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could you please introduce some recent papers on this? How to deal with the (approximate) gradient descent for graphical model inference, and then integrate it into the stochastic gradient descent? I know the Domke's workhttp://machinelearning.wustl.edu/mlpapers/papers/AISTATS2012_Domke12 was devoted on this. And the structured prediction energy networks is also on this https://arxiv.org/abs/1703.05667. But the mathematical requirements for both two papers is beyond mine extreme. Could you please show some simpler methods with simpler math derivations? Thanks a lot!

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  • $\begingroup$ RBMs are a pretty reasonable example of graphical models that utilize deep learning. However, as far as I know, they are slowly going out of style. $\endgroup$ – Alex R. Oct 24 '17 at 22:42
  • $\begingroup$ Hi @AlexR. thanks a lot for your comment! I actually want to study about the conditional random field based graphical models for image related problems. I think RBMs based graphical models is too high level for me as it is too abstract for me. $\endgroup$ – mining Oct 24 '17 at 22:54

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