Constraint-driven multi-task learning
- URL: http://arxiv.org/abs/2208.11656v1
- Date: Wed, 24 Aug 2022 16:53:54 GMT
- Title: Constraint-driven multi-task learning
- Authors: Bogdan Cretu, Andrew Cropper
- Abstract summary: In this project, we extend the Popper ILP system to make use of multi-task learning.
We introduce constraint preservation, a technique that improves overall performance for all approaches.
- Score: 18.27510863075184
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Inductive logic programming is a form of machine learning based on
mathematical logic that generates logic programs from given examples and
background knowledge.
In this project, we extend the Popper ILP system to make use of multi-task
learning. We implement the state-of-the-art approach and several new strategies
to improve search performance. Furthermore, we introduce constraint
preservation, a technique that improves overall performance for all approaches.
Constraint preservation allows the system to transfer knowledge between
updates on the background knowledge set. Consequently, we reduce the amount of
repeated work performed by the system. Additionally, constraint preservation
allows us to transition from the current state-of-the-art iterative deepening
search approach to a more efficient breadth first search approach.
Finally, we experiment with curriculum learning techniques and show their
potential benefit to the field.
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