A Unifying View of Deep Networks and Hierarchical Temporal Memory
Browsing the NUPIC Theory mailing list, I came across a post by Fergal Byrne on the differences and similarities between Deep Learning and MPF/HTM. It’s a great background into some of the pros and cons of each.
Given the popularity and demonstrated success of Deep Learning methods it’s good to understand how they work and how they relate to MPF/HTM theory. For example, both involve construction of hierarchical data representations created via a series of unsupervised classifiers. Fergal rightly admonishes proponents of both methods for their reluctance to research the alternatives!
The article can be found here:
Also published on Medium.