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This is already starting to happen, albeit quite slowly. I think it will gain a lot of momentum and it will lead to very interesting progress in AI. For exampl
by nextos 5y ago
This is already starting to happen, albeit quite slowly. I think it will gain a lot of momentum and it will lead to very interesting progress in AI.
For example, deep functions + probabilistic models yield things such as deep markov models, which are interpretable and can represent really complex distributions such as music.
Deep functions can also be used during sampling to generate sophisticated proposals in problems where standard algorithms struggle to navigate the posterior.
There are also equivalent ideas being explored in RL, such as the OP.
- medo-bear 5y agocan you name some references please? also, what do you mean by deep functions?
- nextos 5y agoApologies, for the late reply. By deep functions I simply meant small deep networks embedded in probabilistic models as a drop-in replacement for probability distributions, like e.g. a neuralHMM: https://pyro.ai/examples/hmm_funsor.html https://pyro.ai/examples/hmm_funsor.html Aside from looking into Pyro docs and all references, another good entry point is this publication: https://arxiv.org/pdf/1610.05735.pdf https://arxiv.org/pdf/1610.05735.pdf. Here the authors show how to use deep networks to aid sampling of complex probabilistic models.
- medo-bear 5y agoThank you. I didn't notice that you replied. These links are quite helpful. I think that eventually AI will start to incorporate all the new ML stuff into the old symbolic stuff and I think probabilistic models are a good way to link the two