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Found 3 papers, 1 papers with code
The Simpler The Better: An Entropy-Based Importance Metric To Reduce Neural Networks' Depth
While deep neural networks are highly effective at solving complex tasks, large pre-trained models are commonly employed even to solve consistently simpler downstream tasks, which do not necessarily require a large model's complexity.
NEPENTHE: Entropy-Based Pruning as a Neural Network Depth's Reducer
While deep neural networks are highly effective at solving complex tasks, their computational demands can hinder their usefulness in real-time applications and with limited-resources systems.
Can Unstructured Pruning Reduce the Depth in Deep Neural Networks?
Pruning is a widely used technique for reducing the size of deep neural networks while maintaining their performance.
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