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A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

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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 in significant error reduction when compared to standard baseline models. Ho ...

IDIAP2003

Classification using localized mixtures of experts

A mixture of experts consists of a gating network that learns to partition the input space and of experts networks attributed to these different regions. This paper focuses on the choice of the gating network. First, a localized gating network based on a m ...

London: IEE1999

Consistent identification of NARX models via regularization networks

Giancarlo Ferrari Trecate

Generalization networks are nonparametric estimators obtained from the application of Tychonov regularization or Bayes estimation to the hypersurface reconstruction problem. Under symmetry assumptions they are a particular type of radial basis function neu ...

1999

Capacity of two-layer feedforward neural networks with binary weights

Demetri Psaltis

The low er and upper bounds for the information capacity of two-layer feedforward neural networks with binary interconnections, integer thresholds for the hidden units, and zero threshold for the output unit is obtained through two steps, First, through a ...

1998

Identification of NARX models using regularization networks: A consistency result

Giancarlo Ferrari Trecate

1998

Robustness and convergence of adaptive schemes in blind equalization and neural network training

Ali H. Sayed

We pursue a time-domain feedback analysis of adaptive schemes with nonlinear update relations. We consider commonly used algorithms in blind equalization and neural network training and study their performance in a purely deterministic framework. The deriv ...

1996