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Found 5 papers, 3 papers with code
Personalized rTMS for Depression: A Review
Personalized treatments are gaining momentum across all fields of medicine.
AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models
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.
Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility
We demonstrate the effectiveness of these developments using synthetic data and electrophysiological recordings with an emphasis on how our learned representations can be used to design scientific experiments.
Estimating a Brain Network Predictive of Stress and Genotype with Supervised Autoencoders
Targeted stimulation of the brain has the potential to treat mental illnesses.
Cross-Spectral Factor Analysis
To facilitate understanding of network-level synchronization between brain regions, we introduce a novel model of multisite low-frequency neural recordings, such as local field potentials (LFPs) and electroencephalograms (EEGs).
