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Comparison

ml-surveys vs awesome-automl-papers

Verdict

Pick ml-surveys if ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems; pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.

Markdown twin · ml-surveys alternatives · awesome-automl-papers alternatives

GraphCanon updated 2w

ml-surveys logo

ml-surveys

eugeneyan/ml-surveys

★ 2.9kpushed Mar 17, 2023
vs
awesome-automl-papers logo

awesome-automl-papers

hibayesian/awesome-automl-papers

★ 4.2kpushed Jun 11, 2024

Trust & integrity

Signalml-surveysawesome-automl-papers
Maintenance
Dormant (1223d since push)
As of 4w · github_public_v1
Dormant (784d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Personal account
As of 4w · github_public_v1
Not a fork · Personal account
As of 2w · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
ml-surveys trust reportawesome-automl-papers trust reportTrust methodology

Tagline

ml-surveys
Survey papers summarizing advances in various AI domains
awesome-automl-papers
A curated list of automated machine learning papers and resources.

Stars

ml-surveys
2.9k
awesome-automl-papers
4.2k

Forks

ml-surveys
291
awesome-automl-papers
678

Open issues

ml-surveys
2
awesome-automl-papers
2

Language

ml-surveys
-
awesome-automl-papers
-

Adopt for

ml-surveys
ml-surveys is a collection of detailed review papers summarizing advancements in various AI domains such as deep learning, NLP, CV, graphs, reinforcement learning, and recommendation systems.
awesome-automl-papers
awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.

Persona

ml-surveys
-
awesome-automl-papers
-

Runtime

ml-surveys
-
awesome-automl-papers
-

License

ml-surveys
MIT
awesome-automl-papers
Apache-2.0

Last pushed

ml-surveys
Mar 17, 2023
awesome-automl-papers
Jun 11, 2024

Categories

ml-surveys
Computer Vision, Evaluation & Observability, Model Training
awesome-automl-papers
Evaluation & Observability, Model Training

Trust and health

Days since push

ml-surveys
1223d
awesome-automl-papers
784d

Full report

ml-surveys
Trust report
awesome-automl-papers
Trust report

Choose ml-surveys if…

  • License: ml-surveys is MIT, awesome-automl-papers is Apache-2.0.
  • Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning.
  • Also covers Computer Vision.
  • When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning

When NOT to use ml-surveys

  • If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary
  • In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
GitHubDetails

Choose awesome-automl-papers if…

  • License: awesome-automl-papers is Apache-2.0, ml-surveys is MIT.
  • Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search.
  • When you need a curated list of academic materials to research or learn about AutoML technologies

When NOT to use awesome-automl-papers

  • If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
  • When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
GitHubDetails

Explore

ml-surveys trust report →awesome-automl-papers trust report →Computer Vision category →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: ml-surveys 2.9k · awesome-automl-papers 4.2k (synced Jul 22, 2026).

Common questions

What is the difference between ml-surveys and awesome-automl-papers?
ml-surveys: Survey papers summarizing advances in various AI domains. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories.
When should I choose ml-surveys over awesome-automl-papers?
Choose ml-surveys over awesome-automl-papers when License: ml-surveys is MIT, awesome-automl-papers is Apache-2.0; Tags unique to ml-surveys: computer-vision, deep-learning, embeddings, machine-learning; Also covers Computer Vision; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning.
When should I choose awesome-automl-papers over ml-surveys?
Choose awesome-automl-papers over ml-surveys when License: awesome-automl-papers is Apache-2.0, ml-surveys is MIT; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies.
When should I avoid ml-surveys?
If you are seeking detailed technical details, original experiments, or specific algorithm implementations as ml-surveys focuses more on synthesis and summary In cases where deep-dive analysis is required into a single niche topic, as ml-surveys provides broad overviews rather than in-depth coverage of individual niches
When should I avoid awesome-automl-papers?
If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
Is ml-surveys or awesome-automl-papers more popular on GitHub?
awesome-automl-papers has more GitHub stars (4,155 vs 2,902). Stars measure visibility, not whether either tool fits your constraints.
Are ml-surveys and awesome-automl-papers open source?
Yes - both are open-source projects on GitHub (ml-surveys: MIT, awesome-automl-papers: Apache-2.0).
Where can I find alternatives to ml-surveys or awesome-automl-papers?
GraphCanon lists graph-backed alternatives at ml-surveys alternatives and awesome-automl-papers alternatives (ml-surveys markdown twin, awesome-automl-papers markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, ml-surveys or awesome-automl-papers?
ml-surveys: Dormant. awesome-automl-papers: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for ml-surveys and awesome-automl-papers?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; awesome-automl-papers trust report.

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