Training Products of Experts by Maximizing Contrastive Likelihood

@misc{citeulike:1402724, abstract = {It is possible to combine multiple probabilistic models of the same data by multiplying the probabilities together and then renormalizing. This is a very ecient way to model highdimensional data which simultaneously satises many dierent low-dimensional constraints because each individual expert model can focus on giving high probability to data vectors that satisfy just one of the constraints. Data vectors that satisfy this one constraint but violate other constraints will be ruled out...}, author = {Hinton, G. }, citeulike-article-id = {1402724}, keywords = {probabilistic-models, product-of-experts}, posted-at = {2007-06-21 17:14:12}, priority = {2}, title = {Training Products of Experts by Maximizing Contrastive Likelihood}, url = {http://citeseer.ist.psu.edu/hinton99training.html}, year = {1999} }

TY - GEN ID - citeulike:1402724 L3 - citeulike-article-id:1402724 TI - Training Products of Experts by Maximizing Contrastive Likelihood N2 - It is possible to combine multiple probabilistic models of the same data by multiplying the probabilities together and then renormalizing. This is a very ecient way to model highdimensional data which simultaneously satises many dierent low-dimensional constraints because each individual expert model can focus on giving high probability to data vectors that satisfy just one of the constraints. Data vectors that satisfy this one constraint but violate other constraints will be ruled out... KW - probabilistic-models KW - product-of-experts AU - Hinton, G PY - 1999/// UR - http://citeseer.ist.psu.edu/hinton99training.html ER -