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[D] Training on realizations, testing on posteriors?

I have a supervised, discriminative problem. The model is a feedforward NN. I’m trying to learn to predict p(Y = y|Z) where Z ~ P(Z | X). At training time I have get a sample Z ~ p(Z | X) but at test time I have the full posterior p(Z | X). Concretely, Z is represented by a 100 dimensional latent vector; at training I only have a 1-hot encoding but at test time I have a distribution. I could, of course, just take the argmax at test time but that throws away the full posterior. Or perhaps I should inject noise at train-time to the one-hot encoding. Is there any literature on this problem?

submitted by /u/the_roboticist
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.