Publications

Here's a collection of research papers I've written and published in various conferences. We're passionate about exploring new methods for improving neural systems.

Subnetwork Ensembles: Theoretical Insights and Empirical Investigations

PhD Dissertation

Neural network ensembles have been effectively used to improve generalization by combining the predictions of multiple independently trained models. However, the growing scale and complexity of deep neural networks have led to these methods becoming prohibitively expensive and time consuming to implement. Low-cost ensemble methods have become increasingly important as they can alleviate the need to train multiple models from scratch while retaining the generalization benefits that...

Stochastic Subnetwork Annealing: A Regularization Technique for Fine Tuning Pruned Subnetworks

ICLR 2024 (In Review)

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 removed from trained models with little discernible loss in accuracy after a small number of continued training epochs. However, pruning too many parameters at once often causes an initial steep drop in accuracy which can undermine convergence quality. Iterative pruning approaches mitigate this by gradually removing...

Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks

AAAI 2022

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 associated with training several independent networks becomes expensive. We introduce a fast, low-cost method for creating diverse ensembles of neural networks without needing to train multiple models from scratch. We do this by first training a single parent network. We then create child networks by cloning the parent and...

Interpretable Diversity Analysis: Visualizing Feature Representations in Low-Cost Ensembles

IJCNN 2022

Diversity is an important consideration in the construction of robust neural network ensembles. A collection of well trained models will generalize better to unseen data if they are diverse in the patterns they respond to and the predictions they make. Encouraging diversity becomes especially important for low-cost ensemble methods, as members often share network structure or training epochs in order to avoid training several independent networks from scratch...

Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution

GECCO 2023

Neuroevolution is a promising area of research that combines evolutionary algorithms with neural networks. A popular subclass of neuroevolutionary methods, called evolution strategies, relies on dense noise perturbations to mutate networks, which can be sample inefficient and challenging for large models with millions of parameters. We introduce an approach to alleviating this problem by decomposing dense mutations into low-dimensional subspaces. Restricting mutations in this way can significantly reduce variance as...

Synaptic Stripping: How Pruning Can Bring Dead Neurons Back To Life

IJCNN 2023

Rectified Linear Units (ReLU) are the default choice for activation functions in deep neural networks. While they demonstrate excellent empirical performance, ReLU activations can fall victim to the dead neuron problem. In these cases, the weights feeding into a neuron end up being pushed into a state where the neuron outputs zero for all inputs. Consequently, the gradient is also zero for all inputs, which means that the weights which feed into the neuron cannot update. The neuron is not able to recover from direct back propagation and model capacity is reduced as those parameters can no longer be further optimized. Inspired by a neurological process of the same name, we introduce Synaptic Stripping as a means to combat this dead neuron problem...

Low-Cost Ensemble Learning: Surveying Efficient Methods For Training Multiple Deep Neural Networks

PhD Preliminary Exam

Ensemble learning has long been known to be a reliable and consistent way to improve generalization performance across a wide range of machine learning tasks. Instead of training and making predictions with a single model, ensembles use several independent models and combine their predictions together. However, training several independent models from scratch can become prohibitively expensive as deep neural networks continue to grow in both scale and complexity...

Quantum Neural Selection: Finding Performant Subnetworks with Quantum Algorithms

GECCO 2022

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 are slowly updated over hundreds of epochs. Recently, it's been shown that large, randomly initialized, neural networks contain subnetworks that perform as well as fully trained models. This insight offers a promising avenue for...