Who’s Ahead in AI Research at NeurIPS 2020? Insights and AI Research Rankings at the Leading AI Conference

UPDATE: We have published an update to this analysis for 2020 here. Enjoy!

Introduction

The Conference on Neural Information Processing Systems (NeurIPS) is one of the most prestigious AI research conferences (the other one is the International Conference on Machine Learning, or ICML). In 2020, the acceptance rate at NeurIPS was 20% — a total of 1,990 papers out of 9,467 submissions got in (source). Compared to 2019, the number of submissions increased by 40%, which is similar to the growth from 2018 to 2019. Using conference proceedings (NeurIPS 2020), we compiled a list of authors and their affiliated organizations and then calculated the Publication Index for each organization (see “Methodology” section below). The most intuitive way to think of the Publication Index is from the point of view of full paper equivalents: Google’s Publication Index of 128 can be interpreted as if Google published 128 full papers at NeurIPS 2020.

Methodology

The methodology of our Publication Index is inspired by the Nature Index:

Who’s Ahead in AI Research at NeurIPS 2020?

Top 100 Global Organizations (Industry & Academia) Leading in AI Research at NeurIPS 2020 (with Publication Indices):

Top 100 Global Organizations (Industry & Academia) Leading in AI Research at NeurIPS 2020
Top 40 American Universities Leading in AI Research at NeurIPS 2020
Top 40 Global Universities Leading in AI Research at NeurIPS 2020
Top 20 Global Companies Leading in AI Research at NeurIPS 2020

Further Analysis

Word Cloud of Paper Titles at NeurIPS 2020:

Word Cloud of Paper Titles at NeurIPS 2020

Discussion

Let’s see what changed in the top 10 rankings between NeurIPS 2019 and NeurIPS 2020 (for 2019 data please see our AI Research Rankings 2019, where we combined insights from NeurIPS 2019 and ICML 2019).

Dataset

Please note that even data science conferences still don’t release publication data in any sort of Python-friendly form 🤷‍♂️, so our analysis ended up being quite manual (i.e. first parse HTML, then fix typos in organization names, standardize them, split lines with multiple organizations, summarize with a pivot table, etc.). If you find any bugs, please email us, and we’ll be happy to fix them.

Serial Entrepreneur & Investor | AI @ MIT & MBA @ Wharton | Peter Thiel, Y Combinator, Palantir, Goldman Sachs