Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Label noise is frequently observed in real-world large-scale datasets. The noise is
introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most …

The presence of label noise often misleads the training of deep neural networks. Departing
from the recent literature which largely assumes the label noise rate is only determined by …

We introduce a temperature into the exponential function and replace the softmax output
layer of the neural networks by a high-temperature generalization. Similarly, the logarithm in …

Over the past decades, numerous loss functions have been been proposed for a variety of
supervised learning tasks, including regression, classification, ranking, and more generally …

This paper presents the first review of noise models in classification covering both label and
attribute noise. Their study reveals the lack of a unified nomenclature in this field. In order to …

A landmark negative result of Long and Servedio has had a considerable impact on
research and development in boosting algorithms, around the now famous tagline that" …

Abstract This paper studies Fenchel-Young losses, a generic way to construct convex loss
functions from a regularization function. We analyze their properties in depth, showing that …

We present a" learning to learn" approach for discovering white-box classification loss
functions that are robust to label noise in the training data. We parameterise a flexible family …

Classification datasets created from chemical processes can be affected by errors, which
impair the accuracy of the models built. This fact highlights the importance of analyzing the …