This competition focuses on developing indexing data structures and search algorithms for practical settings of the Approximate Nearest Neighbor (ANN) or Vector search problem. More details are on the competition’s website. We encourage everyone to join by August 30th, 2023!


NeurIPS 2023 Competition Track: Big-ANN

A paper has been accepted for publication at the International Conference on Computer Vision (ICCV 2023). DeDrift: Robust Similarity Search under Content Drift by Dmitry Baranchuk, Matthijs Douse, Yash Upadhyay, Zeki Yalniz.

Paper accepted to ICCV 2023

At the International Conference on Machine Learning (ICML 2023), Yandex Research will present papers on modelling tabular data with diffusion models, a model-parallel training algorithm designed for poorly connected devices and other topics. We also co-author a tutorial ​​on reinforcement learning from human feedback.

Yandex at ICML 2023

At Yandex Research, we advance progress in science and develop technologies for a better tomorrow.

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Join our research team and get an opportunity to engage in world-class applied and theoretical research, gain experience working on high-impact projects and publish at top-tier academic conferences. You will contribute to high-tech services like computer vision technologies, dialogue systems, neural machine translation and self-driving cars. 

Work with us

Yandex Research team regularly contributes to the computer vision research community, mostly in the field of image retrieval and generative modelling.

Computer vision

Language is one of the key forms of communication. We study methods of language representation and understanding to simplify human-computer interactions.

Natural language processing 

Today, training most powerful models often takes significant resources. Our research aims to make large-scale training more efficient and accessible to the entire machine learning community.

Large-scale machine learning

We study various aspects related to theoretical understanding of ML models and algorithms.

Machine learning theory

Graphs are a natural way to represent data from various domains such as social networks, molecules, text, code, etc. We develop and analyze algorithms for graph-structured data.

Graph machine learning

Probabilistic machine learning describes methods which enable reasoning and inference over unknown quantities. Commonly used in generative modelling, regression and uncertainty quantification.

Probabilistic machine learning

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Large language models are among the most significant recent advances in machine learning. Still, leveraging these models can be difficult: offloading and quantization have limitations, and third-party APIs are less flexible. We propose Petals, an open-source decentralized system (showcased this week at the ACL 2023 Demonstrations track) allowing anybody to run large models or even adapt them using the idle resources of volunteers. In this post, you will learn the motivation behind the system, its underlying ideas, and its advantages compared to other ways of using large models.

Petals: decentralized inference and finetuning of large language models

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Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. In this post, we share some findings on application of diffusion models to tabular data. We introduce TabDDPM — a diffusion model that can be universally applied to any tabular dataset and handles any type of feature (numerical and categorical).

Follow the cutting-edge research in computer science

Blog

Publications

TabDDPM: Modelling Tabular Data with Diffusion Models

SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

Which Tricks Are Important for Learning to Rank?