I may be wrong, but as far as I understand one problem may be that neural networks are not really graphs or hypergraphs. Books show them as a set of layers and some connecting edges which looks a lot like a graph, but when they are implemented in code, they mostly are matrix operations. So, as far as I understand a program implementing a neural network will be doing matrix operations. If I am right about this, then I don't see how seeing atomspace as a neural network will help.
What I am saying is that I don't think the atoms and links can be connected to make a neural network straightforwardly. Of course, one could make atoms that represent the coefficients of the model that the CNN represents and then connect those with links that have weights and then make a function that can take such a hypergraph and tune the weights. But wouldn't that be very inefficient? Wouldn't you want to just represent a feature vector in atomese and then run CNN on it (through an external library perhaps) and get results in atomese that the other algorithms can pick up? But then again, I have very little idea what I am talking about, so I may be way off. -- You received this message because you are subscribed to the Google Groups "opencog" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/opencog. To view this discussion on the web visit https://groups.google.com/d/msgid/opencog/a31444a0-637d-40ee-8e72-3286165f9b95%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
