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At Yandex Research, we advance progress in science and develop technologies for a better tomorrow.
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GraphPFN: a graph foundation model pretrained on diverse synthetic graphsResearchFebruary 13, 2026
SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer DevicesResearchJune 18, 2024
Discrete neural algorithmic reasoningResearchSeptember 20, 2024
Publications
See allRethinking Global Text Conditioning in Diffusion Transformers
ICLR, 2026Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization
ICLR, 2026Nesterov Finds GRAAL: Optimal and Adaptive Gradient Method for Convex Optimization
ICLR, 2026SGD with Adaptive Preconditioning: Unified Analysis and Momentum Acceleration
ICLR, 2026
Research Areas
See allTabular data
Tabular data involves two-dimensional tables with objects (rows) and features (columns), which are used in numerous applied tasks such as classification, regression, ranking and many others.9 publications2 posts2 datasetsLarge-scale machine learning
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.18 publications2 postsGenerative models
Generative models in computer vision are powerful tool for various applications.20 publications6 postsGraph machine learning
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.16 publications6 posts2 datasetsNeural algorithmic reasoning
Algorithmic reasoning focuses on building models that can execute classic algorithms. It allows one to combine the advantages of neural networks with theoretical guarantees of algorithms.2 publications2 postsComputer vision
Yandex Research team regularly contributes to the computer vision research community, mostly in the field of image retrieval and generative modelling.42 publications5 posts1 dataset
