feature-learning

This paper theoretically demonstrates that two-layer neural networks trained on group composition tasks learn spectral representations, with neurons converging to irreducible representations and achieving rotational rank-one alignment, providing a representation-theoretic account of feature learning.

This paper introduces GOEN, a pipeline combining multi-scale features, L2 normalization, and Mahalanobis distance for OOD detection, and finds that CenterLoss regularization actually degrades OOD performance despite improving classification accuracy.

The paper introduces AGOP-Weighted, a post-hoc attribution method that multiplies per-sample gradients by a training-distribution prior to suppress noise and highlight important pixels, and demonstrates significant improvements over existing methods on synthetic and photorealistic benchmarks.

This empirical study validates theoretical findings on feature repulsion and spectral lock-in during the grokking phenomenon in two-layer neural networks, demonstrating how activation functions influence the transition from memorization to generalization.