27 citations · 34 across the 9 of their papers we have counts for
10 papers
Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
Harry Proshian, Nikita Severin, Sergey Nikolenko +5
Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recomm…
Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin +2
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their applica…
LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients
Egor Fadeev, Dzhambulat Mollaev, Aleksei Shestov +6
Learning clients embeddings from sequences of their historic communications is central to financial applications. While large language models (LLMs) offer general world knowledge,…
LLM4ES: Learning User Embeddings from Event Sequences via Large Language Models
Aleksei Shestov, Omar Zoloev, Maksim Makarenko +4
This paper presents LLM4ES, a novel framework that exploits large pre-trained language models (LLMs) to derive user embeddings from event sequences. Event sequences are transformed…
Multimodal Banking Dataset: Understanding Client Needs through Event Sequences
Dzhambulat Mollaev, Alexander Kostin, Maria Postnova +4
Financial organizations collect a huge amount of temporal (sequential) data about clients, which is typically collected from multiple sources (modalities). Despite the urgent pract…
ESQA: Event Sequences Question Answering
Irina Abdullaeva, Andrei Filatov, Mikhail Orlov +6
Event sequences (ESs) arise in many practical domains including finance, retail, social networks, and healthcare. In the context of machine learning, event sequences can be seen as…
Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov +7
In today's world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categor…
Continuous-time convolutions model of event sequences
Vladislav Zhuzhel, Vsevolod Grabar, Galina Boeva +9
Event sequences often emerge in data mining. Modeling these sequences presents two main challenges: methodological and computational. Methodologically, event sequences are non-unif…
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…
CoLES: Contrastive Learning for Event Sequences with Self-Supervision
Dmitrii Babaev, Ivan Kireev, Nikita Ovsov +4
We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw…