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NeurIPS 2024 Fact Sheet
reportCredibility Rating
5/5
Gold(5)Gold standard. Rigorous peer review, high editorial standards, and strong institutional reputation.
Rating inherited from publication venue: NeurIPS
This fact sheet provides statistical context on AI research volume and community size at NeurIPS 2024; useful as a reference for understanding the scale of contemporary AI research output and the growth trajectory of the field.
Metadata
Importance: 25/100organizational reportreference
Summary
Official fact sheet for the 38th NeurIPS conference held in Vancouver, Canada, summarizing key statistics including 19,756 total registrations and 4,497 accepted papers. The document provides a high-level overview of the scale and scope of one of AI's most prominent research conferences.
Key Points
- •19,756 total registrations, reflecting the massive and growing scale of AI research community participation
- •4,497 papers accepted across the main conference and datasets & benchmarks tracks
- •Held in Vancouver, Canada as the 38th annual NeurIPS conference
- •Featured diverse keynote speakers covering cutting-edge AI research topics
- •Serves as an official record of conference metrics useful for tracking AI research growth over time
Review
NeurIPS 2024 represents a significant milestone in AI research, demonstrating continued growth and diversification in the field. The conference saw a 21% overall registration increase, with 16,777 in-person attendees, reflecting the sustained interest in machine learning and artificial intelligence. The program was comprehensive, featuring 11 conference tracks, including 6 on Creative AI, 7 invited talks, and multiple workshops and competitions. The conference's academic rigor was evident in its paper selection process, with an acceptance rate of around 25% for both main conference and datasets tracks. Notably, the event emphasized diversity and inclusion through nine affinity groups and a new high school projects initiative. The invited keynote speakers, including luminaries like Alison Gopnik and Fei-Fei Li, covered diverse topics ranging from child learning to visual intelligence, underscoring the interdisciplinary nature of contemporary AI research. Best paper awards highlighted innovative work in areas such as visual autoregressive modeling, stochastic derivative estimation, and large language model alignment.
Resource ID:
3590e18cc6687057 | Stable ID: Njg2OTkyZj