Papers › A Parameter-efficient Multi-subject Model for Predicting fMRI Activity

A Parameter-efficient Multi-subject Model for Predicting fMRI Activity

4 Aug 2023arXiv:2308.02351archive 2025-07-28

Connor Lane, Gregory Kiar

This is the Algonauts 2023 submission report for team "BlobGPT". Our model consists of a multi-subject linear encoding head attached to a pretrained trunk model. The multi-subject head consists of three components: (1) a shared multi-layer feature projection, (2) shared plus subject-specific low-dimension linear transformations, and (3) a shared PCA fMRI embedding. In this report, we explain these components in more detail and present some experimental results. Our code is available at https://github.com/cmi-dair/algonauts23.

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