Maxim Panov

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Skoltech

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference

In this contribution, we propose a new computationally efficient method ...
02/27/2020 ∙ by Achille Thin, et al. ∙ 76 ∙ share

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NCVis: Noise Contrastive Approach for Scalable Visualization

Modern methods for data visualization via dimensionality reduction, such...
01/30/2020 ∙ by Aleksandr Artemenkov, et al. ∙ 14 ∙ share

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Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based o...
10/14/2017 ∙ by Nikita Mokrov, et al. ∙ 0 ∙ share

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Sparse Group Inductive Matrix Completion

We consider the problem of inductive matrix completion under the assumpt...
04/27/2018 ∙ by Ivan Nazarov, et al. ∙ 0 ∙ share

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Dropout-based Active Learning for Regression

Active learning is relevant and challenging for high-dimensional regress...
06/26/2018 ∙ by Evgenii Tsymbalov, et al. ∙ 0 ∙ share

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Constructing Graph Node Embeddings via Discrimination of Similarity Distributions

The problem of unsupervised learning node embeddings in graphs is one of...
10/06/2018 ∙ by Stanislav Tsepa, et al. ∙ 0 ∙ share

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GTApprox: surrogate modeling for industrial design

We describe GTApprox - a new tool for medium-scale surrogate modeling in...
09/05/2016 ∙ by Mikhail Belyaev, et al. ∙ 0 ∙ share

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Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Active learning methods for neural networks are usually based on greedy ...
02/27/2019 ∙ by Evgenii Tsymbalov, et al. ∙ 0 ∙ share

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Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension

The existing approaches to intrinsic dimension estimation usually are no...
04/12/2019 ∙ by Marina Gomtsyan, et al. ∙ 0 ∙ share

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Accuracy of Gaussian approximation in nonparametric Bernstein – von Mises Theorem

The prominent Bernstein – von Mises (BvM) result claims that the posteri...
10/14/2019 ∙ by Vladimir Spokoiny, et al. ∙ 0 ∙ share

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Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data

Financial institutions obtain enormous amounts of data about user transa...
01/23/2020 ∙ by Valentina Shumovskaia, et al. ∙ 0 ∙ share

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Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles

Modern machine learning models usually do not extrapolate well, i.e., th...
03/06/2020 ∙ by Evgenii Tsymbalov, et al. ∙ 0 ∙ share

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Maxim Panov

Featured Co-authors

Evgeny Burnaev

Dmitry Vetrov

Eric Moulines

Alain Durmus

Mikhail Belyaev

Vladimir Spokoiny

Paul M. Thompson

Neda Jahanshad

Dmitry Yarotsky

Ivan Nazarov

Boris A. Gutman

MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference

NCVis: Noise Contrastive Approach for Scalable Visualization

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

Sparse Group Inductive Matrix Completion

Dropout-based Active Learning for Regression

Constructing Graph Node Embeddings via Discrimination of Similarity Distributions

GTApprox: surrogate modeling for industrial design

Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning

Geometry-Aware Maximum Likelihood Estimation of Intrinsic Dimension

Accuracy of Gaussian approximation in nonparametric Bernstein – von Mises Theorem

Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data

Dropout Strikes Back: Improved Uncertainty Estimation via Diversity Sampled Implicit Ensembles