Neural networks: further insights into error function, generalized weights and others

Some Insights into the Geometry and Training of Neural Networks

Neural networks have been successfully used for classification tasks in a rapidly growing number of practical applications. Despite their popularity and widespread use, there are still many aspects of training and classification that are not well understood. In this paper we aim to provide some new insights into training and classification by analyzing neural networks from a feature-space persp...

Insights into deep neural networks for speaker recognition

Traditional i-vector speaker recognition systems use a Gaussian mixture model (GMM) to collect sufficient statistics. Recently, replacing this GMM with a deep neural network (DNN) has shown promising results. In this paper, we study a number of open issues that relate to performance, computational complexity, and applicability of DNNs as part of the full speaker recognition pipeline. The experi...

Cross Entropy Error Function in Neural Networks: Forecasting Gasoline Demand

This paper applies artificial neural networks to forecast gasoline consumption. The ANN is implemented using the cross entropy error function in the training stage. The cross entropy function is proven to accelerate the backpropagation algorithm and to provide good overall network performance with relatively short stagnation periods. To forecast gasoline consumption (GC), the ANN uses previous ...

Feedforward neural networks with constrained weights

Some neural networks compute, others don't

I address whether neural networks perform computations in the sense of computability theory and computer science. I explicate and defend the following theses. (1) Many neural networks compute--they perform computations. (2) Some neural networks compute in a classical way. Ordinary digital computers, which are very large networks of logic gates, belong in this class of neural networks. (3) Other...