Comparison
awesome-automl-papers vs anomaly-detection-resources
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 anomaly-detection-resources if anomaly-detection-resources: Comprehensive collection of anomaly detection materials including books, courses, datasets, libraries with an AGPL-3.0 license.
Markdown twin · awesome-automl-papers alternatives · anomaly-detection-resources alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | awesome-automl-papers | anomaly-detection-resources |
|---|---|---|
| Maintenance | Dormant (784d since push) As of 2w · github_public_v1 | Slowing (168d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 4d · 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.
- anomaly-detection-resources
- Anomaly detection related books, papers, videos, and toolboxes.
Stars
- awesome-automl-papers
- 4.2k
- anomaly-detection-resources
- 9.4k
Forks
- awesome-automl-papers
- 678
- anomaly-detection-resources
- 1.8k
Open issues
- awesome-automl-papers
- 2
- anomaly-detection-resources
- 14
Language
- awesome-automl-papers
- -
- anomaly-detection-resources
- Python
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.
- anomaly-detection-resources
- anomaly-detection-resources: Comprehensive collection of anomaly detection materials including books, courses, datasets, libraries with an AGPL-3.0 license.
Persona
- awesome-automl-papers
- -
- anomaly-detection-resources
- -
Runtime
- awesome-automl-papers
- -
- anomaly-detection-resources
- -
License
- awesome-automl-papers
- Apache-2.0
- anomaly-detection-resources
- AGPL-3.0
Last pushed
- awesome-automl-papers
- Jun 11, 2024
- anomaly-detection-resources
- Mar 2, 2026
Categories
- awesome-automl-papers
- Evaluation & Observability, Model Training
- anomaly-detection-resources
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- awesome-automl-papers
- Dormant (18%)
- anomaly-detection-resources
- Slowing (36%)
Days since push
- awesome-automl-papers
- 784d
- anomaly-detection-resources
- 168d
Open issues (now)
- awesome-automl-papers
- 2
- anomaly-detection-resources
- 14
Stars delta
- awesome-automl-papers
- Unknown
- anomaly-detection-resources
- +16 (30d)
Open issues delta
- awesome-automl-papers
- Unknown
- anomaly-detection-resources
- 0 (30d)
Full report
- awesome-automl-papers
- Trust report
- anomaly-detection-resources
- Trust report
Choose awesome-automl-papers if…
- License: awesome-automl-papers is Apache-2.0, anomaly-detection-resources is AGPL-3.0.
- 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 anomaly-detection-resources if…
- License: anomaly-detection-resources is AGPL-3.0, awesome-automl-papers is Apache-2.0.
- Tags unique to anomaly-detection-resources: anomaly-detection, awesome-list, fraud-detection, graph-neural-networks.
- Need extensive learning resources on outlier detection techniques
When NOT to use anomaly-detection-resources
- Require proprietary or commercial tools with restrictive licenses
- Looking for a standalone tool rather than a collection of resources
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 (yzhao062/anomaly-detection-resources) · observed Aug 17, 2026
- GitHub forks (yzhao062/anomaly-detection-resources) · observed Aug 17, 2026
- Last push (yzhao062/anomaly-detection-resources) · observed Mar 2, 2026
- License file (AGPL-3.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-automl-papers 4.2k · anomaly-detection-resources 9.4k (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-automl-papers and anomaly-detection-resources?
- awesome-automl-papers: A curated list of automated machine learning papers and resources.. anomaly-detection-resources: Anomaly detection related books, papers, videos, and toolboxes.. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-automl-papers over anomaly-detection-resources?
- Choose awesome-automl-papers over anomaly-detection-resources when License: awesome-automl-papers is Apache-2.0, anomaly-detection-resources is AGPL-3.0; 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 anomaly-detection-resources over awesome-automl-papers?
- Choose anomaly-detection-resources over awesome-automl-papers when License: anomaly-detection-resources is AGPL-3.0, awesome-automl-papers is Apache-2.0; Tags unique to anomaly-detection-resources: anomaly-detection, awesome-list, fraud-detection, graph-neural-networks; Need extensive learning resources on outlier detection techniques.
- 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 anomaly-detection-resources?
- Require proprietary or commercial tools with restrictive licenses Looking for a standalone tool rather than a collection of resources
- Is awesome-automl-papers or anomaly-detection-resources more popular on GitHub?
- anomaly-detection-resources has more GitHub stars (9,364 vs 4,155). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-automl-papers and anomaly-detection-resources open source?
- Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, anomaly-detection-resources: AGPL-3.0).
- Where can I find alternatives to awesome-automl-papers or anomaly-detection-resources?
- GraphCanon lists graph-backed alternatives at awesome-automl-papers alternatives and anomaly-detection-resources alternatives (awesome-automl-papers markdown twin, anomaly-detection-resources 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 anomaly-detection-resources?
- awesome-automl-papers: Dormant. anomaly-detection-resources: Slowing. 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 anomaly-detection-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-automl-papers trust report; anomaly-detection-resources trust report.