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JMLR
2010
202views more  JMLR 2010»
15 years 1 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
CSSE
2008
IEEE
16 years 1 months ago
Application of New Adaptive Higher Order Neural Networks in Data Mining
This paper introduces an adaptive Higher Order Neural Network (HONN) model and applies it in data mining such as simulating and forecasting government taxation revenues. The propo...
Shuxiang Xu, Ling Chen
COLING
1996
15 years 8 months ago
HMM-Based Word Alignment in Statistical Translation
In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the model is to make the alignment probabilities...
Stephan Vogel, Hermann Ney, Christoph Tillmann
BC
2007
98views more  BC 2007»
15 years 7 months ago
Extending the mirror neuron system model, I
The paper introduces mirror neuron system II (MNS2), a new version of the MNS model (Oztop and Arbib in Biol Cybern 87(2):116–140, 2002) of action recognition learning by mirror ...
James Bonaiuto, Edina Rosta, Michael A. Arbib
ISMIR
2003
Springer
116views Music» more  ISMIR 2003»
16 years 8 days ago
Harmonic analysis with probabilistic graphical models
A technique for harmonic analysis is presented that partitions a piece of music into contiguous regions and labels each with the key, mode, and functional chord, e.g. tonic, domin...
Christopher Raphael, Josh Stoddard