[jarvic]

I just skimmed the post as I don't have time to fully read it right now, but I'll point out a couple of problems that you can run into with neural nets and associated approaches.

One issue that can be a back breaker depending on your application is that, to produce a generalizable model, nets tend to need much more training data than the alternatives. There are ways to work around this, though.

The bigger problem to me is interpretability. Deep learning often gives feature sets that are very good for whatever task you are working on, they are in some senses artificial and it is difficult to relate changes in features to changes in the input data. I work with a lot of biological and medical data, and this is an issue because for some applications it is important not to just get accurate classification results, but to be able to understand what your features mean in the context of the original problem. I saw some interesting work in a computer vision paper earlier this year on trying to learn how to visualize how changes in input and outputs of a neural net were related, I'll try to dig that up later if anyone is interested.

I'm not sure how coherent that was as I was trying to get this typed out in a hurry.

[tinkerdol]

Sure, please post the link to the paper, it sounds interesting.