Neural Net (Dense)

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What It Is

Synthetic trained dense-layer weights --- Xavier init plus 50 momentum-smoothed gradient updates, L1-like shrinkage of small weights, and heavy-tailed outliers. An *untrained* layer is i.i.d. Gaussian (indistinguishable from noise); training imprints local correlation.

Interpretation

Standard analysis sees: aperiodic / broadband; high-complexity (noise-like). The atlas detects no named structure beyond this.

What standard analysis sees
tail heaviness0.76
asymmetry0.28
occupancy0.41
short-range corr0.31
long-range memory0.31
spectral colour0.75
periodicity0.06
complexity0.87
time-irreversibility0.44
volatility clustering0.16
multifractality0.31
dimensionality0.80
nonstationarity0.31
What the atlas adds

Nothing beyond the standard reading — this source’s structure is already captured by standard features; the atlas adds no named residual.

Composition

dtypeuint8
range[0, 255]
unique values232 / 16384
mean ± std127 ± 35.7

Render Gallery

Atlas Position

Nearest neighborDistance
Gaussian Noise2.70cross-domain
Entanglement Entropy3.08cross-domain
Poisson Counts3.50cross-domain

Open in Atlas →

Which Geometries Light Up

This source does not rank extreme on any metric.

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