
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 ...
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NCVis: Noise Contrastive Approach for Scalable Visualization
Modern methods for data visualization via dimensionality reduction, such...
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Simultaneous Matrix Diagonalization for Structural Brain Networks Classification
This paper considers the problem of brain disease classification based o...
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Sparse Group Inductive Matrix Completion
We consider the problem of inductive matrix completion under the assumpt...
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Dropoutbased Active Learning for Regression
Active learning is relevant and challenging for highdimensional regress...
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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...
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GTApprox: surrogate modeling for industrial design
We describe GTApprox  a new tool for mediumscale surrogate modeling in...
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Deeper Connections between Neural Networks and Gaussian Processes Speedup Active Learning
Active learning methods for neural networks are usually based on greedy ...
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GeometryAware Maximum Likelihood Estimation of Intrinsic Dimension
The existing approaches to intrinsic dimension estimation usually are no...
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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...
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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...
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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...
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Maxim Panov
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