RRepoGEO

REPOGEO REPORT · LITE

js05212/BayesianDeepLearning-Survey

Default branch master · commit 183871e5 · scanned 6/5/2026, 7:43:29 AM

GitHub: 520 stars · 62 forks

AI VISIBILITY SCORE
22 /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
1 / 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 js05212/BayesianDeepLearning-Survey, 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
    Clarify README's opening to emphasize 'survey/resource list' nature

    Why:

    CURRENT
    This is an updating survey for Bayesian Deep Learning (BDL), an constantly updated and extended version for the manuscript, 'A Survey on Bayesian Deep Learning', published in **ACM Computing Surveys** 2020.
    COPY-PASTE FIX
    This repository provides an actively updated and extended survey of Bayesian Deep Learning (BDL), serving as a comprehensive resource and curated collection of papers. It is the online companion to 'A Survey on Bayesian Deep Learning', published in **ACM Computing Surveys** 2020.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, specifying the chosen open-source license (e.g., MIT, Apache-2.0, CC-BY-4.0 for content).
  • mediumhomepage#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Add `http://wanghao.in/BDL.html` as the homepage URL in the repository settings.

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 js05212/BayesianDeepLearning-Survey
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pyro
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pyro · recommended 1×
  2. TensorFlow Probability · recommended 1×
  3. Stan · recommended 1×
  4. PyMC · recommended 1×
  5. Google's DeepMind · recommended 1×
  • CATEGORY QUERY
    What are the latest advancements and applications in probabilistic deep learning for various fields?
    you: not recommended
    AI recommended (in order):
    1. Pyro
    2. TensorFlow Probability
    3. Stan
    4. PyMC
    5. Google's DeepMind
    6. Google Health
    7. $eta$-VAE
    8. Conditional VAEs (CVAEs)
    9. Real NVP (NICE)
    10. Glow
    11. DALL-E 2
    12. Stable Diffusion
    13. Google's AudioLM
    14. Probabilistic Policy Optimization (PPO)
    15. Soft Actor-Critic (SAC)
    16. Deep Bayesian Q-Networks (DBQN)
    17. DeepAR (Amazon)
    18. Edward2

    AI recommended 18 alternatives but never named js05212/BayesianDeepLearning-Survey. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a comprehensive overview of uncertainty quantification methods in deep learning models.
    you: not recommended
    AI recommended (in order):
    1. Uncertainty in Deep Learning
    2. Deep Learning with Python
    3. TensorFlow (tensorflow/tensorflow)
    4. Keras (keras-team/keras)
    5. A Survey of Uncertainty in Deep Neural Networks
    6. Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
    7. Google AI
    8. Microsoft Research
    9. Medium
    10. Towards Data Science
    11. PyTorch (pytorch/pytorch)
    12. Probabilistic Machine Learning: An Introduction
    13. Awesome Uncertainty in Deep Learning (yandex-research/awesome-uncertainty-in-deep-learning)

    AI recommended 13 alternatives but never named js05212/BayesianDeepLearning-Survey. 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 js05212/BayesianDeepLearning-Survey?
    pass
    AI did not name js05212/BayesianDeepLearning-Survey — 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 js05212/BayesianDeepLearning-Survey in production, what risks or prerequisites should they evaluate first?
    pass
    AI named js05212/BayesianDeepLearning-Survey 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 js05212/BayesianDeepLearning-Survey solve, and who is the primary audience?
    pass
    AI did not name js05212/BayesianDeepLearning-Survey — 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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js05212/BayesianDeepLearning-Survey — 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