RRepoGEO

REPOGEO REPORT · LITE

jjbrophy47/machine_unlearning

Default branch master · commit bc22a9ee · scanned 6/12/2026, 3:28:27 PM

GitHub: 968 stars · 118 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface jjbrophy47/machine_unlearning, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clarify its role as a curated resource

    Why:

    CURRENT
    # Machine Unlearning Papers and Benchmarks
    COPY-PASTE FIX
    # Awesome Machine Unlearning Papers and Benchmarks

This repository is a comprehensive, curated collection of research papers, frameworks, and benchmarks related to machine unlearning.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Create a LICENSE file in the repository root, choosing an appropriate open-source license like MIT or Apache-2.0.)
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    (Go to repository settings, then under 'About', add a relevant URL to the 'Homepage' field, e.g., the GitHub repository URL itself or a dedicated project page.)

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface jjbrophy47/machine_unlearning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Machine Unlearning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Machine Unlearning · recommended 1×
  2. NeurIPS · recommended 1×
  3. ICML · recommended 1×
  4. ICLR · recommended 1×
  5. AAAI · recommended 1×
  • CATEGORY QUERY
    I need to understand the current state of research in machine unlearning; any good resources?
    you: not recommended
    AI recommended (in order):
    1. Awesome Machine Unlearning
    2. NeurIPS
    3. ICML
    4. ICLR
    5. AAAI

    AI recommended 5 alternatives but never named jjbrophy47/machine_unlearning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How do different machine unlearning algorithms compare in terms of effectiveness and efficiency?
    you: not recommended
    AI recommended (in order):
    1. SISA (Sharded, Isolated, Sliced, and Aggregated) Training
    2. Retraining from Scratch
    3. Unlearning by Forgetting
    4. Certified Removal
    5. Influence Function-based Unlearning
    6. Approximate Data Deletion
    7. AMNESIA
    8. Data Pruning/Filtering

    AI recommended 8 alternatives but never named jjbrophy47/machine_unlearning. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of jjbrophy47/machine_unlearning?
    pass
    AI named jjbrophy47/machine_unlearning explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts jjbrophy47/machine_unlearning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jjbrophy47/machine_unlearning explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo jjbrophy47/machine_unlearning solve, and who is the primary audience?
    pass
    AI did not name jjbrophy47/machine_unlearning — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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jjbrophy47/machine_unlearning — RepoGEO report