134 citations · 267 across the 10 of their papers we have counts for
3 papers · 1 filter
Investigating Labeler Bias in Face Annotation for Machine Learning
Luke Haliburton, Sinksar Ghebremedhin, Robin Welsch +2
In a world increasingly reliant on artificial intelligence, it is more important than ever to consider the ethical implications of artificial intelligence on humanity. One key unde…
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whet…
Domain-Invariant Representation Learning from EEG with Private Encoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This beco…