[currymj]

that's exactly it -- they take an existing form for the ansatz (or the general idea of it, at least), and make it more flexible by replacing pieces with neural networks that have many more parameters, while maintaining constraints required by physics. I think this will become very common in the future.

[whinvik]

That maybe true but what I was looking for is a more convincing way of showing that a neural network approximates a function better than other functions, such as say a b-spline. For example, you say that the neural network with many more parameters works better, but what if we had a b-spline with many more nodes.

[hnarayanan]

I don't know anything about anything but I'm willing to bet that the end result is very similar. They're "just" using neural networks as rich approximators.

[jhrmnn]

I'm an author of one of the arXiv papers above. One thing to consider is that the approximative power of a given parametric function is not the only criterium. Being able to optimize that function efficiently is as important. Neural networks excel in this. So the comparison you ask for most likely won't appear, because any other parametric ansatz with tens of thousands (or more) parameters would be impossible to optimize. At the least that's the case in quantum Monte Carlo, the domain of our paper. As for "provable", I also don't think that will appear. All the exact theorems about neural networks are way too abstract to be applicable to practical problems.