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CONIELECOMP
2006
IEEE
14 years 1 months ago
Chaotic Time Series Approximation Using Iterative Wavelet-Networks
This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theo...
E. S. Garcia-Trevino, Vicente Alarcón Aquin...
JMLR
2010
105views more  JMLR 2010»
13 years 2 months ago
On the Convergence Properties of Contrastive Divergence
Contrastive Divergence (CD) is a popular method for estimating the parameters of Markov Random Fields (MRFs) by rapidly approximating an intractable term in the gradient of the lo...
Ilya Sutskever, Tijmen Tieleman
CVPR
2009
IEEE
15 years 2 months ago
Global Connectivity Potentials for Random Field Models
Markov random field (MRF, CRF) models are popular in computer vision. However, in order to be computationally tractable they are limited to incorporate only local interactions a...
Sebastian Nowozin, Christoph H. Lampert
LCN
2005
IEEE
14 years 1 months ago
Approximate Coverage in Wireless Sensor Networks
— Recent advances in microelectronic technology have made it possible to construct compact and inexpensive wireless sensors. Sensor networks have received significant attention ...
Yuzhen Liu, Weifa Liang
JMLR
2006
138views more  JMLR 2006»
13 years 7 months ago
Noisy-OR Component Analysis and its Application to Link Analysis
We develop a new component analysis framework, the Noisy-Or Component Analyzer (NOCA), that targets high-dimensional binary data. NOCA is a probabilistic latent variable model tha...
Tomás Singliar, Milos Hauskrecht