3 accepted papers
We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the…
Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has shown to be a surprisingly effective meth…
We describe a graph-based semi-supervised learning method for acoustic data that uses a Deep Neural Network (DNN) combined with a stochastic graph-based entropic regularizer to favor smooth solutions over a graph induced by the data. We consider graph embeddings constructed from the input features a…