1 citations · 2 across the 5 of their papers we have counts for
5 papers
Knowledge-guided EEG Representation Learning
Aditya Kommineni, Kleanthis Avramidis, Richard Leahy +1
Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, relevant for the domain of biosig…
Learning A Disentangling Representation For PU Learning
Omar Zamzam, Haleh Akrami, Mahdi Soltanolkotabi +1
In this paper, we address the problem of learning a binary (positive vs. negative) classifier given Positive and Unlabeled data commonly referred to as PU learning. Although rudime…
Beta quantile regression for robust estimation of uncertainty in the presence of outliers
Haleh Akrami, Omar Zamzam, Anand Joshi +2
Quantile Regression (QR) can be used to estimate aleatoric uncertainty in deep neural networks and can generate prediction intervals. Quantifying uncertainty is particularly import…
Learning From Positive and Unlabeled Data Using Observer-GAN
Omar Zamzam, Haleh Akrami, Richard Leahy
The problem of learning from positive and unlabeled data (A.K.A. PU learning) has been studied in a binary (i.e., positive versus negative) classification setting, where the input…
Learning from imperfect training data using a robust loss function: application to brain image segmentation
Haleh Akrami, Wenhui Cui, Anand A Joshi +1
Segmentation is one of the most important tasks in MRI medical image analysis and is often the first and the most critical step in many clinical applications. In brain MRI analysis…