End-to-End Learnable Histogram Filters

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The limits and potentials of deep learning for robotics (2018, background): “For example, Jonschkowski et al. [50] solve the aforementioned localization problem for quad-copters by combining a histogram ﬁlter with back-propagation-learned motion and sensing models.”
Chasing Ghosts: Instruction Following as Bayesian State Tracking (2019, methodology): “However, recent work has demonstrated that Bayes filter implementations – including Kalman filters [27], histogram filters [14] and particle filters [28, 29] – can be embedded into deep neural networks.”
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations (2017, background): “Other examples of restriction biases in the visual representation learning literature include convolutional networks [10], spatial transformer networks [5], and spatial softmax [11], which incorporate priors about visual input as architectural constraints of a neural network, as well as backprop Kal”