Models like LASSO and ridge regression are extensively used in practice due to their interpretability, ease of use, and strong theoretical guarantees.

The traditional approach to data-driven modeling of a neural population typically involves two separate stages. First, neural population activity is recorded while an animal performs a task of interest.

Our source code for our work is available here.

This section describes Stein variational gradient descent (SVGD) by Liu and Wang [19]. The overview is meant as supplementary material for Section 5, where we propose to use SVGD for inferring the DiBS posteriors p(Z | D) and p(Z, Θ | D). In contrast to sampling-based MCMC or optimizationbased variational inference methods, SVGD iteratively transports a fixed set of particles to closely match a target distribution, akin to the gradient descent algorithm in optimization. We refer the reader to Liu and Wang [19] for additional details. Let p(x) with x X be a differentiable density that we want to sample from, e.g., to estimate an expectation.

For example, the déjà vu method shows that for certain representation learning models and training images, it is sometimes possible to correctly predict the foreground label given only the representation of the background - better than through dataset-level correlations.