4 papers · 1 filter
Latent Prediction Yields Cross-Subject Alignable Representations in Sensory-Evoked fMRI
Serwan Jassim, Victoria Bosch, Daniel Anthes +2
Decoding sensory-evoked functional magnetic resonance imaging (fMRI) data is a central analytical tool in cognitive neuroscience. Here we ask whether self-supervised learning (SSL)…
Brain-language fusion enables interactive neural readout and in-silico experimentation
Victoria Bosch, Daniel Anthes, Adrien Doerig +3
Large language models (LLMs) have revolutionized human-machine interaction, and have been extended by embedding diverse modalities such as images into a shared language space. Yet,…
Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment
Daniel Anthes, Sushrut Thorat, Peter König +1
Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks. This phenomenon is commonly referred to a…
Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
Daniel Anthes, Sushrut Thorat, Peter König +1
Continual learning algorithms strive to acquire new knowledge while preserving prior information. Often, these algorithms emphasise stability and restrict network updates upon lear…