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What can linearized neural networks actually say about generalization?

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Reaction Enthalpies Using the Neural-Network-Based X1 Approach: The Important Choice of Input Descriptors

Matthew Wodrich

Artificial neural networks represent a simple but efficient way to model and correct known errors existing between commonly used density functional computations and experimental data. The recently proposed X1 approach combines B3LYP energies with a neural- ...

2009

Optimization framework for calibration of constitutive models enhanced by neural networks

Laurent Vulliet, Rafal Filip Obrzud

A two-level procedure designed for the estimation of constitutive model parameters is presented in this paper. The neural network (NN) approach at the first level is applied to achieve the first approximation of parameters. This technique is used to avoid ...

2009

Fast Face Detection using MLP and FFT

We propose a new technique for a faster computation of the activities of the hidden layer units. This has been demonstrated on face detection examples. ...

1999

Optimization of high order perceptrons

Neural networks are widely applied in research and industry. However, their broader application is hampered by various technical details. Among these details are several training parameters and the choice of the topology of the network. The subject of this ...

École Polytechnique Fédérale de Lausanne1997

Optimization of high order perceptrons

EPFL1997

Fast Object Detection using MLP and FFT

We propose a new technique for a faster computation of the activities of the hidden layer units. This has been demonstrated on face detection examples. ...

IDIAP1997

A Boolean Approach to Construct Neural Networks for Non-Boolean Problems

A neural network construction method for problems specified for data sets with in- and/or output values in the continuous or discrete domain is described and evaluated. This approach is based on a Boolean approximation of the data set and is generic for va ...

1996

Hardware-Friendly Learning Algorithms for Neural Networks: An Overview

The hardware implementation of artificial neural networks and their learning algorithms is a fascinating area of research with far-reaching applications. However, the mapping from an ideal mathematical model to compact and reliable hardware is far from evi ...

IEEE Computer Society Press1996