A sketch showing how petri-net-style control flow and API resources of a deep learning training phase can be encoded as linear logic implications, with a proof of one reachability property.
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DeepLL: Considering Linear Logic for the Analysis of Deep Learning Experiments
A sketch showing how petri-net-style control flow and API resources of a deep learning training phase can be encoded as linear logic implications, with a proof of one reachability property.