Representation and extrapolation in multilayer perceptrons

Authors:
Antony Browne
Affiliations:
School of Computing, Information Systems and Mathematics, London Guildhall University, London U.K.
Venue:
Neural Computation
Year:
2002

Citing 11
Cited 0

Coarse-coded symbol memories and their properties

Complex Systems
Mapping part-whole hierarchies into connectionist networks

Artificial Intelligence - On connectionist symbol processing
Tensor product variable binding and the representation of symbolic structures in connectionist systems

Artificial Intelligence - On connectionist symbol processing
Distributed representations

Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

Neural Networks
Detecting systematic structure in distributed representations

Neural Networks
Connectionist inference models

Neural Networks
Parity: The Problem that Won't Go Away

AI '96 Proceedings of the 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Distributed representations and nested compositional structure

Distributed representations and nested compositional structure
Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning

Neural Computation
Holographic reduced representations

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

To give an adequate explanation of cognition and perform certain practical tasks, connectionist systems must be able to extrapolate. This work explores the relationship between input representation and extrapolation, using simulations of multilayer perceptrons trained to model the identity function. It has been discovered that representation has a marked effect on extrapolation.