1 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
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…
Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder
Cooper J. Mellema, Alex Treacher, Kevin P. Nguyen +1
Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functi…
Anatomically-Informed Data Augmentation for functional MRI with Applications to Deep Learning
Kevin P. Nguyen, Cherise Chin Fatt, Alex Treacher +3
The application of deep learning to build accurate predictive models from functional neuroimaging data is often hindered by limited dataset sizes. Though data augmentation can help…