ThatAIguyA world of ideas

THE CONSCIOUSNESS ATLAS
Read. Experiment. Follow the evidence.

ConsciousnessLearn · Explore · Joseph Anady

Centered moving average of gradient norms

The average uses a symmetric window of w observations around time t. For odd w, there are (w−1)/2 samples on each side and one center sample. Because future samples enter the window, the smoothed series is a retrospective descriptive summary rather than an immediately…

Research mathematicsdocumented formula
Learn with the Atlas

Understand the update

Build intuition for derivatives, direction, and learning steps.

Open guided lesson
eq-18a7e538025bcemathematical expression
g‾t(w)=1w∑j=−(w−1)/2(w−1)/2gt+j.{\overline{g}}_{t}^{(w)}=\frac{1}{w}\sum _{j=-(w-1)/2}^{(w-1)/2}{{g}_{t+j}}.

Read the equation

The average uses a symmetric window of w observations around time t. For odd w, there are (w−1)/2 samples on each side and one center sample. Because future samples enter the window, the smoothed series is a retrospective descriptive summary rather than an immediately available online detector.

Terms & definitions

TermMeaning in this source
g_t

Recorded gradient norm

w

Odd window length

g_bar

Smoothed gradient norm

How it works

Read the definition from its right-hand expression to the quantity on the left. Its surrounding source records the intended input, state, or parameter context. Where the source leaves an operation unspecified, this page preserves that gap.

Assumptions & limits

  • End-of-record handling must specify whether incomplete windows are omitted or treated differently.

Original context

Read the surrounding source passage
4.4. Training log summaries

Let gt denote the recorded gradient norm. I report its raw maximum and centered averages over complete windows of five, 11, 21, and 31 steps. For odd window width w,

gt(w)=1wj=−(w−1)/2(w−1)/2gt+j.

(4.8)

Centered smoothing uses future observations relative to its labeled center. It is a descriptive robustness check, not a real time declaration detector. A gradient norm is also not the size of the actual optimizer update, accumulated information, or electrical energy.

To preserve the earlier pilot comparison, I fit two independent lines to the first 300 observations. Each segment must contain at least 20 observations. For 20≤k≤280, the objective is
Exact original notation
{\overline{g}}_{t}^{(w)}=\frac{1}{w}\sum _{j=-(w-1)/2}^{(w-1)/2}{{g}_{t+j}}.

Related builds

Sources & provenance

01
JAIC_Manuscript_Joseph_Anady_FINAL.docx
paragraph 132, math 1 · Sep 22, 2026
{\overline{g}}_{t}^{(w)}=\frac{1}{w}\sum _{j=-(w-1)/2}^{(w-1)/2}{{g}_{t+j}}.
02
JAIC_Manuscript_Joseph_Anady_Revised.docx
paragraph 132, math 1 · Sep 22, 2026
{\overline{g}}_{t}^{(w)}=\frac{1}{w}\sum _{j=-(w-1)/2}^{(w-1)/2}{{g}_{t+j}}.
03
JAIC_Manuscript_Joseph_Anady_FINAL.docx
paragraph 132, math 1 · Sep 22, 2026
{\overline{g}}_{t}^{(w)}=\frac{1}{w}\sum _{j=-(w-1)/2}^{(w-1)/2}{{g}_{t+j}}.
04
JAIC_Manuscript_Joseph_Anady_Revised.docx
paragraph 132, math 1 · Sep 22, 2026
{\overline{g}}_{t}^{(w)}=\frac{1}{w}\sum _{j=-(w-1)/2}^{(w-1)/2}{{g}_{t+j}}.

Search equations, builds, manuscript text, and recovered sources.