Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
Journal of the Royal Statistical Society: Series B (Methodological) , volume =
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
A generalization of Elo ratings updates player strengths via the score (log-likelihood gradient) for varied game outcomes, with derived properties of zero expected value, summation to zero, and reversion to unobserved true skills.
Joint NMF and binomial regression learns response-relevant text signals with competitive performance on simulations and review data.
citing papers explorer
-
Improving Dictionary Learning with Gated Sparse Autoencoders
Gated SAEs decouple which features to use from how large their activations should be, applying the L1 penalty only to selection and thereby eliminating shrinkage while halving the number of firing features needed for good fidelity.
-
Score-Driven Rating System for Sports
A generalization of Elo ratings updates player strengths via the score (log-likelihood gradient) for varied game outcomes, with derived properties of zero expected value, summation to zero, and reversion to unobserved true skills.
-
Learning Interpretable Text Signals for Structured Responses
Joint NMF and binomial regression learns response-relevant text signals with competitive performance on simulations and review data.