Yoshua Benhgio's Learning Deep Architectures for AI book mentions that
we should [...] strive to develop learning algorithms that use the data to determine the depth of the ﬁnal architecture.
Would anyone know of any algorithms proposed thus far to achieve this?
So far I have come across:
The tiling algorithm for building a feed-forward network to learn a Boolean function. It adds layers as well as units, but Boolean functions aren't too relevant for applied problems.