Well like reinforcement learning uses a lot of markov chains, forward/backwards filtering/smoothing etc. Kalman filters are also a sort of Gaussian Process Regression. There is a huge overlap in the classical ML part with signal processing. No specific paper but it's just that ML and especially deep learning often takes already existing ideas from physics or ee and try to apply it on some data, see what happens.