Towards a learning theory of representation alignment
Keywords: learning theory, representation learning, model stitching, representation alignment
TL;DR: properties of stitching and connection to kernel alignment to better understand multimodal representation learning.
Abstract: It has recently been argued that AI models' representations are becoming aligned as their scale and performance increase. Empirical analyses have been designed to support this idea and conjecture the possible alignment of different representations toward a shared statistical model of reality. In this paper, we propose a learning-theoretic perspective to representation alignment. First, we review and connect different notions of alignment based on metric, probabilistic, and spectral ideas. Then, we focus on stitching, a particular approach to understanding the interplay between different representations in the context of a task. Our main contribution here is to relate the properties of stitching to the kernel alignment of the underlying representation. Our results can be seen as a first step toward casting representation alignment as a learning-theoretic problem.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 12067
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