4 citations · 7 across the 2 of their papers we have counts for
4 papers
Evaluating deep transfer learning for whole-brain cognitive decoding
Armin W. Thomas, Ulman Lindenberger, Wojciech Samek +1
Research in many fields has shown that transfer learning (TL) is well-suited to improve the performance of deep learning (DL) models in datasets with small numbers of samples. This…
Challenges for cognitive decoding using deep learning methods
Armin W. Thomas, Christopher Ré, Russell A. Poldrack
In cognitive decoding, researchers aim to characterize a brain region's representations by identifying the cognitive states (e.g., accepting/rejecting a gamble) that can be identif…
Deep Transfer Learning For Whole-Brain fMRI Analyses
Armin W. Thomas, Klaus-Robert Müller, Wojciech Samek
The application of deep learning (DL) models to the decoding of cognitive states from whole-brain functional Magnetic Resonance Imaging (fMRI) data is often hindered by the small s…
Analyzing Neuroimaging Data Through Recurrent Deep Learning Models
Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller +1
The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size and complex temporo-spatial dependency s…