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Neural learners for classification and regression are often criticized as black boxes. We propose a probabilistic decomposition of neural learners that accounts for heterogeneous predictability across the feature space. A training procedure combining the EM algorithm, stochastic gradient descent, and maximum likelihood...
This work proposes a model-based imputation method that enables sampling from p via a sampling-importance-resampling (SIR) procedure, which can be further augmented with a joint diffusion model in the latent space, and achieves competitive performance compared to state-of-the-art procedures.
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