>
We’re using formal logic in the form of abstract rewrite systems over a causal graph to perform geometric deep learning. In theory it should be able to learn the same topological structure of data that neural networks do, but using entirely discrete operations and without the random walk inherent to stochastic gradient descent.Abstract rewrite like a computer algebra system's (e.g. Wolfram) term rewriting equation simplication method?
