5 papers · 1 filter
Representational and Functional Robustness to Electrode Montages in EEG Foundation Models
Jakob Steglich, Justus Meyer zu Bexten, Shakiba Moradi +3
EEG foundation models (EEG-FMs) are intended to generalize across different datasets by learning representations that, ideally, are invariant to dataset-specific EEG configurations…
Attention Trajectories as a Diagnostic Axis for Deep Reinforcement Learning
Charlotte Beylier, Hannah Selder, Arthur Fleig +2
The emergence and evolution of feature reliance in deep reinforcement learning agents remain poorly understood. Here, we introduce a methodological framework for analyzing the lear…
Geometry matters: insights from Ollivier Ricci Curvature and Ricci Flow into representational alignment through Ollivier-Ricci Curvature and Ricci Flow
Nahid Torbati, Michael Gaebler, Simon M. Hofmann +1
Representational similarity analysis (RSA) is widely used to analyze the alignment between humans and neural networks; however, conclusions based on this approach can be misleading…
Revealing the Learning Process in Reinforcement Learning Agents Through Attention-Oriented Metrics
Charlotte Beylier, Simon M. Hofmann, Nico Scherf
The learning process of a reinforcement learning (RL) agent remains poorly understood beyond the mathematical formulation of its learning algorithm. To address this gap, we introdu…
EEG-Features for Generalized Deepfake Detection
Arian Beckmann, Tilman Stephani, Felix Klotzsche +8
Since the advent of Deepfakes in digital media, the development of robust and reliable detection mechanism is urgently called for. In this study, we explore a novel approach to Dee…