SOURCE-LINKED INTELLIGENCE

Residual-Guided Randomized Neural Networks

arXiv · AI, language, vision and robotics · article · Aug 28, 2026 · UTC

Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the output weights in closed form; however, their performance critically depends on a single uninformed draw of hidden units. This one shot and task uninformed feature construction often leads to redundant representations and suboptimal utilization of model capacity. To address this limitation, we propose a simple and broadly applicable residual guided procedure that greedily constructs the hidden layer using a closed form residual decrease criterion.

Read original source ↗ Open in workspace

recordType

paper

region

Global