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Deep Learning with Dynamic Computation Graphs
[article]
2017
arXiv
pre-print
Neural networks that compute over graph structures are a natural fit for problems in a variety of domains, including natural language (parse trees) and cheminformatics (molecular graphs). However, since the computation graph has a different shape and size for every input, such networks do not directly support batched training or inference. They are also difficult to implement in popular deep learning libraries, which are based on static data-flow graphs. We introduce a technique called dynamic
arXiv:1702.02181v2
fatcat:wlhqjcgoofgehpkwa626q3gyoa