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My intuition is that a "program induction" approach has the potential make causal learning possible (without having to predefine the causal graph as I believe P
by BenoitEssiambre 8y ago
My intuition is that a "program induction" approach has the potential make causal learning possible (without having to predefine the causal graph as I believe Pearl requires in his techniques). Here is a description of my failed attempt:
https://www.quora.com/What-deep-learning-ideas-have-you-tried-that-didnt-work/answer/Benoit-Essiambre https://www.quora.com/What-deep-learning-ideas-have-you-trie...
- eli_gottlieb 8y agoWow, how the field advances in but a year! You should take a look at Pyro, and maybe try out implementing some form of variational guide for an adaptor-grammar model of programs. There are papers already claiming to have done it, just not for program induction. I have a few ideas and intuitions to add to yours, so email me via my profile if you want to collaborate.
- BenoitEssiambre 8y agoI wasn't aware of Pyro. Thanks for the reference. I might be interested in collaboration one day, however I would be a terrible collaborator right now as I can only find sporadic short slices of time to put in these sorts of projects. It's rare people show interest in my work so I'll keep you in mind if ever I stumble upon something interesting enough to be worth spending collective efforts on.
- sjg007 8y agoInteresting. I wonder as a next step if you could use a causal graph as part of the controller for producing the programs. So this graph would be predefined but it would able to "reason" on the generated programs. That reasoning could be based on program complexity/minimum description length. So your controller is a supervisor that takes input/output and generates programs that recognize circles.