Neural Net (Pruned 90%)

algorithmic-bytes · 37 views
algorithmic-byteshigh-entropy

What It Is

Pruned neural network weights (90% zero) --- extreme sparsity typical of compressed models. Zeros encode as byte 0; non-zero weight MAGNITUDES scaled to [1, 255] (sign not retained)

Interpretation

Standard analysis sees: heavy-tailed; right-skewed; few distinct values; aperiodic / broadband; multifractal; low-dimensional. The atlas finds no named structure, but the source is distinctively extreme on Hodge–Laplacian:source_fraction (-2.4z) — beyond what the standard bank predicts for it.

What standard analysis sees
tail heaviness0.93
asymmetry0.97
occupancy0.09
short-range corr0.26
long-range memory0.46
spectral colour0.73
periodicity0.00
complexity0.15
time-irreversibility0.58
volatility clustering0.18
multifractality0.96
dimensionality0.13
nonstationarity0.56
What the atlas adds
Atlas-extreme metrics the standard bank can’t predict for this source
Hodge–Laplacian:source_fraction-2.4zbank-miss 1.8σ
Möbius-S³:spinorial_asymmetry+2.0zbank-miss 2.1σ

Composition

dtypeuint8
range[0, 255]
unique values192 / 16384
mean ± std5.7 ± 22.1

Render Gallery

Atlas Position

Nearest neighborDistance
Poker Hands4.05cross-origin
Rainfall (ORD Hourly)4.19cross-origin
Sensor Event Stream4.19cross-origin

Open in Atlas →

Which Geometries Light Up

2-adic › 2-adic:mean_distancerank 302/3060.1145
Catch24 › Catch24:SB_TransitionMatrix_3ac_sumdiagcovrank 5/3060.1991
E8 Lattice › E8 Lattice:std_profilerank 2/3068.4853
Julia Set › Julia Set:escape_entropyrank 4/3064.4550
Moiré › Moiré:moire_invariance_breadthrank 302/3060.0032
in algorithmic-bytes
alphabetical
← / → within domain · ⇧← / ⇧→ alphabetical · ⇧← / ⇧→ inside an open render = same view across sources