In autoregressive models, why are the best results achieved with cross-entropy?

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1. In autoregressive models, why are the best results achieved with cross-entropy?

In classification problems.
This is due in large part to the ability of categorical distributions and cross-entropy to deal with exotic posteriors, in particular multi-modal. (A multimodal posterior is when conflicting interpretations of x exist)
The main goal is to maintain proper statistics of the activations and derivatives. This is done by forcing the the activation statistics during the forward pass by re-normalizing them. During training batch normalization shifts and rescales according to the mean and variance estimated on the batch. During test, it simply shifts and rescales according to the empirical moments estimated during training.
Two populations are Gaussian and have the same covariance matrix.

In autoregressive models, why are the best results achieved with cross-entropy?

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In autoregressive models, why are the best results achieved with cross-entropy?

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All trademarks, logos and brand names are the property of their respective owners. All company, product and service names used in this website are for identification purposes only. Use of these names, trademarks and brands does not imply endorsement.

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