3 citations · 7 across the 11 of their papers we have counts for
11 papers
Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors
Elizaveta Kovtun, Matvey Konovalov, Andrey Sakhovskiy +1
Tactile sensing is an essential modality for robots performing contact-rich, dexterous manipulation, particularly under visual occlusion. While pre-trained image encoders are stand…
LLM Agents Factory: Retrieval of Domain-Specific LLM Agents
Vitalii Belov, Artyom Sosedka, Andrey Sakhovskiy +3
Large language model (LLM) agents improve task performance by decomposing problems into role-specialized behaviors. However, their practical deployment is often limited by the comp…
Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences
Artem Sakhno, Ivan Sergeev, Alexey Shestov +5
Industrial financial systems operate on temporal event sequences such as transactions, user actions, and system logs. While recent research emphasizes representation learning and l…
PINE: Pipeline for Important Node Exploration in Attributed Networks
Elizaveta Kovtun, Maksim Makarenko, Natalia Semenova +2
A graph with semantically attributed nodes are a common data structure in a wide range of domains. It could be interlinked web data or citation networks of scientific publications.…
Towards Foundation Time Series Model: To Synthesize Or Not To Synthesize?
Kseniia Kuvshinova, Olga Tsymboi, Alina Kostromina +2
The industry is rich in cases when we are required to make forecasting for large amounts of time series at once. However, we might be in a situation where we can not afford to trai…
Hiding Backdoors within Event Sequence Data via Poisoning Attacks
Alina Ermilova, Elizaveta Kovtun, Dmitry Berestnev +1
The financial industry relies on deep learning models for making important decisions. This adoption brings new danger, as deep black-box models are known to be vulnerable to advers…
The Battle of Information Representations: Comparing Sentiment and Semantic Features for Forecasting Market Trends
Andrei Zaichenko, Aleksei Kazakov, Elizaveta Kovtun +1
The study of the stock market with the attraction of machine learning approaches is a major direction for revealing hidden market regularities. This knowledge contributes to a prof…
Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention
Elizaveta Kovtun, Galina Boeva, Andrey Shulga +1
Most user-related data can be represented as a sequence of events associated with a timestamp and a collection of categorical labels. For example, the purchased basket of goods and…
Relationships between patenting trends and research activity for green energy technologies
Regina Tuganova, Anna Permyakova, Anna Kuznetsova +5
Green technology is viewed as a means of creating a sustainable society and a catalyst for sustainable development by the global community. It is responsible for both the potential…
New drugs and stock market: how to predict pharma market reaction to clinical trial announcements
Semen Budennyy, Alexey Kazakov, Elizaveta Kovtun +1
Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announceme…
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…