Brute-force memorization is
typically not thought of as an effective form of learning. At the same time, it’s possible that sheer
memorization can in part be an effective problem-solving strategy for natural tasks.
I like the conclusion. Basically neural nets are just beasts with too many parameters and they even show you don't even need that many parameters to fit any data set of size n. This is one reason I think neural nets are kinda a dead end. People don't understand them and it is impossible to get any explanatory results from them and based on these results that kinda makes sense. Neural nets don't learn, they just memorize.
The local linear approximation is a cool idea in this context even though most non-linear systems are modeled in exactly this way. You take a complicated thing and linearized it to understand it. I'll have to look further into lime.
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I like the conclusion. Basically neural nets are just beasts with too many parameters and they even show you don't even need that many parameters to fit any data set of size n. This is one reason I think neural nets are kinda a dead end. People don't understand them and it is impossible to get any explanatory results from them and based on these results that kinda makes sense. Neural nets don't learn, they just memorize.
https://arxiv.org/abs/1311.2901
Check out figure 2. The network learns composition from fundamental shapes and gradients to compositional ones. It's kinda awe inspiring.
Not NN specific, but some more work on explanations: https://homes.cs.washington.edu/~marcotcr/blog/lime/
Visualization of attention mechanisms is pretty cool for explanations: https://arxiv.org/pdf/1502.03044.pdf http://torch.ch/blog/2015/09/21/rmva.html
The local linear approximation is a cool idea in this context even though most non-linear systems are modeled in exactly this way. You take a complicated thing and linearized it to understand it. I'll have to look further into lime.