
Gradient sampling optimization techniques, such as StochGradAdam, can significantly preserve accuracy during and after the pruning process of neural networks compared to traditional optimization methods.


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robust-neural-pruning-with-gradient-sampling-optimization-maintains-high-accuracy-in-residual-neural-networks


Robust Neural Pruning with Gradient Sampling Optimization Maintains High Accuracy in Residual Neural Networks
