That's effectively the right hand side of the bridge that we're building between formal logic and deep learning. So far their work has been viewed mainly as descriptive, helping to understand neural networks better, but as their abstract calls out: "it gives a constructive procedure to incorporate prior physical knowledge into neural architectures and provide principled way to build future architectures yet to be invented". That's us (we hope)!