6 citations · 12 across the 4 of their papers we have counts for
6 papers
Machine Learning for Health symposium 2022 -- Extended Abstract track
Antonio Parziale, Monica Agrawal, Shalmali Joshi +4
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28,…
More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data
Saul Calderon-Ramirez, Luis Oala
A common heuristic in semi-supervised deep learning (SSDL) is to select unlabelled data based on a notion of semantic similarity to the labelled data. For example, labelled images…
Post-Hoc Domain Adaptation via Guided Data Homogenization
Kurt Willis, Luis Oala
Addressing shifts in data distributions is an important prerequisite for the deployment of deep learning models to real-world settings. A general approach to this problem involves…
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
Saul Calderon-Ramirez, Luis Oala, Jordina Torrents-Barrena +4
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch. This work is divid…
Interval Neural Networks as Instability Detectors for Image Reconstructions
Jan Macdonald, Maximilian März, Luis Oala +1
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks. Although neural networks often empirically…
Interval Neural Networks: Uncertainty Scores
Luis Oala, Cosmas Heiß, Jan Macdonald +3
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network. T…