Alexey Zaytsev

Sequence embeddings help to identify fraudulent cases in healthcare insurance

2019 · arXiv (Cornell University)

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. It has been estimated that roughly $10$ percent of the …

Adversarial Attacks on Deep Models for Financial Transaction Records

2021 · arXiv (Cornell University)

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 the NLP community, posing a challenge due to their tremendous number …

A Differentiable Language Model Adversarial Attack on Text Classifiers

2022 · IEEE Access

Transformer models play a crucial role in state of the art solutions to problems arising in the field of natural language processing (NLP). They have billions of parameters and are typically considered as black boxes. …

Embedded Ensembles: Infinite Width Limit and Operating Regimes

2022 · arXiv (Cornell University)

A memory efficient approach to ensembling neural networks is to share most weights among the ensembled models by means of a single reference network. We refer to this strategy as Embedded Ensembling (EE); its particular …

InDiD: Instant Disorder Detection via Representation Learning

2021 · arXiv (Cornell University)

For sequential data, a change point is a moment of abrupt regime switch in data streams. Such changes appear in different scenarios, including simpler data from sensors and more challenging video surveillance data. We need …