Interactive initialization of the multilayer perceptron

Maximum covariance method for weight initialization of multilayer perceptron network

Performance analysis of a MLP weight initialization algorithm

The determination of the initial weights is an important issue in multilayer perceptron design. Recently, we have proposed a new approach to weight initialization based on discriminant analysis techniques. In this paper, the performances of multilayer perceptrons (MLPs) initialized by non-parametric discriminant analysis are compared to those of randomly initialized MLPs using several synthetic...

Accurate Initialization of Neural Network Weights by Backpropagation of the Desired Response

Proper initialization of neural networks is critical for a successful training of its weights. Many methods have been proposed to achieve this, including heuristic least squares approaches. In this paper, inspired by these previous attempts to train (or initialize) neural networks, we formulate a mathematically sound algorithm based on backpropagating the desired output through the layers of a ...

IDIAP Technical report

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 value of the initial weight v ariance (or range), which is the principal parameter of random weight initialization methods for both types of neural networks. An overview of random weight...

Accurate Initialization of Neural Network Weights

Abstracf Proper initialization of neural networks is critical for a successful training of its weights. Many methods have been proposed to achieve this, including heuristic least squares approaches. In this paper, inspired by these previous attempts to train (or initialize) neural networks, we formulate a mathematically sound algorithm based on backpropagating the desired output through the lay...