RRepoGEO

REPOGEO REPORT · LITE

sbrugman/deep-learning-papers

Default branch master · commit 358e2372 · scanned 5/16/2026, 1:48:11 AM

GitHub: 3,187 stars · 407 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 sbrugman/deep-learning-papers, 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 H1 to emphasize 'curated collection'

    Why:

    CURRENT
    # Deep Learning Papers by task
    COPY-PASTE FIX
    # A Curated Collection of Deep Learning Papers by Task
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the text of a standard open-source license like MIT or Apache-2.0, or clarify the licensing terms directly in the README.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a relevant URL (e.g., a GitHub Pages link for the repo, or a related project page) to the 'Homepage' field in the repository's 'About' section.

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 sbrugman/deep-learning-papers
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Papers With Code
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Papers With Code · recommended 1×
  2. arXiv Sanity Preserver · recommended 1×
  3. Distill.pub · recommended 1×
  4. The Batch · recommended 1×
  5. AI Alignment Forum · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of current state-of-the-art deep learning research papers?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. arXiv Sanity Preserver
    3. Distill.pub
    4. The Batch
    5. AI Alignment Forum
    6. LessWrong
    7. Twitter

    AI recommended 7 alternatives but never named sbrugman/deep-learning-papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best deep learning papers categorized by specific tasks like text or visual processing?
    you: not recommended
    AI recommended (in order):
    1. AlexNet
    2. VGGNet
    3. ResNet
    4. Inception (GoogLeNet)
    5. EfficientNet
    6. R-CNN
    7. Faster R-CNN
    8. YOLO (You Only Look Once)
    9. SSD (Single Shot MultiBox Detector)
    10. DETR
    11. FCN (Fully Convolutional Networks)
    12. U-Net
    13. DeepLab (v3+)
    14. Mask R-CNN
    15. GANs (Generative Adversarial Networks)
    16. DCGAN (Deep Convolutional GANs)
    17. StyleGAN
    18. DDPM (Denoising Diffusion Probabilistic Models)
    19. Word2Vec
    20. GloVe
    21. LSTM (Long Short-Term Memory)
    22. GRU (Gated Recurrent Unit)
    23. Attention Is All You Need (Transformer)
    24. BERT (Bidirectional Encoder Representations from Transformers)
    25. GPT (Generative Pre-trained Transformer)
    26. T5 (Text-to-Text Transfer Transformer)
    27. RoBERTa
    28. Neural Machine Translation by Jointly Learning to Align and Translate
    29. DQN (Deep Q-Network)
    30. AlphaGo
    31. A3C (Asynchronous Advantage Actor-Critic)
    32. PPO (Proximal Policy Optimization Algorithms)
    33. SAC (Soft Actor-Critic)
    34. Dropout
    35. Batch Normalization
    36. Adam (Adaptive Moment Estimation)

    AI recommended 36 alternatives but never named sbrugman/deep-learning-papers. 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 sbrugman/deep-learning-papers?
    pass
    AI named sbrugman/deep-learning-papers explicitly

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

  • If a team adopts sbrugman/deep-learning-papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named sbrugman/deep-learning-papers 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 sbrugman/deep-learning-papers solve, and who is the primary audience?
    pass
    AI did not name sbrugman/deep-learning-papers — 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?

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  • Brand-free category queries5 vs 2 in Lite
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