Comparison
awesome-automl-papers vs best_AI_papers_2022
Verdict
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; pick best_AI_papers_2022 if best AI Papers from 2022 offers video explanations and code links for selected research papers.
Markdown twin · awesome-automl-papers alternatives · best_AI_papers_2022 alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-automl-papers | best_AI_papers_2022 |
|---|---|---|
| Maintenance | Dormant (784d since push) As of 2w · github_public_v1 | Dormant (1016d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · 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 |
Tagline
- awesome-automl-papers
- A curated list of automated machine learning papers and resources.
- best_AI_papers_2022
- A curated list of breakthrough AI papers from 2022 with video explanations and code links
Stars
- awesome-automl-papers
- 4.2k
- best_AI_papers_2022
- 3.2k
Forks
- awesome-automl-papers
- 678
- best_AI_papers_2022
- 197
Open issues
- awesome-automl-papers
- 2
- best_AI_papers_2022
- 0
Language
- awesome-automl-papers
- -
- best_AI_papers_2022
- -
Adopt for
- 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.
- best_AI_papers_2022
- Best AI Papers from 2022 offers video explanations and code links for selected research papers.
Persona
- awesome-automl-papers
- -
- best_AI_papers_2022
- -
Runtime
- awesome-automl-papers
- -
- best_AI_papers_2022
- -
License
- awesome-automl-papers
- Apache-2.0
- best_AI_papers_2022
- MIT
Last pushed
- awesome-automl-papers
- Jun 11, 2024
- best_AI_papers_2022
- Oct 18, 2023
Categories
- awesome-automl-papers
- Evaluation & Observability, Model Training
- best_AI_papers_2022
- Evaluation & Observability, Model Training
Trust and health
Days since push
- awesome-automl-papers
- 784d
- best_AI_papers_2022
- 1016d
Open issues (now)
- awesome-automl-papers
- 2
- best_AI_papers_2022
- 0
Full report
- awesome-automl-papers
- Trust report
- best_AI_papers_2022
- Trust report
Choose awesome-automl-papers if…
- License: awesome-automl-papers is Apache-2.0, best_AI_papers_2022 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
Choose best_AI_papers_2022 if…
- License: best_AI_papers_2022 is MIT, awesome-automl-papers is Apache-2.0.
- Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning.
- Need to catch up on key innovations in AI from 2022
When NOT to use best_AI_papers_2022
- Looking for real-time updates or post-2022 research findings
- Require detailed technical analysis beyond paper abstracts
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- GitHub forks (hibayesian/awesome-automl-papers) · observed Aug 4, 2026
- Last push (hibayesian/awesome-automl-papers) · observed Jun 11, 2024
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (louisfb01/best_AI_papers_2022) · observed Jul 31, 2026
- GitHub forks (louisfb01/best_AI_papers_2022) · observed Jul 31, 2026
- Last push (louisfb01/best_AI_papers_2022) · observed Oct 18, 2023
- License file (MIT) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-automl-papers 4.2k · best_AI_papers_2022 3.2k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-automl-papers and best_AI_papers_2022?
- awesome-automl-papers: A curated list of automated machine learning papers and resources.. best_AI_papers_2022: A curated list of breakthrough AI papers from 2022 with video explanations and code links. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-automl-papers over best_AI_papers_2022?
- Choose awesome-automl-papers over best_AI_papers_2022 when License: awesome-automl-papers is Apache-2.0, best_AI_papers_2022 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 choose best_AI_papers_2022 over awesome-automl-papers?
- Choose best_AI_papers_2022 over awesome-automl-papers when License: best_AI_papers_2022 is MIT, awesome-automl-papers is Apache-2.0; Tags unique to best_AI_papers_2022: ai, computer-vision, deep-learning, machine-learning; Need to catch up on key innovations in AI from 2022.
- 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
- When should I avoid best_AI_papers_2022?
- Looking for real-time updates or post-2022 research findings Require detailed technical analysis beyond paper abstracts
- Is awesome-automl-papers or best_AI_papers_2022 more popular on GitHub?
- awesome-automl-papers has more GitHub stars (4,155 vs 3,187). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-automl-papers and best_AI_papers_2022 open source?
- Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, best_AI_papers_2022: MIT).
- Where can I find alternatives to awesome-automl-papers or best_AI_papers_2022?
- GraphCanon lists graph-backed alternatives at awesome-automl-papers alternatives and best_AI_papers_2022 alternatives (awesome-automl-papers markdown twin, best_AI_papers_2022 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, awesome-automl-papers or best_AI_papers_2022?
- awesome-automl-papers: Dormant. best_AI_papers_2022: 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 awesome-automl-papers and best_AI_papers_2022?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-automl-papers trust report; best_AI_papers_2022 trust report.