Have you looked at capsule-routing algorithms that use expectation-maximization to generate layer outputs in deep neural networks? For example, in https://resea

Have you looked at capsule-routing algorithms that use expectation-maximization to generate layer outputs in deep neural networks? For example, in https://research.google/pubs/pub46653/ https://research.google/pubs/pub46653/ and https://arxiv.org/abs/1911.00792 https://arxiv.org/abs/1911.00792 each output capsule in a layer is generated by a Gaussian mixture model and also has an associated "activation value" that increases to the extent the GMM can explain (i.e., generate) its input data data "better" than the GMMs of other output capsules in the same layer. I wonder, do you think these routing algorithms, which are differentiable end-to-end, could be generalized to Dirichlet Process Mixture Models?