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Found 5 papers, 4 papers with code
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples
To provide such sets, conformal predictors often estimate a cutoff threshold for the probability estimates based on a calibration set.
Regularization-wise double descent: Why it occurs and how to eliminate it
The risk of overparameterized models, in particular deep neural networks, is often double-descent shaped as a function of the model size.
Early Stopping in Deep Networks: Double Descent and How to Eliminate it
Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases at last.
Image recognition from raw labels collected without annotators
Image classification problems are typically addressed by first collecting examples with candidate labels, second cleaning the candidate labels manually, and third training a deep neural network on the clean examples.
Leveraging inductive bias of neural networks for learning without explicit human annotations
Classification problems today are typically solved by first collecting examples along with candidate labels, second obtaining clean labels from workers, and third training a large, overparameterized deep neural network on the clean examples.
