Reviews: Modular Networks: Learning to Decompose Neural Computation

The paper is concerned with conditional computation, which is an interesting topic yet at early stages of research, and as such one that requires much research and investigation. The paper proposes a latent-variable approach to constructing modular networks, modeling the choice of processing modules in a layer as a discrete latent variable. A modular network is composed of L modular layers, each comprised of M modules and a controller. Each module is a function (standard layer) f_i(x; \theta_i). The controller accepts the input, chooses K of the M modules to process the input, and outputs the as the module output. Modular layers can be stacked, or placed anywhere inside a standard network.