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Found 3 papers, 1 papers with code
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
We investigate ways to trade off surrogate loss dimension, the number of problem instances, and restricting the region of consistency in the simplex for multiclass classification.
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github. com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning.
Proper losses for discrete generative models
The characterization rules out a loss whose expectation is the cross-entropy between the target distribution and the model.
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