activity
20192022
most citedE.T.-RNN: Applying Deep Learning to Credit Loan Applications

91 citations · 127 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2022★ 1 cited

Insights From the NeurIPS 2021 NetHack Challenge

Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26

In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…

cs.LG2021★ 1 cited

Temporal Graph Network Embedding with Causal Anonymous Walks Representations

Ilya Makarov, Andrey Savchenko, Arseny Korovko +4

Many tasks in graph machine learning, such as link prediction and node classification, are typically solved by using representation learning, in which each node or edge in the netw…

cs.LG2021

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…

cs.LG2020★ 27 cited

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…

cs.SI2019★ 7 cited

Large-Scale Parallel Matching of Social Network Profiles

Alexander Panchenko, Dmitry Babaev, Sergei Obiedkov

A profile matching algorithm takes as input a user profile of one social network and returns, if existing, the profile of the same person in another social network. Such methods ha…

cs.LG2019★ 91 cited

E.T.-RNN: Applying Deep Learning to Credit Loan Applications

Dmitrii Babaev, Maxim Savchenko, Alexander Tuzhilin +1

In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational d…