Ivan Oseledets

research

∙ 09/03/2023

On the structure of the Schur complement matrix for the Stokes equation

In this paper, we investigate the structure of the Schur complement matr...

0 Vladislav Pimanov, et al. ∙

research

∙ 08/31/2023

Unsupervised evaluation of GAN sample quality: Introducing the TTJac Score

Evaluation metrics are essential for assessing the performance of genera...

0 Egor Sevriugov, et al. ∙

research

∙ 08/31/2023

Robust GAN inversion

Recent advancements in real image editing have been attributed to the ex...

0 Egor Sevriugov, et al. ∙

research

∙ 08/08/2023

Quantization Aware Factorization for Deep Neural Network Compression

Tensor decomposition of convolutional and fully-connected layers is an e...

0 Daria Cherniuk, et al. ∙

research

∙ 07/11/2023

On the efficient preconditioning of the Stokes equations in tight geometries

If the Stokes equations are properly discretized, it is well-known that ...

0 Vladislav Pimanov, et al. ∙

research

∙ 06/05/2023

Efficient GPT Model Pre-training using Tensor Train Matrix Representation

Large-scale transformer models have shown remarkable performance in lang...

0 Viktoriia Chekalina, et al. ∙

research

∙ 05/31/2023

Bridging Spectral Embedding and Matrix Completion in Self-Supervised Learning

Self-supervised methods received tremendous attention thanks to their se...

0 Marina Munkhoeva, et al. ∙

research

∙ 05/08/2023

Adaptive Cross Tubal Tensor Approximation

In this paper, we propose a new adaptive cross algorithm for computing a...

0 Salman Ahmadi Asl, et al. ∙

research

∙ 04/11/2023

TT-SDF2PC: Registration of Point Cloud and Compressed SDF Directly in the Memory-Efficient Tensor Train Domain

This paper addresses the following research question: “can one compress ...

0 Alexey I. Boyko, et al. ∙

research

∙ 03/20/2023

Translate your gibberish: black-box adversarial attack on machine translation systems

Neural networks are deployed widely in natural language processing tasks...

0 Andrei Chertkov, et al. ∙

research

∙ 03/14/2023

Multiparticle Kalman filter for object localization in symmetric environments

This study considers the object localization problem and proposes a nove...

0 Roman Korkin, et al. ∙

research

∙ 02/05/2023

Federated Privacy-preserving Collaborative Filtering for On-Device Next App Prediction

In this study, we propose a novel SeqMF model to solve the problem of pr...

0 Albert Sayapin, et al. ∙

research

∙ 01/30/2023

General Covariance Data Augmentation for Neural PDE Solvers

The growing body of research shows how to replace classical partial diff...

0 Vladimir Fanaskov, et al. ∙

research

∙ 01/28/2023

PROTES: Probabilistic Optimization with Tensor Sampling

We develop new method PROTES for optimization of the multidimensional ar...

0 Anastasia Batsheva, et al. ∙

research

∙ 01/12/2023

Machine learning methods for prediction of breakthrough curves in reactive porous media

Reactive flows in porous media play an important role in our life and ar...

0 Daria Fokina, et al. ∙

research

∙ 01/08/2023

Mitigating Human and Computer Opinion Fraud via Contrastive Learning

We introduce the novel approach towards fake text reviews detection in c...

0 Yuliya Tukmacheva, et al. ∙

research

∙ 12/25/2022

FMM-Net: neural network architecture based on the Fast Multipole Method

In this paper, we propose a new neural network architecture based on the...

0 Daria Sushnikova, et al. ∙

research

∙ 12/12/2022

Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations

Self-attentive transformer models have recently been shown to solve the ...

0 Evgeny Frolov, et al. ∙

research

∙ 09/29/2022

NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizers

Classical machine learning models such as deep neural networks are usual...

13 Valentin Leplat, et al. ∙

research

∙ 09/29/2022

Optimization of Functions Given in the Tensor Train Format

Tensor train (TT) format is a common approach for computationally effici...

0 Andrei Chertkov, et al. ∙

research

∙ 09/29/2022

A case study of spatiotemporal forecasting techniques for weather forecasting

The majority of real-world processes are spatiotemporal, and the data ge...

14 Shakir Showkat Sofi, et al. ∙

research

∙ 08/05/2022

Black box approximation in the tensor train format initialized by ANOVA decomposition

Surrogate models can reduce computational costs for multivariable functi...

0 Andrei Chertkov, et al. ∙

research

∙ 08/02/2022

T4DT: Tensorizing Time for Learning Temporal 3D Visual Data

Unlike 2D raster images, there is no single dominant representation for ...

2 Mikhail Usvyatsov, et al. ∙

research

∙ 07/31/2022

Eco2AI: carbon emissions tracking of machine learning models as the first step towards sustainable AI

The size and complexity of deep neural networks continue to grow exponen...

0 Semen Budennyy, et al. ∙

research

∙ 07/13/2022

Cross Tensor Approximation for Image and Video Completion

This paper proposes a general framework to use the cross tensor approxim...

0 Salman Ahmadi Asl, et al. ∙

research

∙ 07/06/2022

Tensor networks in machine learning

A tensor network is a type of decomposition used to express and approxim...

0 Richik Sengupta, et al. ∙

research

∙ 06/08/2022

Constructive TT-representation of the tensors given as index interaction functions with applications

This paper presents a method to build explicit tensor-train (TT) represe...

0 Gleb Ryzhakov, et al. ∙

research

∙ 05/12/2022

Direct optimization of BPX preconditioners

We consider an automatic construction of locally optimal preconditioners...

0 Vladimir Fanaskov, et al. ∙

research

∙ 05/10/2022

Tensor-based Collaborative Filtering With Smooth Ratings Scale

Conventional collaborative filtering techniques don't take into consider...

0 Nikita Marin, et al. ∙

research

∙ 05/09/2022

Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks

Collaborative filtering models generally perform better than content-bas...

0 Artyom Nikitin, et al. ∙

research

∙ 04/30/2022

TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning

We present a novel procedure for optimization based on the combination o...

0 Konstantin Sozykin, et al. ∙

research

∙ 04/25/2022

On the Performance of Machine Learning Methods for Breakthrough Curve Prediction

Reactive flows are important part of numerous technical and environmenta...

0 Daria Fokina, et al. ∙

research

∙ 03/21/2022

Hyperbolic Vision Transformers: Combining Improvements in Metric Learning

Metric learning aims to learn a highly discriminative model encouraging ...

0 Aleksandr Ermolov, et al. ∙

research

∙ 02/23/2022

Extension of Dynamic Mode Decomposition for dynamic systems with incomplete information based on t-model of optimal prediction

The Dynamic Mode Decomposition has proved to be a very efficient techniq...

0 Aleksandr Katrutsa, et al. ∙

research

∙ 02/21/2022

Survey on Large Scale Neural Network Training

Modern Deep Neural Networks (DNNs) require significant memory to store w...

0 Julia Gusak, et al. ∙

research

∙ 02/14/2022

Understanding DDPM Latent Codes Through Optimal Transport

Diffusion models have recently outperformed alternative approaches to mo...

0 Valentin Khrulkov, et al. ∙

research

∙ 02/02/2022

Smoothed Embeddings for Certified Few-Shot Learning

Randomized smoothing is considered to be the state-of-the-art provable d...

0 Mikhail Pautov, et al. ∙

research

∙ 02/01/2022

Few-Bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction

Memory footprint is one of the main limiting factors for large neural ne...

0 Georgii Novikov, et al. ∙

research

∙ 01/31/2022

Memory-Efficient Backpropagation through Large Linear Layers

In modern neural networks like Transformers, linear layers require signi...

0 Daniel Bershatsky, et al. ∙

research

∙ 11/29/2021

Latent Transformations via NeuralODEs for GAN-based Image Editing

Recent advances in high-fidelity semantic image editing heavily rely on ...

0 Valentin Khrulkov, et al. ∙

research

∙ 09/22/2021

CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks

In safety-critical machine learning applications, it is crucial to defen...

0 Mikhail Pautov, et al. ∙

research

∙ 06/13/2021

Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks

The near-infrared (NIR) spectral range (from 780 to 2500 nm) of the mult...

0 Svetlana Illarionova, et al. ∙

research

∙ 05/12/2021

Object-Based Augmentation Improves Quality of Remote SensingSemantic Segmentation

Today deep convolutional neural networks (CNNs) push the limits for most...

8 Svetlana Illarionova, et al. ∙

research

∙ 04/11/2021

Dynamic Modeling of User Preferences for Stable Recommendations

In domains where users tend to develop long-term preferences that do not...

0 Oluwafemi Olaleke, et al. ∙

research

∙ 03/27/2021

Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds

In scientific computing and machine learning applications, matrices and ...

24 Alexander Novikov, et al. ∙

research

∙ 03/18/2021

Optimal soil sampling design based on the maxvol algorithm

Spatial soil sampling is an integral part of a soil survey aimed at crea...

0 Anna Petrovskaia, et al. ∙

research

∙ 03/15/2021

Meta-Solver for Neural Ordinary Differential Equations

A conventional approach to train neural ordinary differential equations ...

0 Julia Gusak, et al. ∙

research

∙ 02/16/2021

Solution of the Fokker-Planck equation by cross approximation method in the tensor train format

We propose the novel numerical scheme for solution of the multidimension...

0 Andrei Chertkov, et al. ∙

research

∙ 02/11/2021

Disentangled Representations from Non-Disentangled Models

Constructing disentangled representations is known to be a difficult tas...

6 Valentin Khrulkov, et al. ∙

research

∙ 02/08/2021

Functional Space Analysis of Local GAN Convergence

Recent work demonstrated the benefits of studying continuous-time dynami...

0 Valentin Khrulkov, et al. ∙

Ivan Oseledets

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