Natural Image Autoencoder-Based fMRI Representations for Trait and State Prediction

arXiv:2609.34167 · cs.CV · Submitted 2026-09-28 · Read on arXiv

cs.CV

Submitted: 2026-09-28

Updated: 2026-09-29

Terminology

• Foundation Model — 49× in this paper · explained in 3 episodes
• Foundation Models — 35× in this paper · explained in 7 episodes
• AUC — 26× in this paper · explained in 2 episodes
• Transformer — 25× in this paper · explained in 2 episodes
• Fine-Tuning — 19× in this paper · explained in 5 episodes
• Linear Probing — 19× in this paper · explained in 2 episodes
• Accuracy — 10× in this paper · explained in 1 episode
• Aggregation — 9× in this paper · explained in 1 episode
• Downstream Tasks — 8× in this paper · explained in 2 episodes
• Late Fusion — 8× in this paper · explained in 2 episodes
• Regularization — 6× in this paper · explained in 3 episodes
• Tokenization — 6× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• Linear probe — 4× in this paper · explained in 2 episodes
• AUROC — 3× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Adaptation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• International Conference on Machine Learning — 3× in this paper
• Latent Space — 3× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Transformer Models — 2× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• Gemma 2: Improving Open Language Models at a Practical Size
• GLU Variants Improve Transformer
• Brain-DiT: A Universal Multi-state fMRI Foundation Model with Metadata-Conditioned Pretraining

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