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Found 3 papers, 2 papers with code
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks.
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features.
Probabilistically-autoencoded horseshoe-disentangled multidomain item-response theory models
By binding the generative IRT model to a Bayesian neural network (forming a probabilistic autoencoder), one obtains a scoring algorithm consistent with the interpretable Bayesian model.
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