815 citations · 3.2k across the 142 of their papers we have counts for
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FMM-Net: neural network architecture based on the Fast Multipole Method
Daria Sushnikova, Pavel Kharyuk, Ivan Oseledets
In this paper, we propose a new neural network architecture based on the H2 matrix. Even though networks with H2-inspired architecture already exist, and our approach is designed t…
Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations
Evgeny Frolov, Ivan Oseledets
Self-attentive transformer models have recently been shown to solve the next item recommendation task very efficiently. The learned attention weights capture sequential dynamics in…
Optimization of Functions Given in the Tensor Train Format
Andrei Chertkov, Gleb Ryzhakov, Georgii Novikov +1
Tensor train (TT) format is a common approach for computationally efficient work with multidimensional arrays, vectors, matrices, and discretized functions in a wide range of appli…
NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer
Valentin Leplat, Daniil Merkulov, Aleksandr Katrutsa +3
Classical machine learning models such as deep neural networks are usually trained by using Stochastic Gradient Descent-based (SGD) algorithms. The classical SGD can be interpreted…
A case study of spatiotemporal forecasting techniques for weather forecasting
Shakir Showkat Sofi, Ivan Oseledets
The majority of real-world processes are spatiotemporal, and the data generated by them exhibits both spatial and temporal evolution. Weather is one of the most essential processes…
Black box approximation in the tensor train format initialized by ANOVA decomposition
Andrei Chertkov, Gleb Ryzhakov, Ivan Oseledets
Surrogate models can reduce computational costs for multivariable functions with an unknown internal structure (black boxes). In a discrete formulation, surrogate modeling is equiv…