10 citations · 20 across the 9 of their papers we have counts for
10 papers · 1 filter
Scalable computation of prediction intervals for neural networks via matrix sketching
Alexander Fishkov, Maxim Panov
Accounting for the uncertainty in the predictions of modern neural networks is a challenging and important task in many domains. Existing algorithms for uncertainty estimation requ…
Embedded Ensembles: Infinite Width Limit and Operating Regimes
Maksim Velikanov, Roman Kail, Ivan Anokhin +4
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 E…
Monte Carlo Variational Auto-Encoders
Achille Thin, Nikita Kotelevskii, Arnaud Doucet +3
Variational auto-encoders (VAE) are popular deep latent variable models which are trained by maximizing an Evidence Lower Bound (ELBO). To obtain tighter ELBO and hence better vari…
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…