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

• URL: http://arxiv.org/abs/2010.08012v1
• Date: Thu, 15 Oct 2020 20:43:17 GMT
• Title: Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
• Authors: Alex Lamb, Anirudh Goyal, Agnieszka S{\l}owik, Michael Mozer, Philippe Beaudoin, Yoshua Bengio
• Abstract summary: 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 and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm.
• Score: 84.57980167400513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previous layer. A downside to this approach is that each layer (or module, as multiple modules can operate in parallel) is tasked with processing the entire hidden state, rather than a particular part of the state which is most relevant for that module. Methods which only operate on a small number of input variables are an essential part of most programming languages, and they allow for improved modularity 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 and bottom-up feedback is limited to classification problems. The key contribution of 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 standard classification, out-of-domain generalization, generative modeling, and learning representations in the context of reinforcement learning.

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