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TL;DR: an early effort to establish a 'standard language' for representing and symbolically manipulating (e.g., transforming) every possible neural net that cou
by cs702 9y ago
TL;DR: an early effort to establish a 'standard language' for representing and symbolically manipulating (e.g., transforming) every possible neural net that could ever be constructed with graphs and tensors.
Very cool to see this sort of thing.
- amelius 9y agoTwo questions: Does this work also allow cyclic structures such as recurrent neural networks (RNNs)? Does this work generalize to graphs in general?
- cs702 9y agoIn principle it should, because these guys are using matrices to represent network structures, and every finite graph (cyclic or acyclic, directed or undirected) can be represented by a matrix. Examples: https://en.wikipedia.org/wiki/Adjacency_matrix#Examples https://en.wikipedia.org/wiki/Adjacency_matrix#Examples