Towards architecture-agnostic neural transfer: a knowledge-enhanced approach
Quinn, Sean and Mileo, AlessandraORCID: 0000-0002-6614-6462
(2019)
Towards architecture-agnostic neural transfer: a knowledge-enhanced approach.
In: 28th International Joint Conference on Artificial Intelligence, 10 - 16 Aug 2019, Macao, China.
ISBN 978-0-9992411-2-7
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.
Kraus, Sarit, (ed.)
Proceedings of the 28th International Joint Conference on Artificial Intelligence.
.
International Joint Conferences on Artificial Intelligence Organization. ISBN 978-0-9992411-2-7
Publisher:
International Joint Conferences on Artificial Intelligence Organization