SATnet is an interesting paper, in this regard. It's built on a theory of backpropping through the semi-definite programming relaxation of a MAXSAT problem, to

SATnet is an interesting paper, in this regard. It's built on a theory of backpropping through the semi-definite programming relaxation of a MAXSAT problem, to learn the MAXSAT problem associated with a class of training examples. It gets good results: learns to solve sudoku puzzles with only binary feedback on its performance (i.e. "that's the solution" or "that's not the solution", no partial feedback on partially-correct solutions). They say it's the first time a neural net has been able to do that. On the other hand, if you look at the coefficient matrix they learn for the MAXSAT problem, it doesn't correspond to any logical proposition because the overwhelming majority of the learned coefficients are close to 0, whereas in the representation of a MAXSAT problem, the coefficients must be -1, 0 or 1, where 0 means the corresponding variable doesn't occur. Is it a major result? Well I'm certainly impressed. Do we understand what the network is doing? I don't think we do... I'm still glad to have read the paper, though. I don't know what standard you would hold researchers to, to let through results like SATnet but reject spray-and-pray results which only look good because of offline multiple sampling. If you could require a lab notebook of authors, detailing every step of development, that might work. https://arxiv.org/abs/1905.12149 https://arxiv.org/abs/1905.12149