Reviews: Learning Attractor Dynamics for Generative Memory

This paper proposes a generative model which builds on ideas from dynamical systems and previous deep learning work like the Kanerva Machine. The main idea is to design and train an architecture that, when unrolled as a dynamical system, has points from the target distribution as attractors. I found the presentation of the model reasonably clear, but thought it suffered from excessive formality. E.g., the description of p(M) could just say that the rows of M are isotropic Gaussian distributions with each row having its own mean and scaled-identity covariance. The references to matrix-variate Gaussians, Kronecker products, vectorization operators, etc. don't contribute to clarity.