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ridge regression

topic2 events
papersTODAY 04:00 UTC

Sparse Autoencoders Can Preserve Different Readouts at Equal Reconstruction Error

A new arXiv paper argues that matching reconstruction error and sparsity levels does not guarantee two sparse autoencoders capture the same linearly decodable information from model activations. The authors formalize this gap as a matrix-valued distortion between optimal ridge readouts and propose decoder-preserving training objectives. The work offers a way to evaluate which downstream signals survive sparse compression.

interpretability/sparse-autoencoders/research/evaluation
papersTODAY 04:00 UTC

Study Finds Structured Features Overfit Where Random Gaussian Features Grok

New research contrasts how over-parameterized ridge regression behaves with structured versus unstructured random feature maps. Prior work by Xu, Vardi and Safran proved that random Gaussian features grok, with the gap between memorization and generalization widening as the weight decay parameter shrinks. The authors report that structured features instead overfit in the regime where random features exhibit grokking.

grokking/generalization/ridge-regression/feature-learning