[ceejay]

Would it be feasible to replace the "common" component of a recurrent neural network with a convolutional neural network?

My lay person's impression is that at its most basic level a recurrent neural network is simply a "conveyor belt" of neural nets which are affected by external weights as well as by the weights from within the network. More precisely the "internal" weights coming from the layer of perceptrons operating at 1 level shallower than itself. So we're dealing in essence with 2 dimensions (shallower to deeper, and older to newer) instead of just one (shallower to deeper).

[felippee]

Why would you want that? Conv net is not some magic. It's just a crude way to reduce dimensionality by loosing spatial location. For some things it works, for some it doesn't.

I think the shift in thinking should rather be: instead of trying to build the best possible associative memory to associate some A with some B, take the memory modules we have (perhaps not perfect) and try to build something bigger out of them. A dynamical model of the observed reality seems like a great thing to build out of such modules.

And this is what the PVM is. Currently made out of shallow, plain vanilla perceptrons, builds a structure which can be arbitrarily deep. Without any "magical" tricks such as dropout, relu, convolution, pooling etc.