In Appendix A, we analyze the expressiveness of V ariational Structured Dropout (VSD) through the approximate posterior structure and the parameterization of prior hierarchy. In Appendix B, we provide proof for the KL condition in VSD. In Appendix C, we derive in details the variational objective of VSD with hierarchical prior. A essential question is how expressive the Dropout posterior in VSD is. MC Dropout objective is a lower bound on the scale mixture model's marginal MAP objective.

Figure 1: Timescales estimated in MT model (revised after bug fix). Colormap follows Figure 1 in main text. We thank the reviewers for their insights and suggestions. All references follow the main paper. As noted in supplementary section 1.3, there was a Encoding model fits rely on cross-validation.

Taking this perspective, we make use of the Cramer-Rao inequality and show a "uncertainty principle"-style result: the product of privacy parameter and the

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