Learning to Transfer: A Foliated Theory

• URL: http://arxiv.org/abs/2107.10763v1
• Date: Thu, 22 Jul 2021 15:46:45 GMT
• Title: Learning to Transfer: A Foliated Theory
• Authors: Janith Petangoda, Marc Peter Deisenroth and Nicholas A. M. Monk
• Abstract summary: Learning to transfer considers learning solutions to tasks in a such a way that relevant knowledge can be transferred to new, related tasks. This is important for general learning, as well as for improving the efficiency of the learning process. We introduce a framework using the differential geometric theory of foliations that provides such a foundation.
• Score: 18.58482811176484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning to transfer considers learning solutions to tasks in a such way that relevant knowledge can be transferred from known task solutions to new, related tasks. This is important for general learning, as well as for improving the efficiency of the learning process. While techniques for learning to transfer have been studied experimentally, we still lack a foundational description of the problem that exposes what related tasks are, and how relationships between tasks can be exploited constructively. In this work, we introduce a framework using the differential geometric theory of foliations that provides such a foundation.

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