--- title: "awesome-automl-papers vs ML-news-of-the-week" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-salvatorera-ml-news-of-the-week" tools: ["hibayesian-awesome-automl-papers", "salvatorera-ml-news-of-the-week"] --- # awesome-automl-papers vs ML-news-of-the-week *GraphCanon updated Aug 4, 2026* ## 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 ML-news-of-the-week if mL-news-of-the-week offers weekly curated lists of the latest ML & AI research, news, resources, and perspectives along with a humorous meme to lighten the mood. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. [ML-news-of-the-week](https://github.com/SalvatoreRa/ML-news-of-the-week) has 183 stars, 12 forks, and 0 open issues, last pushed Jul 27, 2025. Figures are from public GitHub metadata via [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [ML-news-of-the-week's repository](https://github.com/SalvatoreRa/ML-news-of-the-week). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [ML-news-of-the-week](/tools/salvatorera-ml-news-of-the-week.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | A collection of best ML and AI news every week | | Stars | 4,155 | 183 | | Forks | 678 | 12 | | Open issues | 2 | 0 | | Language | - | - | | Adopt for | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | ML-news-of-the-week offers weekly curated lists of the latest ML & AI research, news, resources, and perspectives along with a humorous meme to lighten the mood. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [ML-news-of-the-week](/tools/salvatorera-ml-news-of-the-week.md) | | --- | --- | --- | | Days since push | 784d | 369d | | Open issues (now) | 2 | 0 | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/salvatorera-ml-news-of-the-week/trust.md) | ## Decision facts: awesome-automl-papers - **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. ## Decision facts: ML-news-of-the-week - **Adopt for:** ML-news-of-the-week offers weekly curated lists of the latest ML & AI research, news, resources, and perspectives along with a humorous meme to lighten the mood. ## Choose when ### Choose awesome-automl-papers if… - Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search. - Also covers Model Training. - When you need a curated list of academic materials to research or learn about AutoML technologies ### Choose ML-news-of-the-week if… - Tags unique to ML-news-of-the-week: agents, ai, artificial-intelligence, computer-vision. - Use when you need quick access to aggregated information covering significant advancements in artificial intelligence and machine learning on a weekly basis. - More recently updated (last pushed Jul 27, 2025). ## 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 ## When NOT to use ML-news-of-the-week - Avoid if you require detailed analysis beyond the curated summaries provided, as it may not fulfill needs for deep dives into specific topics. - Not recommended for those who prefer daily updates instead of weekly overviews. ## Common questions ### What is the difference between awesome-automl-papers and ML-news-of-the-week? awesome-automl-papers: A curated list of automated machine learning papers and resources.. ML-news-of-the-week: A collection of best ML and AI news every week. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-automl-papers over ML-news-of-the-week? Choose awesome-automl-papers over ML-news-of-the-week when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; Also covers Model Training; When you need a curated list of academic materials to research or learn about AutoML technologies. ### When should I choose ML-news-of-the-week over awesome-automl-papers? Choose ML-news-of-the-week over awesome-automl-papers when Tags unique to ML-news-of-the-week: agents, ai, artificial-intelligence, computer-vision; Use when you need quick access to aggregated information covering significant advancements in artificial intelligence and machine learning on a weekly basis; More recently updated (last pushed Jul 27, 2025). ### 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 ML-news-of-the-week? Avoid if you require detailed analysis beyond the curated summaries provided, as it may not fulfill needs for deep dives into specific topics. Not recommended for those who prefer daily updates instead of weekly overviews. ### Is awesome-automl-papers or ML-news-of-the-week more popular on GitHub? awesome-automl-papers has more GitHub stars (4,155 vs 183). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-automl-papers and ML-news-of-the-week open source? Yes - both are open-source projects on GitHub (awesome-automl-papers: Apache-2.0, ML-news-of-the-week: Apache-2.0). ### Where can I find alternatives to awesome-automl-papers or ML-news-of-the-week? GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [ML-news-of-the-week alternatives](/tools/salvatorera-ml-news-of-the-week/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/alternatives.md), [ML-news-of-the-week markdown twin](/tools/salvatorera-ml-news-of-the-week/alternatives.md)), 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](/compare/hibayesian-awesome-automl-papers-vs-salvatorera-ml-news-of-the-week.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-automl-papers or ML-news-of-the-week? awesome-automl-papers: Dormant. ML-news-of-the-week: 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 ML-news-of-the-week? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust); [ML-news-of-the-week trust report](/tools/salvatorera-ml-news-of-the-week/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_