REPOGEO REPORT · LITE

janosh/awesome-normalizing-flows

Default branch main · commit 4c31bbbf · scanned 5/13/2026, 11:02:57 AM

GitHub: 1,622 stars · 131 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 janosh/awesome-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

Emphasize the "discovery portal" aspect in the README opening

Why:

CURRENT

A list of awesome resources for understanding and applying normalizing flows (NF): a relatively simple yet powerful new tool in statistics for constructing expressive probability distributions from simple base distributions using a chain (flow) of trainable smooth bijective transformations (diffeomorphisms).

COPY-PASTE FIX

A curated list of awesome resources for understanding, applying, and *discovering implementations* of normalizing flows (NF): a powerful tool for constructing expressive probability distributions. This list helps you navigate the ecosystem of papers, applications, videos, and packages.

mediumreadme#2

Add a "Comparison" section to the README

Why:

COPY-PASTE FIX

## 🤔 How is this different from a library or framework?

This repository is a curated "awesome list" designed to help you discover and navigate the ecosystem of Normalizing Flows. It is not a software library or framework itself. Instead, it points to various implementations (e.g., PyTorch, TensorFlow, JAX packages), research papers, tutorials, and applications. If you're looking to *implement* Normalizing Flows, you'll find links to the tools you need here; if you're looking for a direct implementation, please refer to the 'Packages' section.

mediumhomepage#3
Add the repository URL as the homepage
Why:
COPY-PASTE FIX
```
https://github.com/janosh/awesome-normalizing-flows
```

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 janosh/awesome-normalizing-flows

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

PyTorch Distributions

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

PyTorch Distributions · recommended 1×
tensorflow/probability · recommended 1×
stan-dev/stan · recommended 1×
pyro-ppl/pyro · recommended 1×
google/jax · recommended 1×

CATEGORY QUERY
How to construct expressive probability distributions from simple base distributions?
you: not recommended
AI recommended (in order):
1. PyTorch Distributions
2. TensorFlow Probability (tensorflow/probability)
3. Stan (stan-dev/stan)
4. Pyro (pyro-ppl/pyro)
5. JAX (google/jax)
6. Distrax (deepmind/distrax)
7. BlackJAX (blackjax-devs/blackjax)
8. SciPy.stats
9. Greta (greta-dev/greta)
AI recommended 9 alternatives but never named janosh/awesome-normalizing-flows. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking advanced methods for density estimation in machine learning models.
you: not recommended
AI recommended (in order):
1. PyTorch
2. TensorFlow Probability
3. TensorFlow
4. scikit-learn
5. statsmodels
6. Keras
7. PixelCNN
8. WaveNet
AI recommended 8 alternatives but never named janosh/awesome-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 janosh/awesome-normalizing-flows?
pass
AI did not name janosh/awesome-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?
If a team adopts janosh/awesome-normalizing-flows in production, what risks or prerequisites should they evaluate first?
pass
AI named janosh/awesome-normalizing-flows 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 janosh/awesome-normalizing-flows solve, and who is the primary audience?
pass
AI named janosh/awesome-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 janosh/awesome-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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janosh/awesome-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