Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138814
Title: Evolutionary multi-task learning for modular knowledge representation in neural networks
Authors: Chandra, Rohitash
Gupta, Abhishek
Ong, Yew-Soon
Goh, Chi-Keong
Keywords: Engineering::Computer science and engineering
Issue Date: 2017
Source: Chandra, R., Gupta, A., Ong, Y.-S., & Goh, C.-K. (2018). Evolutionary multi-task learning for modular knowledge representation in neural networks. Neural Processing Letters, 47(3), 993-1009. doi:10.1007/s11063-017-9718-z
Journal: Neural Processing Letters
Abstract: The brain can be viewed as a complex modular structure with features of information processing through knowledge storage and retrieval. Modularity ensures that the knowledge is stored in a manner where any complications in certain modules do not affect the overall functionality of the brain. Although artificial neural networks have been very promising in prediction and recognition tasks, they are limited in terms of learning algorithms that can provide modularity in knowledge representation that could be helpful in using knowledge modules when needed. Multi-task learning enables learning algorithms to feature knowledge in general representation from several related tasks. There has not been much work done that incorporates multi-task learning for modular knowledge representation in neural networks. In this paper, we present multi-task learning for modular knowledge representation in neural networks via modular network topologies. In the proposed method, each task is defined by the selected regions in a network topology (module). Modular knowledge representation would be effective even if some of the neurons and connections are disrupted or removed from selected modules in the network. We demonstrate the effectiveness of the method using single hidden layer feedforward networks to learn selected n-bit parity problems of varying levels of difficulty. Furthermore, we apply the method to benchmark pattern classification problems. The simulation and experimental results, in general, show that the proposed method retains performance quality although the knowledge is represented as modules.
URI: https://hdl.handle.net/10356/138814
ISSN: 1370-4621
DOI: 10.1007/s11063-017-9718-z
Rights: © 2017 Springer Science+Business Media, LLC. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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