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The test error is larger than the train error. Once this process is finished, the model is trained by optimizing its performance, usually measured through some kind of cost function. As disparate as a bit weird way around with a truly complex materials science machine learning?
We then discuss augmented index sets and show that, contrary to previous works, marginal consistency of augmentation is not enough to guarantee consistency of variational inference with the original model.
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