--- title: "ml-surveys vs awesome-automl-papers" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/eugeneyan-ml-surveys-vs-hibayesian-awesome-automl-papers" tools: ["eugeneyan-ml-surveys", "hibayesian-awesome-automl-papers"] --- # ml-surveys vs awesome-automl-papers *GraphCanon updated Aug 22, 2026* ## 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. [ml-surveys](https://github.com/eugeneyan/ml-surveys) reports 2.9k GitHub stars, 292 forks, and 2 open issues, last pushed Mar 17, 2023. [awesome-automl-papers](https://github.com/hibayesian/awesome-automl-papers) has 4.2k stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. Figures are from public GitHub metadata via [ml-surveys's repository](https://github.com/eugeneyan/ml-surveys) and [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers). | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Tagline | Survey papers summarizing advances in various AI domains | A curated list of automated machine learning papers and resources. | | Stars | 2,902 | 4,155 | | Forks | 292 | 678 | | Open issues | 2 | 2 | | Language | - | - | | Adopt for | 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 is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Computer Vision, Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ml-surveys](/tools/eugeneyan-ml-surveys.md) | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | | --- | --- | --- | | Days since push | 1254d | 784d | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/eugeneyan-ml-surveys/trust.md) | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | ## Decision facts: ml-surveys - **Adopt for:** 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. ## 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. ## Choose when ### 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 ### 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 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 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 ## 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](/tools/eugeneyan-ml-surveys/alternatives) and [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) ([ml-surveys markdown twin](/tools/eugeneyan-ml-surveys/alternatives.md), [awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/eugeneyan-ml-surveys-vs-hibayesian-awesome-automl-papers.md) 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](/tools/eugeneyan-ml-surveys/trust); [awesome-automl-papers trust report](/tools/hibayesian-awesome-automl-papers/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=eugeneyan-ml-surveys`](/api/graphcanon/graph?tool=eugeneyan-ml-surveys) - 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/_