Samples, Sources, Space: Decomposing Data Scale in Spatially Structured Representation Learning of Human Brain Microarchitecture

arXiv:2609.31201 · cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.AI

Submitted: 2026-09-25

Updated: 2026-09-25

Terminology

Generalization — 10× in this paper · explained in 13 episodes
Foundation Model — 9× in this paper · explained in 3 episodes
Scaling Laws — 6× in this paper · explained in 4 episodes
Adam — 5× in this paper · explained in 2 episodes
F1 Score — 5× in this paper · explained in 3 episodes
Linear probe — 5× in this paper · explained in 2 episodes
Representation Learning — 5× in this paper · explained in 2 episodes
Transformer — 4× in this paper · explained in 2 episodes
Foundation Models — 3× in this paper · explained in 7 episodes
Benchmark — 2× in this paper · explained in 2 episodes
Pruning — 2× in this paper · explained in 4 episodes
Activation Function — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Contrastive Learning — 1× in this paper · explained in 5 episodes
Data Pruning — 1× in this paper · explained in 2 episodes
Embeddings — 1× in this paper · explained in 2 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Generative Modeling — 1× in this paper · explained in 3 episodes
Linear Probing — 1× in this paper · explained in 2 episodes
Provenance — 1× in this paper · explained in 3 episodes
Regularization — 1× in this paper · explained in 3 episodes

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