Graph Neural Networks with Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data. - Alexander Kovalenko; Vitaliy Pozdnyakov; Ilya Makarov (CoRR, 2022)

SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes. - Maksim Golyadkin; Vitaliy Pozdnyakov; Leonid Zhukov; Ilya Makarov (Artif. Intell., 2023)

Time Series Generation with GANs for Momentum Effect Simulation on Moscow Stock Exchange. - Maksim Kazadaev; Vitaliy Pozdnyakov; Ilya Makarov (CIFEr, 2024)

SensorSCAN: Self-supervised learning and deep clustering for fault diagnosis in chemical processes (Abstract Reprint). - Maksim Golyadkin; Vitaliy Pozdnyakov; Leonid Zhukov; Ilya Makarov (IJCAI, 2024)

AADMIP: Adversarial Attacks and Defenses Modeling in Industrial Processes. - Vitaliy Pozdnyakov; Aleksandr Kovalenko; Ilya Makarov; Mikhail Drobyshevskiy; Kirill Lukyanov (IJCAI, 2024)

Heterogeneous Graph Attention Networks for Scheduling in Cloud Manufacturing and Logistics. - Dmitrii Fomin; Ilya Makarov; Mariia Voronina; Anna Strimovskaya; Vitaliy Pozdnyakov (IEEE Access, 2024)

Graph Neural Networks With Trainable Adjacency Matrices for Fault Diagnosis on Multivariate Sensor Data. - Aleksandr Kovalenko; Vitaliy Pozdnyakov; Ilya Makarov (IEEE Access, 2024)

Adversarial Attacks and Defenses in Automated Control Systems: A Comprehensive Benchmark. - Vitaliy Pozdnyakov; Aleksandr Kovalenko; Ilya Makarov; Mikhail Drobyshevskiy; Kirill Lukyanov (CoRR, 2024)

Binary Cumulative Encoding meets Time Series Forecasting. - Andrei Chernov; Vitaliy Pozdnyakov; Ilya Makarov (CoRR, 2025)