Fisher Information Metric
A Riemannian metric on statistical manifolds that measures how much a probability distribution changes in response to small perturbations of model parameters. It is coordinate-invariant and can be applied in optimization contexts such as neural network pruning.
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The paper proposes using geodesic distance determined by the Fisher information metric as a measure of the true change brought about by pruning.
The Fisher information metric is a Riemannian metric on the statistical manifold, proposed by statistician Ronald Fisher, and has the property of being coordinate-independent.
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The paper proposes using geodesic distance determined by the Fisher information metric as a measure of the true change brought about by pruning.
50%UnverifiedThe Fisher information metric is a Riemannian metric on the statistical manifold, proposed by statistician Ronald Fisher, and has the property of being coordinate-independent.
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