---
title: "NeurIPS (A*): papers and universities"
description: "NeurIPS (Advances in Neural Information Processing Systems (was NIPS)) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 10,131 of its papers from 2024–2026 (4,312 in 2024, 5,819 in 2025; 2026 not in OpenAlex yet). By the countries of the authors' institutions, the most papers come from the United States (2,941), China (2,691) and the United Kingdom (751)."
url: https://scholar4ai.com/proceedings/neurips
markdown_url: https://scholar4ai.com/proceedings/neurips.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "NeurIPS (Advances in Neural Information Processing Systems (was NIPS)) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 10,131 of its papers from 2024–2026 (4,312 in 2024, 5,819 in 2025; 2026 not… (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# NeurIPS: Advances in Neural Information Processing Systems (was NIPS) — papers and institutions, 2024–2026

Papers of NeurIPS, Advances in Neural Information Processing Systems (was NIPS) (CORE rank A*; field: Machine learning), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- NeurIPS (Advances in Neural Information Processing Systems (was NIPS)) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 10,131 of its papers from 2024–2026 (4,312 in 2024, 5,819 in 2025; 2026 not in OpenAlex yet).
- By the countries of the authors' institutions, the most papers come from the United States (2,941), China (2,691) and the United Kingdom (751).
- The institutions with the most papers are Tsinghua University (475), Peking University (325) and Shanghai Jiao Tong University (292).
- Its most-cited paper of these years is “YOLOv10: Real-Time End-to-End Object Detection” (2024), with 3,032 citations.
- OpenAlex has affiliations for only 58% of its papers, so the institution and country counts are incomplete.

## Papers per year

*NeurIPS papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 4,312 | 112 |
| 2025 | 5,819 | 5,797 |
| 2026 | – | – |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4363606243](https://openalex.org/S4363606243) | The conference's proceedings |

## Who publishes there

*Institutions with the most NeurIPS papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 475 |
| 2 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 325 |
| 3 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 292 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 279 |
| 5 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 252 |
| 6 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 250 |
| 7 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 239 |
| 8 | [Stanford University](https://scholar4ai.com/institutions/stanford-university.md) | 224 |
| 9 | Chinese University of Hong Kong | 218 |
| 10 | Carnegie Mellon University | 217 |

*Countries with the most NeurIPS papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | United States | 2,941 |
| 2 | China | 2,691 |
| 3 | United Kingdom | 751 |
| 4 | Hong Kong | 654 |
| 5 | Singapore | 508 |
| 6 | Germany | 392 |
| 7 | Canada | 333 |
| 8 | South Korea | 314 |
| 9 | Switzerland | 259 |
| 10 | Australia | 254 |

*Keywords with the most NeurIPS papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 1,295 |
| 2 | diffusion models | 490 |
| 3 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 352 |
| 4 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 299 |
| 5 | [reinforcement learning](https://scholar4ai.com/keywords/reinforcement-learning.md) | 272 |
| 6 | benchmark | 212 |
| 7 | generative models | 212 |
| 8 | multimodal large language models | 198 |
| 9 | transformer models | 171 |
| 10 | generalization | 160 |

## Most-cited papers

*Most-cited NeurIPS papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 3,032 | [YOLOv10: Real-Time End-to-End Object Detection](https://doi.org/10.52202/079017-3429) | 2024-01-01 | Ao Wang, Hui Chen, Lihao Liu et al. |
| 2 | 1,734 | [VMamba: Visual State Space Model](https://doi.org/10.52202/079017-3273) | 2024-01-01 | Yue Liu, Yunjie Tian, Yuzhong Zhao et al. |
| 3 | 903 | [YOLOv12: Attention-Centric Real-Time Object Detectors](https://doi.org/10.52202/085713-2627) | 2025-01-01 | Yunjie Tian, Qixiang Ye, David Doermann |
| 4 | 668 | [Depth Anything V2](https://doi.org/10.52202/079017-0688) | 2024-01-01 | Lihe Yang, Bingyi Kang, Zilong Huang et al. |
| 5 | 300 | [TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables](https://doi.org/10.52202/079017-0015) | 2024-01-01 | Yuxuan Wang, Haixu Wu, Jiaxiang Dong et al. |
| 6 | 299 | [xLSTM: Extended Long Short-Term Memory](https://doi.org/10.52202/079017-3417) | 2024-01-01 | Maximilian Beck, Korbinian Pöppel, Markus Spanring et al. |
| 7 | 227 | [Gorilla: Large Language Model Connected with Massive APIs](https://doi.org/10.52202/079017-4020) | 2024-01-01 | Shishir G. Patil, Tianjun Zhang, Xin Wang et al. |
| 8 | 221 | [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://doi.org/10.52202/079017-4447) | 2024-01-01 | Zhiwen Fan, Kevin Wang, Kairun Wen et al. |
| 9 | 218 | [CogVLM: Visual Expert for Pretrained Language Models](https://doi.org/10.52202/079017-3860) | 2024-01-01 | Weihan Wang, Qingsong Lv, Wenmeng Yu et al. |
| 10 | 152 | [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://doi.org/10.52202/079017-2694) | 2024-01-01 | Keyu Tian, Yi Jiang, Zehuan Yuan et al. |

## Cite this page

Scholar4AI. (2026). NeurIPS: Advances in Neural Information Processing Systems (was NIPS) — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/neurips

```bibtex
@misc{scholar4ai-proceedings-neurips,
  author = {{Scholar4AI}},
  title = {NeurIPS: Advances in Neural Information Processing Systems (was NIPS) — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/neurips}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).