8 citations · 13 across the 5 of their papers we have counts for
5 papers
A Differentiable Language Model Adversarial Attack on Text Classifiers
Ivan Fursov, Alexey Zaytsev, Pavel Burnyshev +5
Robustness of huge Transformer-based models for natural language processing is an important issue due to their capabilities and wide adoption. One way to understand and improve rob…
Adversarial Attacks on Deep Models for Financial Transaction Records
Ivan Fursov, Matvey Morozov, Nina Kaploukhaya +7
Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in…
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
I. Fursov, A. Zaytsev, N. Kluchnikov +2
An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of…
Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world
Ivan Fursov, Alexey Zaytsev, Nikita Kluchnikov +2
Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literatur…
Sequence embeddings help to identify fraudulent cases in healthcare insurance
I. Fursov, A. Zaytsev, R. Khasyanov +2
Fraud causes substantial costs and losses for companies and clients in the finance and insurance industries. Examples are fraudulent credit card transactions or fraudulent claims.…