Deep transfer learning on continual learning

Deep transfer learning on continual learning

Artificial intelligent agents acting in the real world interact with a multitude of data streams. As a result, they must attain, accumulate and record various tasks from non-stationary data distributions. In addition, self-governing computational agents must acquire an understanding of new experienc...

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Bibliographic Details
Main Author: Sousa Leite de Carvalho, Marcus Vinicius
Other Authors: Zhang Jie
Format: Thesis-Doctor of Philosophy
Language:English
Published: Nanyang Technological University 2023
Subjects:
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Online Access:https://hdl.handle.net/10356/171082

Internet

https://hdl.handle.net/10356/171082

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