Random feature models with activations parameterized as weighted sums of basis functions achieve lower test loss than fixed-activation random feature baselines, with theoretical guarantees on approximation and sample complexity.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Learning Expressive Random Feature Models via Parametrized Activations
Random feature models with activations parameterized as weighted sums of basis functions achieve lower test loss than fixed-activation random feature baselines, with theoretical guarantees on approximation and sample complexity.