RRepoGEO

REPOGEO REPORT · LITE

janosh/awesome-normalizing-flows

Default branch main · commit 5c165c3b · scanned 6/23/2026, 10:06:53 PM

GitHub: 1,631 stars · 131 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 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
    Reposition the README's opening to clarify its nature as a resource list

    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
    This is a curated list of awesome resources for understanding and applying normalizing flows (NF). It serves as a comprehensive guide for researchers, students, and practitioners exploring this powerful technique in statistics and machine learning, distinct from libraries or frameworks for direct implementation.
  • highhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://github.com/janosh/awesome-normalizing-flows
  • mediumreadme#3
    Add a 'Who is this for?' section to the README

    Why:

    COPY-PASTE FIX
    ## Who is this for?

This list is ideal for machine learning researchers, students, and practitioners who want to:
- Understand the theoretical foundations of normalizing flows.
- Explore various applications across different domains.
- Discover relevant papers, videos, and open-source packages.
- Stay updated with the latest developments in the field.

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
0%
Of all named tools, what % are you?
Top rival
pyro-ppl/pyro
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pyro-ppl/pyro · recommended 2×
  2. tensorflow/probability · recommended 2×
  3. pyg-team/pytorch_geometric · recommended 1×
  4. bayesiains/nflows · recommended 1×
  5. VLL-HD/FrEIA · recommended 1×
  • CATEGORY QUERY
    How can I construct expressive probability distributions using invertible transformations for generative models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    2. Pyro (pyro-ppl/pyro)
    3. TensorFlow Probability (TFP) (tensorflow/probability)
    4. nflows (bayesiains/nflows)
    5. FrEIA (Framework for Invertible AI) (VLL-HD/FrEIA)
    6. JAX (google/jax)
    7. Equinox (patrick-kidger/equinox)
    8. Flax (google/flax)
    9. Haiku (deepmind/dm-haiku)

    AI recommended 9 alternatives but never named janosh/awesome-normalizing-flows. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources for density estimation and variational inference with deep learning?
    you: not recommended
    AI recommended (in order):
    1. Pyro (pyro-ppl/pyro)
    2. TensorFlow Probability (TFP) (tensorflow/probability)
    3. Edward2
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. Keras (keras-team/keras)

    AI recommended 6 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 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?

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.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/janosh/awesome-normalizing-flows.svg)](https://repogeo.com/en/r/janosh/awesome-normalizing-flows)
HTML
<a href="https://repogeo.com/en/r/janosh/awesome-normalizing-flows"><img src="https://repogeo.com/badge/janosh/awesome-normalizing-flows.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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