8 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 4 cited
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…
cs.LG2020
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…
cs.LG2019★ 8 cited
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.…