Fig 12. Mean Absolute Error (MAE) performance for a varying number of networks, as determined by k (x-axis), on unseen test data from CamCAN when latent variables are no longer assumed to have an isotropic covariance structure and the full vectorized covariance is employed as features in the linear regression models.

We note that MHA is able to directly accommodate such a scenario and hence is competitive for all choices of latent variable dimension, k.