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Towards Architecture-Agnostic Neural Transfer: a Knowledge-Enhanced Approach

Publication Type: 
Edited Conference Meeting Proceeding
Abstract: 
The ability to enhance deep representations with prior knowledge is receiving a lot of attention from the AI community as a key enabler to improve the way modern Artificial Neural Networks (ANN) learn. In this paper we introduce our approach to this task, which relies on a knowledge extraction algorithm, a knowledge injection algorithm and a common intermediate knowledge representation as an alternative to traditional neural transfer. As a result of this research, we envisage a knowledge-enhanced ANN, which will be able to learn, characterise and reuse knowledge extracted from the learning process, thus enabling more robust architecture-agnostic neural transfer, greater explainability and further integration of neural and symbolic approaches to learning.
Conference Name: 
International Joint Conference on Artificial Intelligence
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
10/08/2019
Conference Location: 
China
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
No