55 citations · 61 across the 12 of their papers we have counts for
5 papers · 1 filter
Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks
Tim Whitaker, Darrell Whitley
Pruning methods have recently grown in popularity as an effective way to reduce the size and computational complexity of deep neural networks. Large numbers of parameters can be re…
Randomly Initialized Subnetworks with Iterative Weight Recycling
Matt Gorbett, Darrell Whitley
The Multi-Prize Lottery Ticket Hypothesis posits that randomly initialized neural networks contain several subnetworks that achieve comparable accuracy to fully trained models of t…
Interpretable Diversity Analysis: Visualizing Feature Representations In Low-Cost Ensembles
Tim Whitaker, Darrell Whitley
Diversity is an important consideration in the construction of robust neural network ensembles. A collection of well trained models will generalize better if they are diverse in th…
Synaptic Stripping: How Pruning Can Bring Dead Neurons Back To Life
Tim Whitaker, Darrell Whitley
Rectified Linear Units (ReLU) are the default choice for activation functions in deep neural networks. While they demonstrate excellent empirical performance, ReLU activations can…
Prune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks
Tim Whitaker, Darrell Whitley
Ensemble Learning is an effective method for improving generalization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associat…