RRepoGEO

REPOGEO REPORT · LITE

kamenbliznashki/normalizing_flows

Default branch master · commit 97a73a01 · scanned 6/15/2026, 5:22:44 PM

GitHub: 640 stars · 102 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 kamenbliznashki/normalizing_flows, 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 emphasize PyTorch implementation resource

    Why:

    CURRENT
    # Normalizing flows

Reimplementations of density estimation algorithms from:
    COPY-PASTE FIX
    # Normalizing Flows in PyTorch

This repository provides clear, modular PyTorch implementations of state-of-the-art density estimation algorithms, including Block Neural Autoregressive Flow (BNAF), Glow, Masked Autoregressive Flow (MAF), RealNVP, and planar flows. It serves as a practical resource for researchers and practitioners looking to understand and apply these modern normalizing flow models for generative tasks.
  • mediumlicense#2
    Add a standard open-source license file

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root with the text of the MIT License.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Set the 'Homepage' field in the repository's 'About' section to `https://github.com/kamenbliznashki/normalizing_flows` or a dedicated project page if one exists.

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 kamenbliznashki/normalizing_flows
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RealNVP
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RealNVP · recommended 1×
  2. Glow · recommended 1×
  3. MAF · recommended 1×
  4. NSF · recommended 1×
  5. VAEs · recommended 1×
  • CATEGORY QUERY
    What are the best deep learning methods for high-dimensional density estimation?
    you: not recommended
    AI recommended (in order):
    1. RealNVP
    2. Glow
    3. MAF
    4. NSF
    5. VAEs
    6. VAE-GAN
    7. PixelCNN
    8. WaveNet
    9. MADE
    10. StyleGAN
    11. BigGAN
    12. Deep Energy Models
    13. DDPM
    14. NCSN

    AI recommended 14 alternatives but never named kamenbliznashki/normalizing_flows. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement modern normalizing flow models for generative tasks in PyTorch?
    you: not recommended
    AI recommended (in order):
    1. nflows
    2. FrEIA
    3. Pyro
    4. PyTorch-GAN

    AI recommended 4 alternatives but never named kamenbliznashki/normalizing_flows. 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 kamenbliznashki/normalizing_flows?
    pass
    AI named kamenbliznashki/normalizing_flows explicitly

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

  • If a team adopts kamenbliznashki/normalizing_flows in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name kamenbliznashki/normalizing_flows — 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?

  • In one sentence, what problem does the repo kamenbliznashki/normalizing_flows solve, and who is the primary audience?
    pass
    AI named kamenbliznashki/normalizing_flows 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 kamenbliznashki/normalizing_flows. 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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kamenbliznashki/normalizing_flows — 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