1 citations · 1 across the 6 of their papers we have counts for
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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…