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by badfortrains
4838 days ago
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The thing that NNs have in their favor that other "20 year old techniques" lack is their ability to model any mathematical equation. There is no fundamental limit to the complexity of systems NNs can model (as there is with other AI techniques). The problem with NNs is the difficulty of training them. Back propagation with random initial weights is simple, but it can easily converge on suboptimal local maximum if the learning rate is too aggressive. On the other hand, a slow learning rate requires an exponential increase in training time and data. Back propagation as a method was never really broken, it simply wasn't efficient enough to be effective in most situations. Deep belief techniques seem to remedy these inefficiencies in a significant way, while remaining a generalized solution. Essentially deep belief networks seem to optimize NNs to the point where new problems are now approachable, and greatly improve the performance of current NN solvable problems. The complaint that "the core of the recipe itself is not very new", seems irrelevant in light of the results. |
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I'm sure that a decision tree can also be viewed as a [universal approximator](http://en.wikipedia.org/wiki/Universal_approximation_theorem) if you let tree height go to infinity (just as you need to let layer size grow unbounded with a NN). In practice, this power is at best irrelevant and often actually a liability (you have to control model complexity to prevent overfitting/memorization).
And, importantly, being able to theoretically encode any function within your model is not the same as having a robust learning algorithm that will actually infer those particular weights from a sample of input/output data.