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Yeah, I very much don't understand OP's argument. And its trivial to write out the nodes and edges (at least for trivially sized neural networks).
by jonnycomputer 5y ago
Yeah, I very much don't understand OP's argument. And its trivial to write out the nodes and edges (at least for trivially sized neural networks).
- zwaps 5y agoI think what op means is this: A graph is mathematically a set of vertices (nodes) and a set of ordered or unordered tuples giving the edges (ties). Now, sometimes you might have a weight on these edges, for example by specifying some function on the edge set. However, it is difficult to see how a neural network that includes operations like sum, multiply and tanh, might be modeled this way. How do you describe a dropout as a graph? I think the argument is that a graph is not sufficient to describe a NN, so technically speaking a NN is not a graph. It is more. It has edges between x and f(x), but we also need to specify what f(x) is. The mathematical definition of a graph doesn't do that.
- deleted 5y ago[deleted]
- Retric 5y agoA weighted graph can include weights for both the vertices and edges. For example a network latency diagram may include the physical wires as separate from the router as the router latency may depend on network load. Similarly routers themselves have internal bandwidth limitations etc. As to the rest separating the NN from everything needed to generate it is a useful distinction. You’re not going to generate a different f(x) by slightly changing the training set etc. It’s however a somewhat arbitrary distinction.