Pruning (artificial neural network): Difference between revisions

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In the context of [[artificial neural network]], '''pruning''' is the practice of removing [[artificial neurons]] after learning, usually with the goal of reducing the computational resources required to run the neural network. A basic algorithm for pruning is as follows<ref>Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). ''Pruning convolutional neural networks for resource efficient inference''. arXiv preprint arXiv:1611.06440.</ref><ref>[https://jacobgil.github.io/deeplearning/pruning-deep-learning Pruning deep neural networks to make them fast and small].</ref>:

In the context of [[artificial neural network]], '''pruning''' is the practice of removing [[artificial neurons]] after learning, usually with the goal of reducing the computational resources required to run the neural network. A basic algorithm for pruning is as follows:<ref>Molchanov, P., Tyree, S., Karras, T., Aila, T., & Kautz, J. (2016). ''Pruning convolutional neural networks for resource efficient inference''. arXiv preprint arXiv:1611.06440.</ref><ref>[https://jacobgil.github.io/deeplearning/pruning-deep-learning Pruning deep neural networks to make them fast and small].</ref>:

#Evaluate the importance of each neuron.

#Rank the neurons according to their importance (assuming there is a clearly defined measure for "importance").

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== References ==

{{reflist}}

[[Category:Artificial neural networks]]

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[[Category:Artificial neural networks]]