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.

Paired image-to-image translation trained on synthetic data generated by StyleGAN2 that outperforms existing approaches in image manipulation.

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.