4 citations · 8 across the 4 of their papers we have counts for
4 papers · 1 filter
Generative Principal Component Regression via Variational Inference
Austin Talbot, Corey J Keller, David E Carlson +1
The ability to manipulate complex systems, such as the brain, to modify specific outcomes has far-reaching implications, particularly in the treatment of psychiatric disorders. One…
AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models
William E. Carson, 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 out…
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility
Liyun Tu, Austin Talbot, Neil Gallagher +1
Probabilistic generative models are attractive for scientific modeling because their inferred parameters can be used to generate hypotheses and design experiments. This requires th…
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
Austin Talbot, David Dunson, Kafui Dzirasa +1
Targeted stimulation of the brain has the potential to treat mental illnesses. We propose an approach to help design the stimulation protocol by identifying electrical dynamics acr…