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.

翻译：提出随机云方法，一种免训练的神经架构搜索算法，通过随机探索与渐进式结构缩减发现最小前馈网络拓扑结构。与需要完整训练-剪枝-再训练周期的后训练剪枝方法不同，该方法无需反向传播即可评估随机初始化的网络，逐步缩减其拓扑结构，最终仅训练最优的最小候选网络。在7个分类基准上，与幅度剪枝和随机剪枝基线进行对比评估。随机云在6/7的数据集上达到或超越两种基线方法，在声纳数据集上相比幅度剪枝获得统计学显著改进（准确率提升+4.9个百分点，p=0.017），同时实现87%的参数缩减。关键优势在于，该方法在5/4的数据集上比两种剪枝基线更高效（计算成本仅为完整训练的0.67-0.94倍），因其完全避免了训练完整规模网络的过程。