I think part of the reason why memory / attention mechanisms haven't caught on quite yet is that they add a layer of complexity to reasoning about what DNNs can/cannot do, which is already challenging for researchers. It takes awhile to prove that these are scalable enough for a "killer app".

Also, industry applications of deep learning are at least 1-2 years behind academia, so I don't expect to see any use of differentiable memory mechanisms in production anytime soon.

Anyway, the workshop reading list looks amazing. Thanks for this!