Selected projects

To encourage future developments of scalable similarity search algorithms, we release two billion-scale datasets that can serve as representative benchmarks for researchers from the machine learning and algorithmic communities interested in efficient similarity search.

A library to train large neural networks across the internet. Imagine training one huge transformer on thousands of computers from universities, companies, and volunteers.

Image restoration with neural networks but without learning.

CatBoost is a fast, scalable, high performance gradient boosting on decision trees library. Used for ranking, classification, regression and other ML tasks.