--- title: "awesome-automl-papers vs best_AI_papers_2022" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hibayesian-awesome-automl-papers-vs-louisfb01-best-ai-papers-2022" tools: ["hibayesian-awesome-automl-papers", "louisfb01-best-ai-papers-2022"] --- # awesome-automl-papers vs best_AI_papers_2022 *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 best_AI_papers_2022 if best AI Papers from 2022 offers video explanations and code links for selected research papers. [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. [best_AI_papers_2022](https://www.louisbouchard.ai) has 3.2k stars, 197 forks, and 0 open issues, last pushed Oct 18, 2023. Figures are from public GitHub metadata via [awesome-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [best_AI_papers_2022's repository](https://github.com/louisfb01/best_AI_papers_2022). | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | | --- | --- | --- | | Tagline | A curated list of automated machine learning papers and resources. | A curated list of breakthrough AI papers from 2022 with video explanations and code links | | Stars | 4,155 | 3,187 | | Forks | 678 | 197 | | 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. | Best AI Papers from 2022 offers video explanations and code links for selected research papers. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [best_AI_papers_2022](/tools/louisfb01-best-ai-papers-2022.md) | | --- | --- | --- | | Days since push | 784d | 1016d | | Open issues (now) | 2 | 0 | | Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/louisfb01-best-ai-papers-2022/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: best_AI_papers_2022 - **Adopt for:** Best AI Papers from 2022 offers video explanations and code links for selected research papers. ## Choose when ### 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 ### 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 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 best_AI_papers_2022 - Looking for real-time updates or post-2022 research findings - Require detailed technical analysis beyond paper abstracts ## 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](/tools/hibayesian-awesome-automl-papers/alternatives) and [best_AI_papers_2022 alternatives](/tools/louisfb01-best-ai-papers-2022/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/alternatives.md), [best_AI_papers_2022 markdown twin](/tools/louisfb01-best-ai-papers-2022/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-louisfb01-best-ai-papers-2022.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 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](/tools/hibayesian-awesome-automl-papers/trust); [best_AI_papers_2022 trust report](/tools/louisfb01-best-ai-papers-2022/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/_