3 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
UQ-ARMED: Uncertainty quantification of adversarially-regularized mixed effects deep learning for clustered non-iid data
Alex Treacher, Kevin Nguyen, Dylan Owens +2
This work demonstrates the ability to produce readily interpretable statistical metrics for model fit, fixed effects covariance coefficients, and prediction confidence. Importantly…
Longitudinal Prognosis of Parkinsons Outcomes using Causal Connectivity
Cooper J. Mellema, Kevin P. Nguyen, Alex Treacher +2
Parkinsons disease (PD) is a movement disorder and the second most common neurodengerative disease but despite its relative abundance, there are no clinically accepted neuroimaging…
DC and SA: Robust and Efficient Hyperparameter Optimization of Multi-subnetwork Deep Learning Models
Alex H. Treacher, Albert Montillo
We present two novel hyperparameter optimization strategies for optimization of deep learning models with a modular architecture constructed of multiple subnetworks. As complex net…
Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data
Kevin P. Nguyen, Albert Montillo
Natural science datasets frequently violate assumptions of independence. Samples may be clustered (e.g. by study site, subject, or experimental batch), leading to spurious associat…
Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI
Cooper J. Mellema, Albert Montillo
Objective: New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify…