no code implementations • 13 May 2022 • Tianhui Zhou, William E. Carson IV, Michael Hunter Klein, David Carlson
Finally, we justify our approach by providing theoretical analyses that demonstrate that MDCN improves on the generalization bound of the new, unobserved target center.
1 code implementation • 7 Jan 2022 • William E. Carson IV, Austin Talbot, David Carlson
Deep autoencoders are often extended with a supervised or adversarial loss to learn latent representations with desirable properties, such as greater predictivity of labels and outcomes or fairness with respects to a sensitive variable.
no code implementations • 24 Sep 2021 • William E. Carson IV, Dmitry Isaev, Samatha Major, Guillermo Sapiro, Geraldine Dawson, David Carlson
Second, we show this same model can be used to learn a disentangled representation of multimodal biomarkers that results in an increase in predictive performance.