Second Order Derivatives for Network Pruning: Optimal Brain Surgeon Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Paper summary Optimal Brain Surgeon (OBS) is about pruning neural networks to minimize the amount of parameters, training time and overfitting. It is very similar to [Optimal Brain Damage](http://www.shortscience.org/paper?bibtexKey=conf/nips/CunDS89#martinthoma), but claims to choose better weights. However, it does require to compute the inverse hessian. The hessian matrix of a neural network is a $n \times n$ matrix, where $n$ is the number of parameters of the network. Typically, $n > 10^6$. This makes the approach unusable.
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Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Hassibi, Babak and Stork, David G.
Neural Information Processing Systems Conference - 1992 via Bibsonomy
Keywords: dblp


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