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105
Voted
NIPS
1997
15 years 1 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung
107
Voted
CVPR
2003
IEEE
16 years 2 months ago
Learning Bayesian Network Classifiers for Facial Expression Recognition using both Labeled and Unlabeled Data
Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through...
Ira Cohen, Nicu Sebe, Fabio Gagliardi Cozman, Marc...
MM
2004
ACM
167views Multimedia» more  MM 2004»
15 years 6 months ago
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
Xiaofei He, Wei-Ying Ma, HongJiang Zhang
113
Voted
SIGUCCS
2005
ACM
15 years 6 months ago
Developing a synchronous web seminar application for online learning
Many higher education institutions are searching for cost effective tools for the delivery of a feature rich, synchronous online learning environment. While there are several comm...
Michael D. Ciocco, Neil Toporski, Michael Dorris
107
Voted
NIPS
1998
15 years 1 months ago
Learning Nonlinear Dynamical Systems Using an EM Algorithm
The Expectation Maximization EM algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables 2 . It has been app...
Zoubin Ghahramani, Sam T. Roweis