604 citations · 2.5k across the 63 of their papers we have counts for
68 papers
Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
Ilya Burenko, Dmitry Vetrov
In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks. When modality imbalance is pronounced,…
UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model
Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov +2
This paper introduces UnDiff, a diffusion probabilistic model capable of solving various speech inverse tasks. Being once trained for speech waveform generation in an unconditional…
To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning
Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry Vetrov +1
Transfer learning and ensembling are two popular techniques for improving the performance and robustness of neural networks. Due to the high cost of pre-training, ensembles of mode…
Differentiable Rendering with Reparameterized Volume Sampling
Nikita Morozov, Denis Rakitin, Oleg Desheulin +2
In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of scene pictures. To generate a pixel of a novel view, it marc…
Star-Shaped Denoising Diffusion Probabilistic Models
Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin +4
Denoising Diffusion Probabilistic Models (DDPMs) provide the foundation for the recent breakthroughs in generative modeling. Their Markovian structure makes it difficult to define…
StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation
Aibek Alanov, Vadim Titov, Maksim Nakhodnov +1
Domain adaptation of GANs is a problem of fine-tuning GAN models pretrained on a large dataset (e.g. StyleGAN) to a specific domain with few samples (e.g. painting faces, sketches,…
Entropic Neural Optimal Transport via Diffusion Processes
Nikita Gushchin, Alexander Kolesov, Alexander Korotin +2
We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between continuous probability distributions which are access…
HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks
Aibek Alanov, Vadim Titov, Dmitry Vetrov
Domain adaptation framework of GANs has achieved great progress in recent years as a main successful approach of training contemporary GANs in the case of very limited training dat…
Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes
Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov +1
A fundamental property of deep learning normalization techniques, such as batch normalization, is making the pre-normalization parameters scale invariant. The intrinsic domain of s…
FFC-SE: Fast Fourier Convolution for Speech Enhancement
Ivan Shchekotov, Pavel Andreev, Oleg Ivanov +2
Fast Fourier convolution (FFC) is the recently proposed neural operator showing promising performance in several computer vision problems. The FFC operator allows employing large r…
HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement
Pavel Andreev, Aibek Alanov, Oleg Ivanov +1
Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we sho…
Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems
Evgeny Bobrov, Sergey Troshin, Nadezhda Chirkova +4
Channel decoding, channel detection, channel assessment, and resource management for wireless multiple-input multiple-output (MIMO) systems are all examples of problems where machi…
Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces
Kirill Struminsky, Artyom Gadetsky, Denis Rakitin +2
Structured latent variables allow incorporating meaningful prior knowledge into deep learning models. However, learning with such variables remains challenging because of their dis…
Automating Control of Overestimation Bias for Reinforcement Learning
Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin +3
Overestimation bias control techniques are used by the majority of high-performing off-policy reinforcement learning algorithms. However, most of these techniques rely on pre-defin…
Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study
Pavel Andreev, Alexander Fritzler, Dmitry Vetrov
Generative adversarial networks (GANs) have an enormous potential impact on digital content creation, e.g., photo-realistic digital avatars, semantic content editing, and quality e…
Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations
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. At the same time,…
On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay
Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova +2
Training neural networks with batch normalization and weight decay has become a common practice in recent years. In this work, we show that their combined use may result in a surpr…
Towards Practical Credit Assignment for Deep Reinforcement Learning
Vyacheslav Alipov, Riley Simmons-Edler, Nikita Putintsev +2
Credit assignment is a fundamental problem in reinforcement learning, the problem of measuring an action's influence on future rewards. Explicit credit assignment methods have the…
On Power Laws in Deep Ensembles
Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan +1
Ensembles of deep neural networks are known to achieve state-of-the-art performance in uncertainty estimation and lead to accuracy improvement. In this work, we focus on a classifi…
Involutive MCMC: a Unifying Framework
Kirill Neklyudov, Max Welling, Evgenii Egorov +1
Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. The field has developed a broad sp…
MARS: Masked Automatic Ranks Selection in Tensor Decompositions
Maxim Kodryan, Dmitry Kropotov, Dmitry Vetrov
Tensor decomposition methods have proven effective in various applications, including compression and acceleration of neural networks. At the same time, the problem of determining…
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Alexander Shekhovtsov, Viktor Yanush
Training neural networks with binary weights and activations is a challenging problem due to the lack of gradients and difficulty of optimization over discrete weights. Many succes…
Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones?
Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov
One of the generally accepted views of modern deep learning is that increasing the number of parameters usually leads to better quality. The two easiest ways to increase the number…
Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics
Arsenii Kuznetsov, Pavel Shvechikov, Alexander Grishin +1
The overestimation bias is one of the major impediments to accurate off-policy learning. This paper investigates a novel way to alleviate the overestimation bias in a continuous co…