# Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems

arXiv: https://arxiv.org/abs/2208.03316 · HTML: https://mlsanity.com/paper/2208.03316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 11 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments](https://mlsanity.com/paper/1710.03641/) (2017)
- [A Systems Theory of Transfer Learning](https://mlsanity.com/paper/2107.01196/) (2021)
- [Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like](https://mlsanity.com/paper/1604.04660/) (2016)
- [Mesarovician Abstract Learning Systems](https://mlsanity.com/paper/2111.14766/) (2021)

## Built on by (most cited first, 2 of 2)

- [On Extending the Automatic Test Markup Language (ATML) for Machine Learning](https://mlsanity.com/paper/2404.03769/) (2024)
- [A Systems Theoretic Approach to Online Machine Learning](https://mlsanity.com/paper/2404.03775/) (2024)

## Abstract (arXiv)

Transfer learning, multi-task learning, and meta-learning are well-studied topics concerned with the generalization of knowledge across learning tasks and are closely related to general intelligence. But, the formal, general systems differences between them are underexplored in the literature. This lack of systems-level formalism leads to difficulties in coordinating related, inter-disciplinary engineering efforts. This manuscript formalizes transfer learning, multi-task learning, and meta-learning as abstract learning systems, consistent with the formal-minimalist abstract systems theory of Mesarovic and Takahara. Moreover, it uses the presented formalism to relate the three concepts of learning in terms of composition, hierarchy, and structural homomorphism. Findings are readily depicted in terms of input-output systems, highlighting the ease of delineating formal, general systems differences between transfer, multi-task, and meta-learning.