Introduction to Neural Network Verification [pdf]

A weird property of the described abstractions is that as you go tighter (interval -> zonotope -> polyhedra), the trained networks counterintuitively become less robust. Why does more precision in verification hurt training?

A recent work not mentioned in the last chapter "Adversarial Training with Abstraction" is [1], which kind of explains this issue using the notions of continuity and sensitivity of the abstractions.

[1]: https://arxiv.org/abs/2102.06700

Does anyone know some employers that are hiring for this stuff in industry? I can’t imagine many startups apply this stuff due to prohibitive costs. Research in this area is obviously orders of magnitude more computationally taxing than simply training neural networks.

So, stupid question, is it possible to train neural networks using 3D CGI that will then be somewhat performant in real life?

Like, would a network be able to overcome the error due to switch between a 3d model and actual video?

For anyone interested in elegant implementations of state of the art algorithms for verification, there is a nice library in Jax:

https://deepmind.com/research/open-source/efficient-and-tigh...

This is something I am very interested in, There’s lot of work to be done when it comes to building verified and explainable learning systems (not just neural networks).

I think the verification tools are finally getting better to the point of them being useful for this kind of stuff. L

Neural network verification is one of the most exciting research areas IMO, but is still little understood! The maths behind it are beautiful btw.

Good job putting this together :)