Reviews: Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes

As the authors note, capacity is often balanced against robustness; since code redundancy is needed to enable recovery from noise, capacity is necessarily reduced because of the redundancy, making this an especially difficult problem. The authors rise to this challenge and claim to produce a network that exhibits "exponential capacity, robustness to large errors, and self decoding or clean-up of these errors". There network takes the structure of a restricted Boltzmann machine wherein the hidden units are comprised of clusters of neurons that laterally inhibit each other, each with the same connectivity to the input units but with different weights. The authors motivate their network using inspirations and ideas from expander graphs, error correcting codes, and Hopfield Networks. The proposed solution is straightforward and well-motivated, and all the design and algorithm choices seem quite sensible.