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About 10,000 deep learning papers have been written about "hard-coding priors about a specific task into a NN architecture works better than a lack of prior" -- but they're typically being passed as "architecture XYZ offers superior performance for [overly generic task category]"
You can always "buy" performance by either training on more data, better data, or by injecting task information into the architecture or the preprocessing. However, this isn't informative about the generalization power of the techniques used (which is the only thing that matters)
Basically, a lot of papers can be rephrased as "we achieved better performance on this specific task by going to great lengths to inject more information about the task in our training setup"
An extreme case of this: working with a synthetically-generated dataset where samples follow a "template" (e.g. bAbI), and manually hard-coding that template into your NN architecture
Fitting parametric models via gradient descent, unsurprisingly, works best when what you are fitting is already a template of the solution. Of course, convnets are an instance of this (but in a good way, since their assumptions generalize to all visual data).
For most relevant applications, in particular medicine, we will never have enough data to "let the data sort itself out". Apart from this, why ignore that we have decades or even centuries of knowledge? Finding the best way to incorporate this into #MachineLearning is key
Agree, especially if the goal is to mimic the brain.
Not doing this myself but I could see situations where data is limited and adding knowledge could help it generalize.
The point is that by picking a specific architecture you are implicitly selecting a prior. There is no way to avoid doing this since the space of all possible models is vastly greater than any data you could possibly select.
Can you give an example of the kind of thing you are describing? What is the "prior" knowledge? I think there is a huge gap between the language of architectural structures and typical languages for prior knowledge.
I mentioned the example of bAbI. If you already know how to go from the input data to the answer, you can express the process as a parametric formula and have your network learn the parameters. Most of the work was done by you, not by your network.
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