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Learning to Transfer: A Foliated Theory

arXiv 2107.10763 ↗First posted 22 Jul 2021cs.LGcited by 0

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Abstract (arXiv)

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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References · 22

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10 more
  1. 2018Recasting Gradient-Based Meta-Learning as Hierarchical Bayescited by 180
  2. 2018An introduction to domain adaptation and transfer learningcited by 112
  3. 2020Deep Visual Domain Adaptationcited by 91
  4. 2020On the Value of Target Data in Transfer Learningcited by 62
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  10. 2019Disentangled Skill Embeddings for Reinforcement Learningcited by 9

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