Inthis work, we study Transformer-based models for brain network analysis.

Adepiction 4. The architecture 2. Ourf is

Thesemodels typically assume that all event sequences come from the same data distribution.

Vanilla training canbeconsidered asaspecial case where no perturbation is allowed, i.e., zero adversarial budget. Therefore, we focus on the impact of the adversarial budget size on the loss landscape.

Perception, in theoretical neuroscience, has been modeled as the encoding of external stimuli into internal signals, which are then decoded.

Existing image-text modality alignment in Vision Language Models (VLMs) treats each text token equally in an autoregressive manner.