Alexey Zaytsev

research

∙ 09/12/2023

Long-term drought prediction using deep neural networks based on geospatial weather data

The accurate prediction of drought probability in specific regions is cr...

0 Vsevolod Grabar, et al. ∙

research

∙ 09/10/2023

Machine Translation Models Stand Strong in the Face of Adversarial Attacks

Adversarial attacks expose vulnerabilities of deep learning models by in...

0 Pavel Burnyshev, et al. ∙

research

∙ 09/09/2023

Correcting sampling biases via importancereweighting for spatial modeling

In machine learning models, the estimation of errors is often complex du...

0 Boris Prokhorov, et al. ∙

research

∙ 08/22/2023

Designing an attack-defense game: how to increase robustness of financial transaction models via a competition

Given the escalating risks of malicious attacks in the finance sector an...

0 Alexey Zaytsev, et al. ∙

research

∙ 08/22/2023

Uncertainty Estimation of Transformers' Predictions via Topological Analysis of the Attention Matrices

Determining the degree of confidence of deep learning model in its predi...

0 Elizaveta Kostenok, et al. ∙

research

∙ 08/20/2023

Hiding Backdoors within Event Sequence Data via Poisoning Attacks

The financial industry relies on deep learning models for making importa...

0 Elizaveta Kovtun, et al. ∙

research

∙ 08/15/2023

Portfolio Selection via Topological Data Analysis

Portfolio management is an essential part of investment decision-making....

0 Petr Sokerin, et al. ∙

research

∙ 03/01/2023

Label Attention Network for sequential multi-label classification

Multi-label classification is a natural problem statement for sequential...

1 Elizaveta Kovtun, et al. ∙

research

∙ 02/13/2023

Continuous-time convolutions model of event sequences

Massive samples of event sequences data occur in various domains, includ...

6 Vladislav Zhuzhel, et al. ∙

research

∙ 02/06/2023

Uncertainty estimation for time series forecasting via Gaussian process regression surrogates

Machine learning models are widely used to solve real-world problems in ...

0 Leonid Erlygin, et al. ∙

research

∙ 12/29/2022

Unsupervised construction of representations for oil wells via Transformers

Determining and predicting reservoir formation properties for newly dril...

0 Alina Rogulina, et al. ∙

research

∙ 09/28/2022

Non-contrastive approaches to similarity learning: positive examples are all you need

The similarity learning problem in the oil & gas industry aims to constr...

0 Alexander Marusov, et al. ∙

research

∙ 09/26/2022

Self-supervised similarity models based on well-logging data

Adopting data-based approaches leads to model improvement in numerous Oi...

0 Sergey Egorov, et al. ∙

research

∙ 09/05/2022

ScaleFace: Uncertainty-aware Deep Metric Learning

The performance of modern deep learning-based systems dramatically depen...

0 Roman Kail, et al. ∙

research

∙ 08/31/2022

QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise

There is a constant need for high-performing and computationally efficie...

0 Egor Shvetsov, et al. ∙

research

∙ 06/27/2022

Effective training-time stacking for ensembling of deep neural networks

Ensembling is a popular and effective method for improving machine learn...

0 Polina Proscura, et al. ∙

research

∙ 06/27/2022

Transfer learning for ensembles: reducing computation time and keeping the diversity

Transferring a deep neural network trained on one problem to another req...

0 Ilya Shashkov, et al. ∙

research

∙ 04/18/2022

Usage of specific attention improves change point detection

The change point is a moment of an abrupt alteration in the data distrib...

0 Anna Dmitrienko, et al. ∙

research

∙ 04/15/2022

Deep learning model solves change point detection for multiple change types

A change points detection aims to catch an abrupt disorder in data distr...

0 Alexander Stepikin, et al. ∙

research

∙ 02/24/2022

Embedded Ensembles: Infinite Width Limit and Operating Regimes

A memory efficient approach to ensembling neural networks is to share mo...

0 Maksim Velikanov, et al. ∙

research

∙ 02/11/2022

Similarity learning for wells based on logging data

One of the first steps during the investigation of geological objects is...

12 Evgenia Romanenkova, et al. ∙

research

∙ 07/23/2021

A Differentiable Language Model Adversarial Attack on Text Classifiers

Robustness of huge Transformer-based models for natural language process...

0 Ivan Fursov, et al. ∙

research

∙ 06/15/2021

Adversarial Attacks on Deep Models for Financial Transaction Records

Machine learning models using transaction records as inputs are popular ...

0 Ivan Fursov, et al. ∙

research

∙ 06/04/2021

Principled change point detection via representation learning

Change points are abrupt alterations in the distribution of sequential d...

0 Evgenia Romanenkova, et al. ∙

research

∙ 04/03/2021

COHORTNEY: Deep Clustering for Heterogeneous Event Sequences

There is emerging attention towards working with event sequences. In par...

0 Vladislav Zhuzhel, et al. ∙

research

∙ 07/20/2020

Unsupervised anomaly detection for discrete sequence healthcare data

Fraud in healthcare is widespread, as doctors could prescribe unnecessar...

0 Victoria Snorovikhina, et al. ∙

research

∙ 04/20/2020

Recurrent Convolutional Neural Networks help to predict location of Earthquakes

We examine the applicability of modern neural network architectures to t...

0 Roman Kail, et al. ∙

research

∙ 03/09/2020

Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world

An adversarial attack paradigm explores various scenarios for vulnerabil...

0 Ivan Fursov, et al. ∙

research

∙ 06/06/2019

Failures detection at directional drilling using real-time analogues search

One of the main challenges in the construction of oil and gas wells is t...

0 Ekaterina Gurina, et al. ∙

research

∙ 03/27/2019

Real-time data-driven detection of the rock type alteration during a directional drilling

During the directional drilling, a bit may sometimes go to a nonproducti...

0 Evgenya Romanenkova, et al. ∙

research

∙ 02/19/2019

Multifidelity Bayesian Optimization for Binomial Output

The key idea of Bayesian optimization is replacing an expensive target f...

0 Leonid Matyushin, et al. ∙

research

∙ 06/08/2018

Data-driven model for the identification of the rock type at a drilling bit

In order to bridge the gap of more than 15m between the drilling bit and...

0 Nikita Klyuchnikov, et al. ∙

research

∙ 07/12/2017

Large Scale Variable Fidelity Surrogate Modeling

Engineers widely use Gaussian process regression framework to construct ...

0 Evgeny Burnaev, et al. ∙

research

∙ 10/21/2016

Minimax Error of Interpolation and Optimal Design of Experiments for Variable Fidelity Data

Engineering problems often involve data sources of variable fidelity wit...

0 Alexey Zaytsev, et al. ∙

Alexey Zaytsev

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