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Last week, deep learning research leader Yann LeCun took part in a Quora Session, during which he answered questions from community members on a wide variety of (mostly machine/deep learning) topics.
During the session, this question was posed:
When will we see a theoretical background and mathematical foundation for deep learning?
The answer turned into a very eloquent overview of three particular thoughts on why deep learning works so well. Here is a quick overview.
LeCun's first point of explanation, which maps to a good reason why deep learning works so well, is as follows:
One theoretical puzzle is why the type of non-convex optimization that needs to be done when training deep neural nets seems to work reliably.
The main idea here is that local minima do not arise in very high dimensional space, so greedy-search gradient optimization is not trapped in a "box." As LeCun states:
It’s hard to build a box in 100 million dimensions.
Moving on, LeCun introduces his next point as:
Another interesting theoretical question is why multiple layers help.
The point here, beyond LeCun stating that there is not a complete understanding as to why, is that multiple layers help to implement complex functions more concisely. While he points out that computer scientists are accustomed to the idea of sequential steps and multiple layers of computation, this doesn't quite cover the reasons why multiple layers in deep neural networks work as they do.
For his last point, he turns to a specific neural network architecture.
A third interesting question is why ConvNets work so well.
Interesting question indeed. This article gets cited as reading for why ConvNet architectures are right for analyzing certain types of signals, touching on the fact that ConvNets actually work very well for some type of signals, like spatial. Sorting out why is this is so is one thing, but noting that it is true places squarely into perspective just how well ConvNets work when they do.
While Yann LeCun was answering the question posed to him on when we could expect a mathematical foundation for deep leaning to emerge, in doing so he provided valuable insight into why deep learning functions as well as it does.
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