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TNN
2008
177views more  TNN 2008»
13 years 8 months ago
Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model
Previous work on statistical language modeling has shown that it is possible to train a feed-forward neural network to approximate probabilities over sequences of words, resulting...
Yoshua Bengio, Jean-Sébastien Senecal
CGF
2005
167views more  CGF 2005»
13 years 8 months ago
Adaptive Deformable Models for Graphics and Vision
Deformable models are a powerful tool in both computer graphics and computer vision. The description and implementation of the deformations have to be simultaneously flexible and ...
Siome Goldenstein, Christian Vogler, Luiz Velho
VC
2008
93views more  VC 2008»
13 years 8 months ago
Efficient product sampling using hierarchical thresholding
Abstract We present an efficient method for importance sampling the product of multiple functions. Our algorithm computes a quick approximation of the product on-the-fly, based on ...
Fabrice Rousselle, Petrik Clarberg, Luc Leblanc, V...
ASC
2004
13 years 8 months ago
Neural network-based colonoscopic diagnosis using on-line learning and differential evolution
In this paper, on-line training of neural networks is investigated in the context of computer-assisted colonoscopic diagnosis. A memory-based adaptation of the learning rate for t...
George D. Magoulas, Vassilis P. Plagianakos, Micha...
IVC
2000
186views more  IVC 2000»
13 years 8 months ago
Uncertainty analysis of 3D reconstruction from uncalibrated views
We consider reconstruction algorithms using points tracked over a sequence of (at least three) images, to estimate the positions of the cameras (motion parameters), the 3D coordin...
Etienne Grossmann, José Santos-Victor