Kalman Filter
It's worth noting that unlike KF and particle filters, none of the deep learning stuff that's making headlines these days is actually capable of properly modeling / simulating / approximating a dynamic system with a continuous time variable.Most striking in deep learning, is that when all is said and done (i.e. unwrapping the so-called "recurrent" part of a deep net) all you're left with is a dumb function from Rn to Rp.In other words, while the term "recurrent" might fool you into believing it, there isn't any actual feedback loop in deep nets, and they therefore can't properly model something whose output evolves over time.I think there's something to be explored in adding actual feedback into deep nets.The price is of course that the output is not a vector anymore: you must compute feed forward passes until the net "settles" to a fixed point or rather until the desired behavior is observed at the outputs.Backprop also becomes a whole new game in that setting.[EDIT]: but the prize is: your deep net, instead of being an approximation of a function from Rn to Rp, becomes an approximation of something that can transform a function into another function.