RRepoGEO

REPOGEO REPORT · LITE

openai/iaf

Default branch master · commit ad33fe48 · scanned 6/7/2026, 5:18:03 AM

GitHub: 531 stars · 137 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 openai/iaf, 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

2 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 paragraph to clarify project identity

    Why:

    CURRENT
    Code for reproducing key results in the paper Improving Variational Inference with Inverse Autoregressive Flow by Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling.
    COPY-PASTE FIX
    This repository provides the **Python implementation** for reproducing key results from the paper 'Improving Variational Inference with Inverse Autoregressive Flow'. It showcases the use of **Inverse Autoregressive Flow (IAF)** to enhance **variational inference** algorithms, particularly for deep generative models.
  • mediumabout#2
    Enhance the repository description for clarity

    Why:

    CURRENT
    Code for reproducing key results in the paper "Improving Variational Inference with Inverse Autoregressive Flow"
    COPY-PASTE FIX
    Python implementation for reproducing key results from the paper 'Improving Variational Inference with Inverse Autoregressive Flow', demonstrating Inverse Autoregressive Flow (IAF) for enhanced variational inference in deep learning.

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 openai/iaf
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
TensorFlow Probability
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. TensorFlow Probability · recommended 1×
  2. PyTorch · recommended 1×
  3. Pyro · recommended 1×
  4. NumPyro · recommended 1×
  5. Stan · recommended 1×
  • CATEGORY QUERY
    How can I improve the performance and expressiveness of variational inference algorithms?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Probability
    2. PyTorch
    3. Pyro
    4. NumPyro
    5. Stan
    6. nflows
    7. Adversarial Variational Bayes (AVB)
    8. Variational Autoencoders (VAEs)
    9. Importance Weighted Autoencoders (IWAE)
    10. TensorFlow
    11. Gumbel-Softmax
    12. Straight-Through Estimator

    AI recommended 12 alternatives but never named openai/iaf. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Python implementations are available for inverse autoregressive flow in deep learning?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Probability (tensorflow/probability)
    2. PyTorch-Flows (bayesiains/pytorch-flows)
    3. nflows (bayesiains/nflows)
    4. Pyro (pyro-ppl/pyro)
    5. TensorFlow Addons (tensorflow/addons)

    AI recommended 5 alternatives but never named openai/iaf. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 openai/iaf?
    pass
    AI named openai/iaf explicitly

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

  • If a team adopts openai/iaf in production, what risks or prerequisites should they evaluate first?
    pass
    AI named openai/iaf 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 openai/iaf solve, and who is the primary audience?
    pass
    AI named openai/iaf explicitly

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

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openai/iaf — 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