Skeletonization: a technique for trimming the fat from a network via relevance assessment
Advances in neural information processing systems 1
Creating artificial neural networks that generalize
Neural Networks
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Advances in neural information processing systems 2
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
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This paper presents a new pruning method to determine a nearly optimum multi-layer neural network structure. The aim of the proposed method is to reduce the size of the network by freezing any node that does not actively participate in the training process. A node is not active if it has little or no effect to reduce the error of the network as the training proceeds. Experimental results demonstrate a moderate to nearly significant reduction in the network size and generalization performance. A notable improvement in the network's training time is also observed.