activity
20222024
most citedPrune and Tune Ensembles: Low-Cost Ensemble Learning With Sparse Independent Subnetworks

3 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

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…

cs.LG2023

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…

cs.LG2023

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…

cs.NE2023

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…

cs.LG2023

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…

cs.LG2023

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…

cs.LG2022★ 3 cited

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…