Showing 2026Show all
2 papers · 1 filter
cs.LG2026
The Geometric Structure of Models Learning Sparse Data
Thomas Walker, T. Mitchell Roddenberry, Ahmed Imtiaz Humayun +2
The manifold hypothesis (MH) is often used to explain how machine learning can overcome the curse of dimensionality. However, the MH is only applicable in regimes where the trainin…
cs.LG2026
The Linear Centroids Hypothesis: Features as Directions Learned by Local Experts
Thomas Walker, Ahmed Imtiaz Humayun, Randall Balestriero +1
The Linear Representation Hypothesis (LRH) identifies features of a trained deep network (DN) as linear directions in the activation spaces, i.e., output spaces of intermediate lay…