel...
NIPS
2003
13 years 6 months ago
2003
Consider a number of moving points, where each point is attached to a joint of the human body and projected onto an image plane. Johannson showed that humans can effortlessly det...
AAAI
2006
13 years 6 months ago
2006
AND/OR search spaces have recently been introduced as a unifying paradigm for advanced algorithmic schemes for graphical models. The main virtue of this representation is its sens...
UAI
2008
13 years 6 months ago
2008
We introduce a new type of graphical model called a `cumulative distribution network' (CDN), which expresses a joint cumulative distribution as a product of local functions. ...
UAI
2008
13 years 6 months ago
2008
The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabi...
NIPS
2007
13 years 6 months ago
2007
We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to max-product but unlike max-product it ...
NIPS
2007
13 years 6 months ago
2007
The expectation maximization (EM) algorithm is a widely used maximum likelihood estimation procedure for statistical models when the values of some of the variables in the model a...
FLAIRS
2007
13 years 7 months ago
2007
Discovery of graphical models is NP-hard in general, which justifies using heuristics. We consider four commonly used heuristics. We summarize the underlying assumptions and anal...