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Found 3 papers, 1 papers with code
Revisiting Explicit Regularization in Neural Networks for Reliable Predictive Probability
In this work, we revisit the role and importance of explicit regularization methods for generalization of the predictive probability, not just the generalization of the 0-1 loss.
Revisiting Explicit Regularization in Neural Networks for Well-Calibrated Predictive Uncertainty
From the statistical learning perspective, complexity control via explicit regularization is a necessity for improving the generalization of over-parameterized models.
Being Bayesian about Categorical Probability
Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability.
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