43 citations · 153 across the 27 of their papers we have counts for
4 papers · 2 filters
EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data
Ivan Sukharev, Valentina Shumovskaia, Kirill Fedyanin +2
In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based…
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain +4
In this contribution, we propose a new computationally efficient method to combine Variational Inference (VI) with Markov Chain Monte Carlo (MCMC). This approach can be used with g…
NCVis: Noise Contrastive Approach for Scalable Visualization
Aleksandr Artemenkov, Maxim Panov
Modern methods for data visualization via dimensionality reduction, such as t-SNE, usually have performance issues that prohibit their application to large amounts of high-dimensio…
Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev +2
Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this w…