AINeutralarXiv – CS AI · Jun 86/10
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A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Researchers propose a mathematical framework for understanding how sparse autoencoders learn and represent concepts, formalizing concept learning as a set-alignment problem and establishing geometric conditions for neuron-level concept representation. The work connects concept learning to formal concept analysis, revealing that neuron interpretation involves complex many-to-many mappings rather than simple one-to-one relationships.