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GECCO
2004
Springer
134views Optimization» more  GECCO 2004»
15 years 3 months ago
A Descriptive Encoding Language for Evolving Modular Neural Networks
Evolutionary algorithms are a promising approach for the automated design of artificial neural networks, but they require a compact and efficient genetic encoding scheme to repres...
Jae-Yoon Jung, James A. Reggia
JMLR
2012
13 years 15 days ago
Deep Boltzmann Machines as Feed-Forward Hierarchies
The deep Boltzmann machine is a powerful model that extracts the hierarchical structure of observed data. While inference is typically slow due to its undirected nature, we argue ...
Grégoire Montavon, Mikio L. Braun, Klaus-Ro...
TNN
2008
177views more  TNN 2008»
14 years 10 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
ICASSP
2009
IEEE
15 years 1 months ago
Neural network based language models for highly inflective languages
Speech recognition of inflectional and morphologically rich languages like Czech is currently quite a challenging task, because simple n-gram techniques are unable to capture impo...
Tomas Mikolov, Jirí Kopecký, Lukas B...