32 citations · 94 across the 30 of their papers we have counts for
14 papers · 1 filter
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust
Leonid Erlygin, Vladimir Zholobov, Valeriia Baklanova +2
Machine learning models play a vital role in time series forecasting. These models, however, often overlook an important element: point uncertainty estimates. Incorporating these e…
Effective training-time stacking for ensembling of deep neural networks
Polina Proscura, Alexey Zaytsev
Ensembling is a popular and effective method for improving machine learning (ML) models. It proves its value not only in classical ML but also for deep learning. Ensembles enhance…
Transfer learning for ensembles: reducing computation time and keeping the diversity
Ilya Shashkov, Nikita Balabin, Evgeny Burnaev +1
Transferring a deep neural network trained on one problem to another requires only a small amount of data and little additional computation time. The same behaviour holds for ensem…
Towards OOD Detection in Graph Classification from Uncertainty Estimation Perspective
Gleb Bazhenov, Sergei Ivanov, Maxim Panov +2
The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore…
Adversarial Attacks on Deep Models for Financial Transaction Records
Ivan Fursov, Matvey Morozov, Nina Kaploukhaya +7
Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in…
COHORTNEY: Non-Parametric Clustering of Event Sequences
Vladislav Zhuzhel, Rodrigo Rivera-Castro, Nina Kaploukhaya +3
Cohort analysis is a pervasive activity in web analytics. One divides users into groups according to specific criteria and tracks their behavior over time. Despite its extensive us…
Unsupervised anomaly detection for discrete sequence healthcare data
Victoria Snorovikhina, Alexey Zaytsev
Fraud in healthcare is widespread, as doctors could prescribe unnecessary treatments to increase bills. Insurance companies want to detect these anomalous fraudulent bills and redu…
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
I. Fursov, A. Zaytsev, N. Kluchnikov +2
An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of…
Recurrent Convolutional Neural Networks help to predict location of Earthquakes
Roman Kail, Alexey Zaytsev, Evgeny Burnaev
We examine the applicability of modern neural network architectures to the midterm prediction of earthquakes. Our data-based classification model aims to predict if an earthquake w…
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world
Ivan Fursov, Alexey Zaytsev, Nikita Kluchnikov +2
Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literatur…
Sequence embeddings help to identify fraudulent cases in healthcare insurance
I. Fursov, A. Zaytsev, R. Khasyanov +2
Fraud causes substantial costs and losses for companies and clients in the finance and insurance industries. Examples are fraudulent credit card transactions or fraudulent claims.…
Application of Machine Learning to accidents detection at directional drilling
Ekaterina Gurina, Nikita Klyuchnikov, Alexey Zaytsev +5
We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling. The algorithm is based on machine learning. It compares the real-time drilli…
Real-time data-driven detection of the rock type alteration during a directional drilling
Evgenya Romanenkova, Alexey Zaytsev, Nikita Klyuchnikov +8
During the directional drilling, a bit may sometimes go to a nonproductive rock layer due to the gap about 20m between the bit and high-fidelity rock type sensors. The only way to…
Data-driven model for the identification of the rock type at a drilling bit
Nikita Klyuchnikov, Alexey Zaytsev, Arseniy Gruzdev +12
Directional oil well drilling requires high precision of the wellbore positioning inside the productive area. However, due to specifics of engineering design, sensors that explicit…