1 code implementation • 4 Nov 2022 • Luis Oala, Marco Aversa, Gabriel Nobis, Kurt Willis, Yoan Neuenschwander, Michèle Buck, Christian Matek, Jerome Extermann, Enrico Pomarico, Wojciech Samek, Roderick Murray-Smith, Christoph Clausen, Bruno Sanguinetti
This can lead to speed up and stabilization of classifier training at a margin of up to 20% in validation accuracy.
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
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 investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.
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.