Atlas' classifier adopts an inducing point approach, in which the latent variables are augmented with additional inducing points. Our strategy follows closely to the one detailed in Hensman et al., where a bound on the marginal likelihood for classification problems in derived. This bound is optimized by adjusting the hyperparameters of the GP kernel, parameters of the multivariate normal variational distribution, and the inducing inputs/points simultaneously using stochastic gradient descent. The classification approach is implemented using the GpyTorch library, and has the added benefit of scaling more favourably with |D c | than does exact GP inference.