Paper Digest: 100 Must-Read Machine Learning Papers of the Past 10 Years (2016-2025)

Machine learning has changed faster in the past decade than in any period before it. The Transformer replaced recurrence with attention. GPT-3 showed what scale alone could do. Diffusion models became the standard way to generate images. InstructGPT, LoRA, and DPO made it practical to adapt and align large language models. PyTorch and AdamW became default tools in nearly every lab. Keeping up is hard: NeurIPS alone now accepts thousands of papers a year. This reading list is a starting point: 100 machine learning papers from 2016 to 2025 that the research community has built on most.

How the papers were selected

The papers were selected by citation count from the three flagship machine learning conferences: NeurIPS, ICML, and ICLR. For each year from 2016 to 2025, we took the ten most-cited papers across that year’s conferences. Selecting by year keeps recent work from being crowded out by older papers that have simply had more time to collect citations. Our ICLR data begins in 2018, so the 2016 and 2017 picks come from NeurIPS and ICML. These venues cover the whole field, so the list includes plenty of vision, language, and speech papers alongside core machine learning.

The list is ordered newest year first. Read from the bottom up, it doubles as a short history of how the field moved:

• GANs, deep reinforcement learning, and graph neural networks
• transformers and large-scale pretraining
• self-supervised learning and diffusion models
• today’s work on aligning large language models and teaching them to reason

A note on scope: this list reflects our selection criteria, not a definitive ranking of the best machine learning research. There may well be better or more important papers elsewhere. Some landmark work appeared outside these three conferences: BERT at NAACL, AlphaFold in Nature, and many influential LLM technical reports, such as LLaMA, only on arXiv. Citation counts also favor popular topics and lag behind the newest work. Treat the list as a well-grounded map of the field rather than the final word. For the full year-by-year lists, see the Most Influential NeurIPS, ICML, and ICLR papers pages. Lists for other venues are on the Best Paper Digest page.

Keeping up beyond this list

If you work across fields, the companion lists follow the same method: 100 must-read computer vision papers of the past 10 years (CVPR, ICCV, ECCV) and 100 must-read natural language processing papers of the past 10 years (ACL, EMNLP, NAACL).

A citation-based list looks backward. It tells you what mattered, not what is emerging this month. Every paper on Paper Digest links to related papers, patents, grants, and experts, so you can explore the research around any of the work below. You can also run a literature review on a specific topic. If you would like new machine learning papers matched to your interests each morning, you can sign up and set up a daily digest.

TABLE 1: Paper Digest: 100 Must-Read Machine Learning Papers of the Past 10 Years (2016-2025)