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Found 7 papers, 5 papers with code
Learning to Defer to a Population: A Meta-Learning Approach
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert.
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles
We study the statistical properties of learning to defer (L2D) to multiple experts.
Calibrated Learning to Defer with One-vs-All Classifiers
We find that Mozannar & Sontag's (2020) multiclass framework is not calibrated with respect to expert correctness.
[Re] Explaining Groups of Points in Low-Dimensional Representations
We reproduce the original experiments using their source code.
DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions
However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration.
Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence
Instead, we propose a method for identifying lesion changes of high certainty, and establish on a dataset of longitudinal multiple sclerosis cases that this method is able to separate progressive from stable timepoints with a very high level of discrimination (AUC = 0. 99), while changes in lesion volume are much less able to perform this separation (AUC = 0. 71).
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
We trained two state-of-the-art fully convolutional CNN architectures on the 2016 MSSEG training dataset, which was annotated by seven independent human raters: a reference implementation of a 3D Unet, and a more recently proposed 3D-to-2D architecture (DeepSCAN).
