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The Interchangeability of Learning Rate and Gain in Backpropagation Neural Networks

Publications associées (56)

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Recurrent Convolutional Neural Networks for Scene Labeling

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

The goal of the scene labeling task is to assign a class label to each pixel in an image. To ensure a good visual coherence and a high class accu- racy, it is essential for a model to capture long range (pixel) label dependencies in images. In a feed-forwa ...

2014

Analysis of MLP Based Hierarchical Phoneme Posterior Probability Estimator

Hervé Bourlard, Hynek Hermansky, Joel Praveen Pinto

We analyze a simple hierarchical architecture consisting of two multilayer perceptron (MLP) classifiers in tandem to estimate the phonetic class conditional probabilities. In this hierarchical setup, the first MLP classifier is trained using standard acous ...

2010

Hierarchical Tandem Features for ASR in Mandarin

Hervé Bourlard, Joel Praveen Pinto

We apply multilayer perceptron (MLP) based hierarchical Tandem features to large vocabulary continuous speech recognition in Mandarin. Hierarchical Tandem features are estimated using a cascade of two MLP classifiers which are trained independently. The fi ...

Idiap2010

Application of Artificial Neural Network to Predict Survival Time for Patients with Bladder Cancer

Rafal Tomasz Dlugosz

This paper presents an application of an artificial neural network to determine survival time of patients with a bladder cancer. Different learning methods have been investigated to find a solution, which is most optimal from a computational complexity poi ...

Springer-Verlag2009

MLP Based Hierarchical System for Task Adaptation in ASR

Hervé Bourlard, Joel Praveen Pinto

We investigate a multilayer perceptron (MLP) based hierarchical approach for task adaptation in automatic speech recognition. The system consists of two MLP classifiers in tandem. A well-trained MLP available off-the-shelf is used at the first stage of the ...

2009

Optimal Setting of Weights, Learning Rate, and Gain

The optimal setting of the initial weights, learning rate, and gain of the activation function, which are key parameters of a neural network, influencing training time and generalization performance, are investigated by means of a large number of experimen ...

IDIAP1997

High Order and Multilayer Perceptron Initialization

Proper initialization is one of the most important prerequisites for fast convergence of feed-forward neural networks like high order and multilayer perceptrons. This publication aims at determining the optimal variance (or range) for the initial weights a ...

IEEE1997

Incorporation of Liquid-Crystal Light Valve Non-Linearities in Optical Multilayer Neural Networks

Sigmoidlike activation functions, as available in analog hardware, differ in various ways from the standard sigmoidal function because they are usually asymmetric, truncated, and have a non-standard gain. We present an adaptation of the backpropagation lea ...

The Optical Society of America (OSA)1996

On the Robustness of Perceptron Learning Recurrent Networks

Ali H. Sayed

This paper extends a recent time-domain feedback analysis of Perceptron learning networks to recurrent networks and provides a study of the robustness performance of the training phase in the presence of uncertainties. In particular. a bound is established ...

Elsevier1996

Overcoming Inaccuracies in Optical Multilayer Perceptrons

All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to no ...

AATI1996