4 papers
OOD Detection for EEG-based Machine Learning in High-Risk Environments
Philipp Bomatter, Henry Gouk
Machine learning models for electroencephalography (EEG) analysis show great promise across a wide range of applications, but their deployment in high-risk domains is hindered by t…
The Signal in the Noise: OOD Detection Through Goodness-of-Fit Testing in Factorised Latent Spaces
Philipp Bomatter, Jack Geary, Henry Gouk
Deep generative models offer a natural foundation for out-of-distribution (OOD) detection, yet prior work has shown that their assigned likelihoods are notoriously unreliable indic…
Is Limited Participant Diversity Impeding EEG-based Machine Learning?
Philipp Bomatter, Henry Gouk
The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical applications. However, the genera…
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes
Philipp Bomatter, Mengmi Zhang, Dimitar Karev +3
Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporat…