Publication

A Constructive Training Algorithm for Feedforward Neural Networks with Ternary Weights

Frédéric Jean-Michel Aviolat
1994

Abstract

We address the problem of training a special kind of Boolean feedforward neural networks, namely networks built from linear binary threshold units with ternary weights called majority units. We propose an original constructive training algorithm, inspired from both the approaches of the Tiling and the Upstart algorithms, and we give a simple proof of its convergence. Numerical experiments were carried out to test the size of the constructed networks as well as their generalization ability. Comparisons are made with classical threshold unit networks.

Official source

https://infoscience.epfl.ch/entities/publication/e23c1d14-1d38-4e9e-90d0-6d046e8dc6c1

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Frédéric Jean-Michel Aviolat
1994

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e23c1d14-1d38-4e9e-90d0-6d046e8dc6c1

About this result

Related concepts (31)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Feedforward neural network

A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes (if any) and to the output nodes, without any cycles or loops, in contrast to recurrent neural networks, which have a bi-directional flow.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Related publications (32)

A Constructive Training Algorithm for Feedforward Neural Networks with Ternary Weights

Chat with Graph Search

From recursion to incrementality: return to recurrent neural networks

Ultrasound imaging with beamforming from data that are enhanced by a neural network in the signal domain

Layer-specific functional gradients uncover intrinsic-network organization and feedback processing