7 citations · 12 across the 11 of their papers we have counts for
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cs.LG2019★ 1 cited
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…
cs.LG2019★ 1 cited
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…
cs.LG2019★ 1 cited
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…