3 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Neural Subnetwork Ensembles
Tim Whitaker
Neural network ensembles have been effectively used to improve generalization by combining the predictions of multiple independently trained models. However, the growing scale and…
Quantum Neuron Selection: Finding High Performing Subnetworks With Quantum Algorithms
Tim Whitaker
Gradient descent methods have long been the de facto standard for training deep neural networks. Millions of training samples are fed into models with billions of parameters, which…
Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution
Tim Whitaker, Darrell Whitley
Neuroevolution is a promising area of research that combines evolutionary algorithms with neural networks. A popular subclass of neuroevolutionary methods, called evolution strateg…
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…