47 citations · 52 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 5 cited
A Framework for Neural Network Pruning Using Gibbs Distributions
Alex Labach, Shahrokh Valaee
Modern deep neural networks are often too large to use in many practical scenarios. Neural network pruning is an important technique for reducing the size of such models and accele…
cs.LG2019
Regularizing Neural Networks by Stochastically Training Layer Ensembles
Alex Labach, Shahrokh Valaee
Dropout and similar stochastic neural network regularization methods are often interpreted as implicitly averaging over a large ensemble of models. We propose STE (stochastically t…
cs.NE2019★ 47 cited
Survey of Dropout Methods for Deep Neural Networks
Alex Labach, Hojjat Salehinejad, Shahrokh Valaee
Dropout methods are a family of stochastic techniques used in neural network training or inference that have generated significant research interest and are widely used in practice…