Integration by parts definitely needs ML.

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From: Aaron Meurer
Sent: 28 September 2019 00:18
To: sympy
Subject: [sympy] Symbolic integrator using a neural network

There's a review paper for ICLR 2020 on training a neural network to
do symbolic integration. They claim that it outperforms Mathematica by
a large margin. Machine learning papers can sometimes make overzealous
claims, so scepticism is in order.

https://openreview.net/pdf?id=S1eZYeHFDS

The don't seem to post any code. The paper is in double blind review,
so maybe it will be available later. Or maybe it is available now and
I don't see it. If someone knows, please post a link here.

They do cite the SymPy paper, but it's not clear if they actually use SymPy.

I think it's an interesting concept. They claim that they generate
random functions and differentiate them to train the network. But I
wonder if one could instead take a large pattern matching integration
table like RUBI and train it on that, and produce something that works
better than RUBI. The nice thing about indefinite integration is it's
trivial to check if an answer is correct (just check if
diff(integral(f)) - f == 0), so heuristic approaches that can
sometimes give nonsense are tenable, because you can just throw out
wrong answers.

I'm also curious (and sceptical) on just how well a neural network can
"learn" symbolic mathematics and specifically an integration
algorithm. Another interesting thing to do would be to try to train a
network to integrate rational functions, to see if it can effectively
recreate the algorithm (for those who don't know, there is a complete
algorithm which can integrate any rational function). My guess is that
this sort of thing is still beyond the capabilities of a neural
network.

Aaron Meurer

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