We perform experiments on high-dimensional Gaussians, Lotka-V olterra dynamics, and a Pickover attractor.

We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI).

In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors in high-dimensional spaces.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Shared variability is separated into terms that express condition dependence, as well as trial-to-trial variation in trajectories.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

By now Bayesian methods are routinely used in practice for solving inverse problems.

Indoing so, we generalize existing approaches and offer information-theoretic corrections and efficient variational approximations.