[19eightyfour]

If this article is correct about limitations, couldn't one simply include a Turing machine model into the process to train algorithms?

Some ideas:

- The vectors are Turing tapes, or

- Each point in a tape is a DNN, or

- The "tape" is actually a "tree" each point in the tape is actually a branch point of a tree with probabilities going each way, and the DNN model can "prune this tree" to refine the set of "spanning trees" / programs.

Or, hehe, maybe I'm leading people off track. I know absolutely nothing about DNN ( except I remember some classes on gradient descent and SVMs from bioinformatics ).

[nullc]

You can bolt all kinds of funny structures into some DNN system, but if the system doesn't have well behaved gradients (or if it isn't even differentiable) it won't train.