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Is DNN the answer or a stepping stone?

One issue I haven’t seen addressed very much is the relationship between the DNN learning algorithms available now, and the learning algorithms for real NN applications. When we started learning about neural networks in the past (the early 90s to the late 90s), the DNN research literature was rather thin, whereas the real NN research literature was pretty extensive. However, over the last several years as we’ve seen tremendous advances in DNN algorithms, many of the underlying NN techniques have been abandoned. I’m wondering: has this occurred because DNN has matured as a tool or because there are real-world applications that demand more power and flexibility than were available 30-40 years ago? My personal opinion (unexpressed yet) is that the former is more likely, with the latter being a possible second option.

A:

The relationship of DNN methods to NN methods depends heavily on your definition of DNN. There are two “main streams” of DNN research:

Deep architecture search (or simply deep learning in general, if you are more interested in algorithms) – DNN models are built with a variety of heuristics, trained using different techniques (e.g. gradient descent, gradient ascent, reinforcement learning) and different objectives (e.g. classification, regression, learning or inference tasks). One of the most popular and recent areas of these methods is (deep) RL [1,2], when the agent is expected to take actions in unknown environment [3].
Deep learning algorithms that solve a problem or learn something from very little training – for example: Deep Neural Networks [4]. In this case, the neural architecture is typically the main focus and its learning approach may or may not also involve search. (see: AlexNet [5] etc.)

One of the reasons these methods are less well-established or popular than gradient-based “traditional” methods is that for most models the parameters are very high-dimensional (e.g. the number of parameters in most deep architectures is more than $10^5$), making it infeasible to learn with gradient methods.
On another note, the DNN community is still (maybe unsurprisingly) fairly centralized around academia (e.g. most papers on DNN/NLP are published in TACL/NAACL,

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