1 citations · 1 across the 6 of their papers we have counts for
6 papers
CLaST: Context-aware Contrastive VAE for Probabilistic Time Series Forecasting
Alexander Marusov, Dmitry Anikin, Petr Sokerin +3
Probabilistic forecasting models are widely used for time series forecasting in domains such as energy systems, finance, medicine, and transportation. In recent years, deep generat…
GENADA: efficient generative time series adversarial attack framework
Michael Baronov, Denis Vorobev, Margarita Rusanova +2
Deep learning models are widely used for time series analysis in domains such as healthcare, finance, energy systems, and environmental monitoring. However, these models remain vul…
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs
Maxim Zhelnin, Dmitry Redko, Daniil Volkov +8
Sequential recommendations (SR) with transformer-based architectures are widely adopted in real-world applications, where SR models require frequent retraining to adapt to ever-cha…
Concealed Adversarial attacks on neural networks for sequential data
Petr Sokerin, Dmitry Anikin, Sofia Krehova +1
The emergence of deep learning led to the broad usage of neural networks in the time series domain for various applications, including finance and medicine. While powerful, these m…
Looking around you: external information enhances representations for event sequences
Petr Sokerin, Maria Kovaleva, Ekaterina Boyarina +3
Representation learning produces models in different domains, such as store purchases, client transactions, and general people's behavior. However, such models for event sequences…
Portfolio Selection via Topological Data Analysis
Petr Sokerin, Kristian Kuznetsov, Elizaveta Makhneva +1
Portfolio management is an essential part of investment decision-making. However, traditional methods often fail to deliver reasonable performance. This problem stems from the inab…