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- W4376312509 abstract "Disentangling the factors of variation in data is a fundamental concept in machine learning and has been studied in various ways by different researchers, leading to a multitude of definitions. Despite the numerous empirical studies, more theoretical research is needed to fully understand the defining properties of disentanglement and how different definitions relate to each other. This paper presents a meta-analysis of existing definitions of disentanglement, using category theory as a unifying and rigorous framework. We propose that the concepts of the cartesian and monoidal products should serve as the core of disentanglement. With these core concepts, we show the similarities and crucial differences in dealing with (i) functions, (ii) equivariant maps, (iii) relations, and (iv) stochastic maps. Overall, our meta-analysis deepens our understanding of disentanglement and its various formulations and can help researchers navigate different definitions and choose the most appropriate one for their specific context." @default.
- W4376312509 created "2023-05-13" @default.
- W4376312509 creator A5015157301 @default.
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- W4376312509 date "2023-05-11" @default.
- W4376312509 modified "2023-10-01" @default.
- W4376312509 title "A Category-theoretical Meta-analysis of Definitions of Disentanglement" @default.
- W4376312509 doi "https://doi.org/10.48550/arxiv.2305.06886" @default.
- W4376312509 hasPublicationYear "2023" @default.
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