From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
NeutralArtificial Intelligence
A recent study discusses the evolution of sparse autoencoders in neural networks, highlighting their role in extracting interpretable features. While traditionally viewed as linear and orthogonal, new findings suggest that these models may also capture hierarchical and nonlinear characteristics, expanding our understanding of their capabilities.
— Curated by the World Pulse Now AI Editorial System


