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Feed-forward neural networks consist of a sequence of layers, in which eachlayer performs some processing on the information from the previous layer. Adownside to this approach is that each layer (or module, as multiple modulescan operate in parallel) is tasked with processing the entire hidden state,rather than a particular part of the state which is most relevant for thatmodule. Methods which only operate on a small number of input variables are anessential part of most programming languages, and they allow for improvedmodularity and code re-usability. Our proposed method, Neural Function Modules(NFM), aims to introduce the same structural capability into deep learning.Most of the work in the context of feed-forward networks combining top-down andbottom-up feedback is limited to classification problems. The key contributionof our work is to combine attention, sparsity, top-down and bottom-up feedback,in a flexible algorithm which, as we show, improves the results in standardclassification, out-of-domain generalization, generative modeling, and learningrepresentations in the context of reinforcement learning.

Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers