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Bayesian program learning looks really promising, largely because it requires less data than deep neural networks to learn. A couple things to note: 1) One of
by vonnik 11y ago
Bayesian program learning looks really promising, largely because it requires less data than deep neural networks to learn.
A couple things to note:
1) One of the authors of the paper, Russ Salakhutdinov, has worked alongside Geoff Hinton to produce major advances in deep learning.
2) While the title appears to include a broad set of problems, for the moment their work has been on images.
- nickhuh 11y agoI agree, but I'd expand on that and say that BPL and Probabilistic programming in general benefits hugely from the fact that it is interpretable and thus useful in ways that deep learning is not quite as useful. On top of that, the integration of causal concepts allows the program to generalize to completely novel situations in ways that it's unclear whether it nor other techniques can. For example, can we teach a neural net that if the sun didn't rise tomorrow people would still go to work at 9am? (As long as they weren't too freaked out of course :-) )