Reviews: Combining Generative and Discriminative Models for Hybrid Inference

Overall this is a nice idea that works on using black box models to amortize the residuals from doing inference assuming a linearized approximation to the model. I found the experiments to be well organized albeit mostly on small scale/synthetic data. Summary: This paper introduces a procedure for combining graph neural networks with traditional methods for probabilistic inference (instantiated in HMMs). When we have linear dynamics in a HMM, inference is exact. For nonlinear dynamics, when we have access to the functional form of the true dynamics of the state space model, we can linearize the transition and emission functions (via a Taylor expansion) and represent them as matrices.