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Optimal Pruning for Neural Architectures using Fisher Information Distances
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pruningneural networksFisher informationmodel optimization
2609.16129
Builder Relevance
1h ago80%
Abstract
A new parameter pruning scheme is introduced that utilizes geodesic distances in model space based on Fisher information metrics.
Reality Card
Core Claim
The proposed pruning method outperforms traditional magnitude pruning and local Fisher information methods in terms of accuracy and Matthews correlation coefficient across various architectures and datasets.
Method / Result
Achieved superior performance across all tested architectures and datasets, including MNIST and CIFAR-10.
Limitations
The paper does not specify potential limitations or reproducibility concerns.
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