6 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…
Batch Size or Negatives? A Selection Rule for Memory-Constrained Recommender Training
Artyom Sabitov, Daniil Volkov, Alexey Zaytsev
Large-scale neural recommender systems are typically trained with a softmax cross-entropy objective over the full item vocabulary. For a typical large number of possible items ,…
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…
Designing an attack-defense game: how to increase robustness of financial transaction models via a competition
Alexey Zaytsev, Maria Kovaleva, Alex Natekin +7
Banks routinely use neural networks to make decisions. While these models offer higher accuracy, they are susceptible to adversarial attacks, a risk often overlooked in the context…