Most prolific authors

The top 10 authors with the most papers at NeurIPS 2023 are:

• Bo Li: 15 papers
• Ludwig Schmidt: 14 papers
• Bo Han: 13 papers
• Mihaela van der Schaar: 13 papers
• Hao Wang: 12 papers
• Dacheng Tao: 11 papers
• Bernhard Schölkopf: 11 papers
• Masashi Sugiyama: 11 papers
• Andreas Krause: 11 papers
• Tongliang Liu: 11 papers

Number of unique authors

There were 13,012 unique authors at NeurIPS 2023, up from 9913 at NeurIPS 2022.

This continues the exponential explosion of unique authors over the past decade.

Number of authors per paper

• The average number of authors per paper was 4.98, up from 4.66 at NeurIPS 2022.
• Additionally, there were a handful of single-author papers, in contrast to NeurIPS 2022, where the minimum number of authors was 2.
• The paper with the most authors was ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Title Length

• The average title length was 8.72 words, up from 8.48 at NeurIPS 2022. This continues an ongoing trend of title lengthening:

Prevalence of Acronyms

22% of titles introduced an acronym, up from 18% at NeurIPS 2022.

LaTeX in Titles

• 1.3% of titles contained LaTeX, whereas none of the titles at NeurIPS 2022 contained LaTeX.

Abstract Length

• The longest abstract was from [Re] On the Reproducibility of FairCal: Fairness Calibration for Face Verification, which has 373 words.
• The shortest abstract was from Improved Convergence in High Probability of Clipped Gradient Methods with Heavy Tailed Noise, which has 29 words.

GitHub Reigns Supreme

• Out of the 3581 abstracts, 675 explicitly mention GitHub, including a link to their code, models, or data.
• Only 79 abstracts include a URL that is not GitHub.

Modalities, Models, and Tasks

Using a CLIP model, we zero-shot classified/predicted the modality of focus for each paper based on its abstract. The categories were ["vision", "text", "audio", "tabular", "time series", "multimodal"].

By far the biggest category was multimodal, with a count of 1296. However, the CLIP model's inclination towards "multimodal" may be somewhat biased by trying to partially fit other modalities — the words multi-modal and multimodal only show up in 156 abstracts, and phrases like vision-language and text-to-image only appear a handful of times across the dataset.

Themes occurring frequently include:

• "benchmark": 730
• ("generation", "generate"): 681
• ("efficient", "efficiency"): 963
• "agent": 280
• ("llm", "large language model"): 238