4 papers
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…
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…
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…