7 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 1 cited
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples
Fatih Furkan Yilmaz, Reinhard Heckel
Modern image classifiers are very accurate, but the predictions come without uncertainty estimates. Conformal predictors provide uncertainty estimates by computing a set of classes…
cs.LG2022
Regularization-wise double descent: Why it occurs and how to eliminate it
Fatih Furkan Yilmaz, Reinhard Heckel
The risk of overparameterized models, in particular deep neural networks, is often double-descent shaped as a function of the model size. Recently, it was shown that the risk as a…
cs.LG2020★ 7 cited
Early Stopping in Deep Networks: Double Descent and How to Eliminate it
Reinhard Heckel, Fatih Furkan Yilmaz
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…