Unverified50% confidenceFactExact time
The soft cross-entropy loss function is -∑ p_i log q_i, where p_i is the target distribution and q_i is the model output distribution.
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11/2/2026
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UnverifiedThe binary cross-entropy loss formula is -[y·log(p) + (1-y)·log(1-p)], where y is the true label and p is the model's predicted probability for the positive class.74% similarUnverifiedPerplexity is defined as PPL = 2^{H(p,q)}, where H(p,q) is the cross-entropy of the model distribution relative to the true distribution, and no model's perplexity can fall below the value corresponding to the true entropy rate of the language.66% similarUnverifiedCross-entropy loss is the most commonly used loss function in classification tasks, rooted in information theory, and measures the distance between the model's predicted probability distribution and the true distribution.65% similarUnverifiedCovariate shift refers to the inconsistency of the feature distribution P(X) between source and target, while model shift involves the shift of the conditional distribution P(Y|X).64% similarUnverifiedFor a softmax output layer, cross-entropy is a strictly convex function for single-layer models.62% similar
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