RRepoGEO

REPOGEO REPORT · LITE

thuml/awesome-multi-task-learning

Default branch main · commit 42f90c11 · scanned 6/7/2026, 8:02:45 AM

GitHub: 837 stars · 65 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 thuml/awesome-multi-task-learning, 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
    Add license information to README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., '## License\nThis project is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).'
  • highreadme#2
    Add a sentence to README's opening to highlight unique value

    Why:

    CURRENT
    A curated list of datasets, codebases, and papers on Multi-Task Learning (MTL), from a Machine Learning perspective.
    COPY-PASTE FIX
    This is the most comprehensive and actively maintained curated list of datasets, codebases, and papers on Multi-Task Learning (MTL), from a Machine Learning perspective.
  • mediumabout#3
    Refine 'About' description to emphasize comprehensiveness

    Why:

    CURRENT
    A curated list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.
    COPY-PASTE FIX
    The definitive curated list of DATASETS, CODEBASES, and PAPERS on Multi-Task Learning (MTL), from a Machine Learning perspective.

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 thuml/awesome-multi-task-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome Multi-Task Learning
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome Multi-Task Learning · recommended 1×
  2. Papers With Code · recommended 1×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. Kaggle · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources for multi-task learning research and implementation?
    you: not recommended
    AI recommended (in order):
    1. Awesome Multi-Task Learning
    2. Papers With Code
    3. PyTorch
    4. TensorFlow
    5. Kaggle
    6. Fast.ai
    7. Hugging Face Transformers

    AI recommended 7 alternatives but never named thuml/awesome-multi-task-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective deep learning architectures and optimization strategies for multi-task models?
    you: not recommended
    AI recommended (in order):
    1. MT-DNN
    2. Uber's MTDNN
    3. Google's MMoE
    4. Cross-Stitch Networks
    5. Sluice Networks
    6. Multi-gate Mixture-of-Experts (MMoE)
    7. GradNorm
    8. PCGrad
    9. MGDA-UB
    10. Multi-Task Learning Using Uncertainty to Weigh Losses
    11. GradVac
    12. Reinforcement Learning (RL) based schedulers

    AI recommended 12 alternatives but never named thuml/awesome-multi-task-learning. 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 thuml/awesome-multi-task-learning?
    pass
    AI did not name thuml/awesome-multi-task-learning — 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?

  • If a team adopts thuml/awesome-multi-task-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named thuml/awesome-multi-task-learning 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 thuml/awesome-multi-task-learning solve, and who is the primary audience?
    pass
    AI named thuml/awesome-multi-task-learning explicitly

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

Embed your GEO score

Drop this badge into the README of thuml/awesome-multi-task-learning. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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thuml/awesome-multi-task-learning — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite