Revenge of Monosemanticity: Specialized Neurons Improve Data Efficiency in MLPs

Abstract:Understanding how neural networks learn and organize features is central to understanding their behavior. Much existing theory of feature learning has focused on the emergence of a global low-dimensional predictive geometry. We show that this picture is incomplete. In regression problems with clustered data, we demonstrate that multilayer perceptrons (MLPs) naturally develop monosemantic specialized neurons: individual neurons become strongly aligned with a specific predictive feature relevant to a particular region of the input space. Rather than learning a single global low-dimensional representation, MLPs learn a collection of local low-dimensional representations that can collectively span a high-dimensional space. This specialization provably gives MLPs a data-efficiency advantage over feature-learning methods based on a global low-dimensional representation.

Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2608.24007 [cs.LG]

(or arXiv:2608.24007v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.24007 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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