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Ji

Ji

Unclaimed

AEO Score: 4/10

Monitoring for AI engine activity

In the Engagemii AEO index

jiha-kim.github.io

About Ji

HOMECATEGORIESSERIESCRASH COURSESTAGSARCHIVESABOUTHomeDeriving Reverse-Time Stochastic Differential Equations (SDEs) Post CancelWhen Equivalent Weights Train DifferentlyWhy coordinate-level optimizers can behave differently on weights that represent the same model, and how quotient-aware updates remove the hidden gauge.

Key Topics

When Equivalent Weights Train Differently
Fast Tight Spectral-Norm Bounds
Autoregression vs Diffusion - Understanding Sampling via Optimal Transport
Fast Polar Decomposition for Muon Optimizer with Rational and Polynomial Iterations
Lion-K CCWD: Corrected Cautious Weight Decay
Transformers as Constrained Optimization

Details

Category: Technology

jiha-kim.github.io

AI Visibility Breakdown

1

Structured Data

4

Content Structure

4

Entity Clarity

5

E-E-A-T Signals

6

Technical AEO

4

AI Discoverability

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Picked for Ji: Tech & Electronics

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Source & Attribution

Scored by Engagemii on May 28, 2026. Methodology: engagemii.com/aeo/methodology

Source URL: https://engagemii.com/aeo/brands/jiha-kim-github-io

Cite this score: Engagemii (2026). "AEO Score for Ji." Retrieved from https://engagemii.com/aeo/brands/jiha-kim-github-io

Licensed under CC BY 4.0. You may reuse this data with attribution: a visible link to engagemii.com.

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