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About this paper
Dan Shiebler, Bruno Gavranović, Paul Wilson
This survey paper provides a comprehensive overview of how category theory can serve as a unifying language for machine learning, addressing the field's fragmentation and reliance on heuristic design. It systematically organizes research across gradient-based learning, probabilistic methods, and invariant/equivariant learning, formalizing their core components and demonstrating category theory's utility in structuring knowledge.
Key insights
- Machine learning, despite its success, suffers from fragmentation and a lack of unifying theoretical frameworks, leading to issues like research debt and ad-hoc model construction.
- Category theory provides a suitable 'unifying language' due to its inherent focus on compositionality and abstract structure, enabling a more principled understanding of ML systems.
- Categorical perspectives offer a rigorous way to formalize and generalize concepts across gradient-based learning, probabilistic methods, and invariant/equivariant learning.
Summary by alphaXiv.
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