Comparison
ml-surveys vs best_AI_papers_2021
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 best_AI_papers_2021 if best_AI_papers_2021 offers a curated list of key AI papers published in 2021 with videos, articles, and code examples.
Markdown twin · ml-surveys alternatives · best_AI_papers_2021 alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | ml-surveys | best_AI_papers_2021 |
|---|---|---|
| Maintenance | Dormant (1223d since push) As of 4w · github_public_v1 | Dormant (1016d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- ml-surveys
- Survey papers summarizing advances in various AI domains
- best_AI_papers_2021
- A curated list of AI research papers from 2021 with explanations and resources
Stars
- ml-surveys
- 2.9k
- best_AI_papers_2021
- 2.9k
Forks
- ml-surveys
- 291
- best_AI_papers_2021
- 237
Open issues
- ml-surveys
- 2
- best_AI_papers_2021
- 0
Language
- ml-surveys
- -
- best_AI_papers_2021
- -
Adopt for
- ml-surveys
- 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.
- best_AI_papers_2021
- Best_AI_papers_2021 offers a curated list of key AI papers published in 2021 with videos, articles, and code examples.
Persona
- ml-surveys
- -
- best_AI_papers_2021
- -
Runtime
- ml-surveys
- -
- best_AI_papers_2021
- -
License
- ml-surveys
- MIT
- best_AI_papers_2021
- The tool is provided under an MIT license, permitting reuse and modification with attribution.
Last pushed
- ml-surveys
- Mar 17, 2023
- best_AI_papers_2021
- Oct 18, 2023
Categories
- ml-surveys
- Computer Vision, Evaluation & Observability, Model Training
- best_AI_papers_2021
- Computer Vision, Data & Retrieval, Model Training
Trust and health
Days since push
- ml-surveys
- 1223d
- best_AI_papers_2021
- 1016d
Open issues (now)
- ml-surveys
- 2
- best_AI_papers_2021
- 0
Full report
- ml-surveys
- Trust report
- best_AI_papers_2021
- Trust report
Choose ml-surveys if…
- Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning.
- Also covers Evaluation & Observability.
- When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning
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
Choose best_AI_papers_2021 if…
- The code repository does not specify the primary programming language used for the content but contains links to Python-based code samples.
- Tags unique to best_AI_papers_2021: ai, artificial-intelligence, research-paper.
- Also covers Data & Retrieval.
- If you are seeking current insights into AI advancements from 2021, especially on topics such as ethical considerations or governance aspects.
When NOT to use best_AI_papers_2021
- Should not be used if one is looking for historical context beyond AI advances strictly from the period 2021, as it focuses specifically on that time frame.
- Not recommended if comprehensive coverage of AI research topics outside the themes covered in papers published solely in 2021 are needed.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (eugeneyan/ml-surveys) · observed Jul 22, 2026
- GitHub forks (eugeneyan/ml-surveys) · observed Jul 22, 2026
- Last push (eugeneyan/ml-surveys) · observed Mar 17, 2023
- License file (MIT) · observed Jul 22, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (louisfb01/best_AI_papers_2021) · observed Jul 31, 2026
- GitHub forks (louisfb01/best_AI_papers_2021) · observed Jul 31, 2026
- Last push (louisfb01/best_AI_papers_2021) · 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: ml-surveys 2.9k · best_AI_papers_2021 2.9k (synced Jul 22, 2026).
Common questions
- What is the difference between ml-surveys and best_AI_papers_2021?
- ml-surveys: Survey papers summarizing advances in various AI domains. best_AI_papers_2021: A curated list of AI research papers from 2021 with explanations and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose ml-surveys over best_AI_papers_2021?
- Choose ml-surveys over best_AI_papers_2021 when Tags unique to ml-surveys: embeddings, nlp, recommender-system, reinforcement-learning; Also covers Evaluation & Observability; When you need comprehensive overviews and summaries of the latest research trends in multiple areas within machine learning.
- When should I choose best_AI_papers_2021 over ml-surveys?
- Choose best_AI_papers_2021 over ml-surveys when The code repository does not specify the primary programming language used for the content but contains links to Python-based code samples; Tags unique to best_AI_papers_2021: ai, artificial-intelligence, research-paper; Also covers Data & Retrieval; If you are seeking current insights into AI advancements from 2021, especially on topics such as ethical considerations or governance aspects.
- 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 best_AI_papers_2021?
- Should not be used if one is looking for historical context beyond AI advances strictly from the period 2021, as it focuses specifically on that time frame. Not recommended if comprehensive coverage of AI research topics outside the themes covered in papers published solely in 2021 are needed.
- Is ml-surveys or best_AI_papers_2021 more popular on GitHub?
- ml-surveys has more GitHub stars (2,902 vs 2,896). Stars measure visibility, not whether either tool fits your constraints.
- Are ml-surveys and best_AI_papers_2021 open source?
- Yes - both are open-source projects on GitHub (ml-surveys: MIT, best_AI_papers_2021: MIT).
- Where can I find alternatives to ml-surveys or best_AI_papers_2021?
- GraphCanon lists graph-backed alternatives at ml-surveys alternatives and best_AI_papers_2021 alternatives (ml-surveys markdown twin, best_AI_papers_2021 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, ml-surveys or best_AI_papers_2021?
- ml-surveys: Dormant. best_AI_papers_2021: 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 best_AI_papers_2021?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ml-surveys trust report; best_AI_papers_2021 trust report.