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Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation
Konstantinos Pitas
Explaining how overparametrized neural networks simultaneously achieve low risk and zero empirical risk on benchmark datasets is an open problem. PAC-Bayes bounds optimized using v…
Revisiting hard thresholding for DNN pruning
Konstantinos Pitas, Mike Davies, Pierre Vandergheynst
The most common method for DNN pruning is hard thresholding of network weights, followed by retraining to recover any lost accuracy. Recently developed smart pruning algorithms use…
The role of invariance in spectral complexity-based generalization bounds
Konstantinos Pitas, Andreas Loukas, Mike Davies +1
Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels. Describing CNN…
FeTa: A DCA Pruning Algorithm with Generalization Error Guarantees
Konstantinos Pitas, Mike Davies, Pierre Vandergheynst
Recent DNN pruning algorithms have succeeded in reducing the number of parameters in fully connected layers, often with little or no drop in classification accuracy. However, most…