What If the FFN Can Only Learn on the Diagonal?

← Back to the full catalog

Objective. A 4-part series (Experiments 95-98) restricting the ViT-100K FFN’s fc1/fc2 weights to a diagonal, a swept diagonal band, a fixed diagonal subset, and IIR-initialized bands — asking how much of the FFN’s dense connectivity FAct’s advantage actually needs.

   
GPU(s) not recorded individually (FMNIST, single-seed scale, shared GPU fleet)
Dataset(s) FashionMNIST
Model ViT-100K, FAct K=2 (global) vs. GELU, restricted-connectivity FFN

What If the FFN Can Only Learn on the Diagonal? — result chart

Result summary

  • An apparent “step” in accuracy between band-width 2 and band-width 4 (seen in an early pass) was later retracted: it was a seed-0 artifact, not a real effect.
  • Once measured properly, the margin’s standard deviation across this series is 3x the per-arm standard deviation — the noise between arms dwarfs the noise within any one arm.

Insights

  • The retraction is the finding: single-seed margins in a series like this are actively misleading — a visually clean step-function result evaporated once seed variance was accounted for. A useful caution against reading structure into a one-seed sweep, including some of the earlier (frozen) results in this same series.

← Back to the full catalog