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Abstract: . Various proposals have recently been made which cast cortical processing in terms of hierarchical statistical generative models (Mumford, 1994; Kawato, 1993; Hinton & Zemel, 1994; Zemel, 1994; Hinton et al , 1995; Dayan et al , 1995; Olshausen & Field, 1996; Rao & Ballard, 1995). In the case of vision, these claim that top-down connections in the cortical hierarchy capture essential aspects of how the activities of neurons in primary sensory areas are generated by the contents of... (Update)
Context of citations to this paper: More
...results. 32 models it is impossible (even in theory) to know the precise values of s, so one must be content with a probability density p(s x) [29]. By Bayes rule, this is given as p(s x) p(x s)p(s) p(x) 6.2) To obtain a point estimate of the hidden variables, many models...
...solved analytically. In particular, such methods are one of the main options for performing approximate inference in Bayesian networks [11]. With that in mind, it is perhaps even a bit surprising that Monte Carlo sampling has not, to our knowledge, previously been suggested as...
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BibTeX entry: (Update)
Dayan, P (1997). Recognition in hierarchical models. In F Cucker & M Shub, editors, Foundations of Computational Mathematics. Berlin, Germany: Springer. http://citeseer.ist.psu.edu/dayan97recognition.html More
@misc{ dayan97recognition,
author = "P. Dayan",
title = "Recognition in hierarchical models",
text = "Dayan, P (1997). Recognition in hierarchical models. In F Cucker & M Shub,
editors, Foundations of Computational Mathematics. Berlin, Germany: Springer.",
year = "1997",
url = "citeseer.ist.psu.edu/dayan97recognition.html" }
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