Random Cloud: Finding Minimal Neural Architectures Without Training
This work offers a training-free neural architecture search method for practitioners seeking efficient, minimal networks without the computational cost of training.
The Random Cloud method finds minimal neural architectures without training, matching or outperforming pruning baselines on 6 of 7 datasets with up to 87% parameter reduction and faster runtime (0.67-0.94× full training cost).
I propose the \emph{Random Cloud} method, a training-free approach to neural architecture search that discovers minimal feedforward network topologies through stochastic exploration and progressive structural reduction. Unlike post-training pruning methods that require a full train-prune-retrain cycle, this method evaluates randomly initialized networks without backpropagation, progressively reduces their topology, and only trains the best minimal candidate at the end. I evaluate on 7 classification benchmarks against magnitude pruning and random pruning baselines. The Random Cloud matches or outperforms both baselines in 6 of 7 datasets, achieving statistically significant improvements on Sonar ($+4.9$pp accuracy, $p{=}0.017$ vs magnitude pruning) with 87\% parameter reduction. Crucially, the method is faster than both pruning baselines in 4 of 5 datasets (0.67--0.94$\times$ the cost of full training), since it avoids training the full-size network entirely.