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no code implementations • ICML 2020 • Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.

no code implementations • 1 Jun 2023 • Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov, Nicholas Babaev, Dmitry Vetrov

Being once trained for speech waveform generation in an unconditional manner, it can be adapted to different tasks including degradation inversion, neural vocoding, and source separation.

no code implementations • 6 Mar 2023 • Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry Vetrov, Ekaterina Lobacheva

Transfer learning and ensembling are two popular techniques for improving the performance and robustness of neural networks.

1 code implementation • 21 Feb 2023 • Nikita Morozov, Denis Rakitin, Oleg Desheulin, Dmitry Vetrov, Kirill Struminsky

To generate a pixel of a novel view, it marches a ray through the pixel and computes a weighted sum of radiance emitted from a dense set of ray points.

no code implementations • 10 Feb 2023 • Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Aibek Alanov, Dmitry Vetrov

Methods based on Denoising Diffusion Probabilistic Models (DDPM) became a ubiquitous tool in generative modeling.

no code implementations • 20 Dec 2022 • Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov

Particularly, we show there exist directions in StyleSpace (StyleDomain directions) that are sufficient for adapting to similar domains and they can be reduced further.

no code implementations • 2 Nov 2022 • Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry Vetrov, Evgeny Burnaev

Our algorithm is based on the saddle point reformulation of the dynamic version of EOT which is known as the Schr\"odinger Bridge problem.

1 code implementation • 17 Oct 2022 • Aibek Alanov, Vadim Titov, Dmitry Vetrov

We apply this parameterization to the state-of-art domain adaptation methods and show that it has almost the same expressiveness as the full parameter space.

1 code implementation • 8 Sep 2022 • Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry Vetrov

In this work, we investigate the properties of training scale-invariant neural networks directly on the sphere using a fixed ELR.

1 code implementation • 6 Apr 2022 • Ivan Shchekotov, Pavel Andreev, Oleg Ivanov, Aibek Alanov, Dmitry Vetrov

The FFC operator allows employing large receptive field operations within early layers of the neural network.

2 code implementations • 24 Mar 2022 • Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry Vetrov

Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models.

no code implementations • 29 Dec 2021 • Evgeny Bobrov, Sergey Troshin, Nadezhda Chirkova, Ekaterina Lobacheva, Sviatoslav Panchenko, Dmitry Vetrov, Dmitry Kropotov

Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machine learning (ML) can be successfully applied.

no code implementations • 23 Nov 2021 • Evgeny Bobrov, Alexander Markov, Sviatoslav Panchenko, Dmitry Vetrov

In this paper, we consider the problem of finding precoding matrices with high spectral efficiency (SE) using variational autoencoder (VAE).

no code implementations • NeurIPS 2021 • Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry Vetrov

Structured latent variables allow incorporating meaningful prior knowledge into deep learning models.

no code implementations • 26 Oct 2021 • Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin, Arsenii Ashukha, Artur Kadurin, Dmitry Vetrov

Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms.

no code implementations • 31 Aug 2021 • Pavel Andreev, Alexander Fritzler, Dmitry Vetrov

While quantization is well established for discriminative models, the performance of modern quantization techniques in application to GANs remains unclear.

1 code implementation • NeurIPS 2021 • Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry Vetrov

Training neural networks with batch normalization and weight decay has become a common practice in recent years.

no code implementations • 15 Jun 2021 • Arsenii Ashukha, Andrei Atanov, Dmitry Vetrov

Averaging predictions over a set of models -- an ensemble -- is widely used to improve predictive performance and uncertainty estimation of deep learning models.

no code implementations • 8 Jun 2021 • Vyacheslav Alipov, Riley Simmons-Edler, Nikita Putintsev, Pavel Kalinin, Dmitry Vetrov

Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards.

1 code implementation • NeurIPS 2020 • Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry Vetrov

Ensembles of deep neural networks are known to achieve state-of-the-art performance in uncertainty estimation and lead to accuracy improvement.

no code implementations • 30 Jun 2020 • Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation.

1 code implementation • 18 Jun 2020 • Maxim Kodryan, Dmitry Kropotov, Dmitry Vetrov

Tensor decomposition methods have proven effective in various applications, including compression and acceleration of neural networks.

no code implementations • 14 May 2020 • Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov

In this work, we consider a fixed memory budget setting, and investigate, what is more effective: to train a single wide network, or to perform a memory split -- to train an ensemble of several thinner networks, with the same total number of parameters?

9 code implementations • ICML 2020 • Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov

The overestimation bias is one of the major impediments to accurate off-policy learning.

1 code implementation • 4 Mar 2020 • Daniil Polykovskiy, Dmitry Vetrov

Variational autoencoders are prominent generative models for modeling discrete data.

1 code implementation • ICLR Workshop DeepDiffEq 2019 • Viktor Oganesyan, Alexandra Volokhova, Dmitry Vetrov

Stochastic regularization of neural networks (e. g. dropout) is a wide-spread technique in deep learning that allows for better generalization.

1 code implementation • 21 Feb 2020 • Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Vetrov

Test-time data augmentation$-$averaging the predictions of a machine learning model across multiple augmented samples of data$-$is a widely used technique that improves the predictive performance.

2 code implementations • ICLR 2020 • Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov

Uncertainty estimation and ensembling methods go hand-in-hand.

no code implementations • ICLR 2020 • Diego Granziol, Timur Garipov, Dmitry Vetrov, Stefan Zohren, Stephen Roberts, Andrew Gordon Wilson

This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.

no code implementations • ICLR 2020 • Aibek Alanov, Max Kochurov, Artem Sobolev, Daniil Yashkov, Dmitry Vetrov

We show that it takes the best properties of VAE and GAN objectives.

1 code implementation • 22 Nov 2019 • Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry Vetrov

Learning models with discrete latent variables using stochastic gradient descent remains a challenge due to the high variance of gradient estimates.

no code implementations • 13 Nov 2019 • Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry Vetrov

Recently, a lot of techniques were developed to sparsify the weights of neural networks and to remove networks' structure units, e. g. neurons.

1 code implementation • NeurIPS 2019 • Maksim Kuznetsov, Daniil Polykovskiy, Dmitry Vetrov, Alexander Zhebrak

Previous works show that the richer family of prior distributions may help to avoid the mode collapse problem in GANs and to improve the evidence lower bound in VAEs.

no code implementations • pproximateinference AABI Symposium 2019 • Iuliia Molchanova, Dmitry Molchanov, Novi Quadrianto, Dmitry Vetrov

In this work we construct flexible joint distributions from low-dimensional conditional semi-implicit distributions.

no code implementations • WS 2019 • Maxim Kodryan, Artem Grachev, Dmitry Ignatov, Dmitry Vetrov

Reduction of the number of parameters is one of the most important goals in Deep Learning.

1 code implementation • 17 Jul 2019 • Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty.

1 code implementation • NeurIPS 2019 • Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov

For any implicit probabilistic model and a target distribution represented by a set of samples, implicit Metropolis-Hastings operates by learning a discriminator to estimate the density-ratio and then generating a chain of samples.

1 code implementation • NeurIPS 2019 • Artem Sobolev, Dmitry Vetrov

Variational Inference is a powerful tool in the Bayesian modeling toolkit, however, its effectiveness is determined by the expressivity of the utilized variational distributions in terms of their ability to match the true posterior distribution.

3 code implementations • 1 May 2019 • Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry Vetrov

This paper proposes a semi-conditional normalizing flow model for semi-supervised learning.

no code implementations • 9 Apr 2019 • Aibek Alanov, Max Kochurov, Denis Volkhonskiy, Daniil Yashkov, Evgeny Burnaev, Dmitry Vetrov

We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism.

7 code implementations • NeurIPS 2019 • Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.

1 code implementation • NIPS Workshop CDNNRIA 2018 • Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov

Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e. g. neurons.

no code implementations • 11 Nov 2018 • Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov

Neural Network is a powerful Machine Learning tool that shows outstanding performance in Computer Vision, Natural Language Processing, and Artificial Intelligence.

1 code implementation • 1 Nov 2018 • Valery Kharitonov, Dmitry Molchanov, Dmitry Vetrov

We study the Automatic Relevance Determination procedure applied to deep neural networks.

3 code implementations • EMNLP 2018 • Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov

In natural language processing, a lot of the tasks are successfully solved with recurrent neural networks, but such models have a huge number of parameters.

no code implementations • ICLR 2019 • Kirill Neklyudov, Evgenii Egorov, Pavel Shvechikov, Dmitry Vetrov

From this point of view, the problem of constructing a sampler can be reduced to the question - how to choose a proposal for the MH algorithm?

2 code implementations • ICLR 2019 • Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry Vetrov, Max Welling

Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution.

no code implementations • ICLR 2019 • Aibek Alanov, Max Kochurov, Daniil Yashkov, Dmitry Vetrov

We experimentally demonstrate that our model generates samples and reconstructions of quality competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and achieves good quantitative results on CIFAR10.

no code implementations • 5 Oct 2018 • Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov

Unlike discriminator-based and kernel-based approaches to implicit variational inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically exact.

1 code implementation • ACL 2018 • Artyom Gadetsky, Ilya Yakubovskiy, Dmitry Vetrov

We explore recently introduced definition modeling technique that provided the tool for evaluation of different distributed vector representations of words through modeling dictionary definitions of words.

3 code implementations • ICLR 2019 • Oleg Ivanov, Michael Figurnov, Dmitry Vetrov

We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot".

14 code implementations • 14 Mar 2018 • Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence.

Ranked #75 on Image Classification on CIFAR-100 (using extra training data)

2 code implementations • ICLR 2019 • Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging.

10 code implementations • NeurIPS 2018 • Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson

The loss functions of deep neural networks are complex and their geometric properties are not well understood.

no code implementations • 20 Feb 2018 • Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

In industrial machine learning pipelines, data often arrive in parts.

1 code implementation • 13 Feb 2018 • Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry Vetrov

In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation.

no code implementations • 1 Dec 2017 • Michael Figurnov, Artem Sobolev, Dmitry Vetrov

We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs.

2 code implementations • 31 Jul 2017 • Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov

Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights.

5 code implementations • NeurIPS 2017 • Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e. g. removes neurons and/or convolutional channels in CNNs.

14 code implementations • ICML 2017 • Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout.

1 code implementation • CVPR 2017 • Michael Figurnov, Maxwell D. Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov

This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.

no code implementations • 28 Nov 2016 • Michael Figurnov, Kirill Struminsky, Dmitry Vetrov

Variational inference is a powerful tool for approximate inference.

2 code implementations • 10 Nov 2016 • Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry Vetrov

Convolutional neural networks excel in image recognition tasks, but this comes at the cost of high computational and memory complexity.

1 code implementation • 5 Sep 2016 • Mikhail Belyaev, Evgeny Burnaev, Ermek Kapushev, Maxim Panov, Pavel Prikhodko, Dmitry Vetrov, Dmitry Yarotsky

We describe GTApprox - a new tool for medium-scale surrogate modeling in industrial design.

no code implementations • ICCV 2015 • Alexander Kirillov, Bogdan Savchynskyy, Dmitrij Schlesinger, Dmitry Vetrov, Carsten Rother

We consider the task of finding M-best diverse solutions in a graphical model.

4 code implementations • NeurIPS 2015 • Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov

Deep neural networks currently demonstrate state-of-the-art performance in several domains.

Ranked #73 on Image Classification on MNIST

2 code implementations • NeurIPS 2016 • Michael Figurnov, Aijan Ibraimova, Dmitry Vetrov, Pushmeet Kohli

We propose a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such as mobile phones.

3 code implementations • 25 Feb 2015 • Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov

Recently proposed Skip-gram model is a powerful method for learning high-dimensional word representations that capture rich semantic relationships between words.

1 code implementation • 15 Jan 2015 • Anton Osokin, Dmitry Vetrov

In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem.

no code implementations • 23 Jun 2014 • Roman Shapovalov, Dmitry Vetrov, Anton Osokin, Pushmeet Kohli

Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training.

no code implementations • CVPR 2013 • Roman Shapovalov, Dmitry Vetrov, Pushmeet Kohli

Experimental results show that the spatial dependencies learned by our method significantly improve the accuracy of segmentation.

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