One Approach to Fuzzy Expert Systems Construction. - Dmitry Kropotov; Dmitry P. Vetrov (ICEIS (2), 2003)

Data Dependent Classifier Fusion for Construction of Stable Effective Algorithms. - Dmitry P. Vetrov; Dmitry Kropotov (ICPR (1), 2004)

An Algorithm for Rule Generation in Fuzzy Expert Systems. - Dmitry Kropotov; Dmitry P. Vetrov (ICPR (1), 2004)

The Use of Bayesian Framework for Kernel Selection in Vector Machines Classifiers. - Dmitry Kropotov; Nikita Ptashko; Dmitry P. Vetrov (CIARP, 2005)

The Use of Stability Principle for Kernel Determination in Relevance Vector Machines. - Dmitry Kropotov; Dmitry P. Vetrov; Nikita Ptashko; Oleg Vasiliev (ICONIP (1), 2006)

On Kernel Selection in Relevance Vector Machines Using Stability Principle. - Dmitry Kropotov; Nikita Ptashko; Oleg Vasiliev; Dmitry P. Vetrov (ICPR (4), 2006)

Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization. - Ludmila I. Kuncheva; Dmitry P. Vetrov (IEEE Trans. Pattern Anal. Mach. Intell., 2006)

On one method of non-diagonal regularization in sparse Bayesian learning. - Dmitry Kropotov; Dmitry P. Vetrov (ICML, 2007)

Fuzzy Rules Generation Method for Pattern Recognition Problems. - Dmitry Kropotov; Dmitry P. Vetrov (WILF, 2007)

3-D Mouse Brain Model Reconstruction from a Sequence of 2-D Slices in Application to Allen Brain Atlas. - Anton Osokin; Dmitry P. Vetrov; Dmitry Kropotov (CIBB, 2009)

ODDboost: Incorporating Posterior Estimates into AdaBoost. - Olga Barinova; Dmitry P. Vetrov (MLDM, 2009)

An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice. - Anton Osokin; Dmitry P. Vetrov; Alexey Lebedev; Vladimir Galatenko; Dmitry Kropotov; Konstantin V. Anokhin (CIBB, 2010)

Variational Relevance Vector Machine for Tabular Data. - Dmitry Kropotov; Dmitry P. Vetrov; Lior Wolf; Tal Hassner (ACML, 2010)

Submodular decomposition framework for inference in associative Markov networks with global constraints. - Anton Osokin; Dmitry P. Vetrov; Vladimir Kolmogorov (CVPR, 2011)

Image Segmentation with a Shape Prior Based on Simplified Skeleton. - Boris Yangel; Dmitry P. Vetrov (EMMCVPR, 2011)

Automated atlas-based segmentation of NISSL-stained mouse brain sections using supervised learning. - Olga V. Senyukova; A. S. Lukin; Dmitry P. Vetrov (Program. Comput. Softw., 2011)

Submodular Relaxation for MRFs with High-Order Potentials. - Anton Osokin; Dmitry P. Vetrov (ECCV Workshops (3), 2012)

Spatial Inference Machines. - Roman Shapovalov; Dmitry P. Vetrov; Pushmeet Kohli (CVPR, 2013)

Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data. - Boris Yangel; Dmitry P. Vetrov (EMMCVPR, 2013)

Машинное обучение - состояние и перспективы (Machine Learning: State of the Art and Perspectives). - Dmitry P. Vetrov (RCDL, 2013)

Multi-utility Learning: Structured-Output Learning with Multiple Annotation-Specific Loss Functions. - Roman Shapovalov; Dmitry P. Vetrov; Anton Osokin; Pushmeet Kohli (EMMCVPR, 2014)

Variational Inference for Sequential Distance Dependent Chinese Restaurant Process. - Sergey Bartunov; Dmitry P. Vetrov (ICML, 2014)

Putting MRFs on a Tensor Train. - Alexander Novikov 001; Anton Rodomanov; Anton Osokin; Dmitry P. Vetrov (ICML, 2014)

Inferring M-Best Diverse Labelings in a Single One. - Alexander Kirillov; Bogdan Savchynskyy; Dmitrij Schlesinger; Dmitry P. Vetrov; Carsten Rother (ICCV, 2015)

M-Best-Diverse Labelings for Submodular Energies and Beyond. - Alexander Kirillov; Dmytro Shlezinger; Dmitry P. Vetrov; Carsten Rother; Bogdan Savchynskyy (NIPS, 2015)

Tensorizing Neural Networks. - Alexander Novikov 001; Dmitry Podoprikhin; Anton Osokin; Dmitry P. Vetrov (NIPS, 2015)

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. - Michael Figurnov; Dmitry P. Vetrov; Pushmeet Kohli (CoRR, 2015)

Submodular Relaxation for Inference in Markov Random Fields. - Anton Osokin; Dmitry P. Vetrov (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Breaking Sticks and Ambiguities with Adaptive Skip-gram. - Sergey Bartunov; Dmitry Kondrashkin; Anton Osokin; Dmitry P. Vetrov (AISTATS, 2016)

Deep Part-Based Generative Shape Model with Latent Variables. - Alexander Kirillov; Mikhail Gavrikov; Ekaterina Lobacheva; Anton Osokin; Dmitry P. Vetrov (BMVC, 2016)

PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. - Mikhail Figurnov; Aizhan Ibraimova; Dmitry P. Vetrov; Pushmeet Kohli (NIPS, 2016)

A new approach for sparse Bayesian channel estimation in SCMA uplink systems. - Kirill Struminsky; Stanislav Kruglik; Dmitry P. Vetrov; Ivan V. Oseledets (WCSP, 2016)

GTApprox: Surrogate modeling for industrial design. - Mikhail Belyaev; Evgeny Burnaev; Ermek Kapushev; Maxim Panov; Pavel V. Prikhodko; Dmitry P. Vetrov; Dmitry Yarotsky (Adv. Eng. Softw., 2016)

Robust Variational Inference. - Michael Figurnov; Kirill Struminsky; Dmitry P. Vetrov (CoRR, 2016)

Ultimate tensorization: compressing convolutional and FC layers alike. - Timur Garipov; Dmitry Podoprikhin; Alexander Novikov 001; Dmitry P. Vetrov (CoRR, 2016)

Spatially Adaptive Computation Time for Residual Networks. - Michael Figurnov; Maxwell D. Collins; Yukun Zhu; Li Zhang 003; Jonathan Huang; Dmitry P. Vetrov; Ruslan Salakhutdinov (CVPR, 2017)

Fast Adaptation in Generative Models with Generative Matching Networks. - Sergey Bartunov; Dmitry P. Vetrov (ICLR (Workshop), 2017)

Variational Dropout Sparsifies Deep Neural Networks. - Dmitry Molchanov; Arsenii Ashukha; Dmitry P. Vetrov (ICML, 2017)

Structured Bayesian Pruning via Log-Normal Multiplicative Noise. - Kirill Neklyudov; Dmitry Molchanov; Arsenii Ashukha; Dmitry P. Vetrov (NIPS, 2017)

Bayesian Sparsification of Recurrent Neural Networks. - Ekaterina Lobacheva; Nadezhda Chirkova; Dmitry P. Vetrov (CoRR, 2017)

Probabilistic Adaptive Computation Time. - Michael Figurnov; Artem Sobolev; Dmitry P. Vetrov (CoRR, 2017)

Conditional Generators of Words Definitions. - Artyom Gadetsky; Ilya Yakubovskiy; Dmitry P. Vetrov (ACL (2), 2018)

ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks. - Iurii Kemaev; Daniil Polykovskiy; Dmitry P. Vetrov (ACML, 2018)

Few-shot Generative Modelling with Generative Matching Networks. - Sergey Bartunov; Dmitry P. Vetrov (AISTATS, 2018)

Bayesian Compression for Natural Language Processing. - Nadezhda Chirkova; Ekaterina Lobacheva; Dmitry P. Vetrov (EMNLP, 2018)

Uncertainty Estimation via Stochastic Batch Normalization. - Andrei Atanov; Arsenii Ashukha; Dmitry Molchanov; Kirill Neklyudov; Dmitry P. Vetrov (ICLR (Workshop), 2018)

Bayesian Incremental Learning for Deep Neural Networks. - Max Kochurov; Timur Garipov; Dmitry Podoprikhin; Dmitry Molchanov; Arsenii Ashukha; Dmitry P. Vetrov (ICLR (Workshop), 2018)

Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs. - Timur Garipov; Pavel Izmailov; Dmitrii Podoprikhin; Dmitry P. Vetrov; Andrew Gordon Wilson (NeurIPS, 2018)

Averaging Weights Leads to Wider Optima and Better Generalization. - Pavel Izmailov; Dmitrii Podoprikhin; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (UAI, 2018)

Universal Conditional Machine. - Oleg Ivanov; Michael Figurnov; Dmitry P. Vetrov (CoRR, 2018)

Pairwise Augmented GANs with Adversarial Reconstruction Loss. - Aibek Alanov; Max Kochurov; Daniil Yashkov; Dmitry P. Vetrov (CoRR, 2018)

The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models. - Andrei Atanov; Arsenii Ashukha; Kirill Struminsky; Dmitry P. Vetrov; Max Welling (CoRR, 2018)

Metropolis-Hastings view on variational inference and adversarial training. - Kirill Neklyudov; Pavel Shvechikov; Dmitry P. Vetrov (CoRR, 2018)

Variational Dropout via Empirical Bayes. - Valery Kharitonov; Dmitry Molchanov; Dmitry P. Vetrov (CoRR, 2018)

Bayesian Sparsification of Gated Recurrent Neural Networks. - Ekaterina Lobacheva; Nadezhda Chirkova; Dmitry P. Vetrov (CoRR, 2018)

A Simple Method to Evaluate Support Size and Non-uniformity of a Decoder-Based Generative Model. - Kirill Struminsky; Dmitry P. Vetrov (AIST, 2019)

Doubly Semi-Implicit Variational Inference. - Dmitry Molchanov; Valery Kharitonov; Artem Sobolev; Dmitry P. Vetrov (AISTATS, 2019)

The Deep Weight Prior. - Andrei Atanov; Arsenii Ashukha; Kirill Struminsky; Dmitry P. Vetrov; Max Welling (ICLR (Poster), 2019)

Variational Autoencoder with Arbitrary Conditioning. - Oleg Ivanov; Michael Figurnov; Dmitry P. Vetrov (ICLR (Poster), 2019)

Variance Networks: When Expectation Does Not Meet Your Expectations. - Kirill Neklyudov; Dmitry Molchanov; Arsenii Ashukha; Dmitry P. Vetrov (ICLR (Poster), 2019)

A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models. - Maksim Kuznetsov; Daniil Polykovskiy; Dmitry P. Vetrov; Alexander Zhebrak (NeurIPS, 2019)

A Simple Baseline for Bayesian Uncertainty in Deep Learning. - Wesley J. Maddox; Pavel Izmailov; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (NeurIPS, 2019)

The Implicit Metropolis-Hastings Algorithm. - Kirill Neklyudov; Evgenii Egorov; Dmitry P. Vetrov (NeurIPS, 2019)

Importance Weighted Hierarchical Variational Inference. - Artem Sobolev; Dmitry P. Vetrov (NeurIPS, 2019)

Efficient Language Modeling with Automatic Relevance Determination in Recurrent Neural Networks. - Maxim Kodryan; Artem M. Grachev; Dmitry I. Ignatov; Dmitry P. Vetrov (RepL4NLP@ACL, 2019)

Subspace Inference for Bayesian Deep Learning. - Pavel Izmailov; Wesley J. Maddox; Polina Kirichenko; Timur Garipov; Dmitry P. Vetrov; Andrew Gordon Wilson (UAI, 2019)

Semi-Conditional Normalizing Flows for Semi-Supervised Learning. - Andrei Atanov; Alexandra Volokhova; Arsenii Ashukha; Ivan Sosnovik; Dmitry P. Vetrov (CoRR, 2019)

MLRG Deep Curvature. - Diego Granziol; Xingchen Wan; Timur Garipov; Dmitry P. Vetrov; Stephen Roberts 001 (CoRR, 2019)

Low-Variance Black-Box Gradient Estimates for the Plackett-Luce Distribution. - Artyom Gadetsky; Kirill Struminsky; Christopher Robinson; Novi Quadrianto; Dmitry P. Vetrov (AAAI, 2020)

Structured Sparsification of Gated Recurrent Neural Networks. - Ekaterina Lobacheva; Nadezhda Chirkova; Alexander Markovich; Dmitry P. Vetrov (AAAI, 2020)

Deterministic Decoding for Discrete Data in Variational Autoencoders. - Daniil Polykovskiy; Dmitry P. Vetrov (AISTATS, 2020)

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. - Arsenii Ashukha; Alexander Lyzhov; Dmitry Molchanov; Dmitry P. Vetrov (ICLR, 2020)

Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics. - Arsenii Kuznetsov; Pavel Shvechikov; Alexander Grishin; Dmitry P. Vetrov (ICML, 2020)

Involutive MCMC: a Unifying Framework. - Kirill Neklyudov; Max Welling; Evgenii Egorov; Dmitry P. Vetrov (ICML, 2020)

On Power Laws in Deep Ensembles. - Ekaterina Lobacheva; Nadezhda Chirkova; Maxim Kodryan; Dmitry P. Vetrov (NeurIPS, 2020)

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation. - Alexander Lyzhov; Yuliya Molchanova; Arsenii Ashukha; Dmitry Molchanov; Dmitry P. Vetrov (UAI, 2020)

User-controllable Multi-texture Synthesis with Generative Adversarial Networks. - Aibek Alanov; Max Kochurov; Denis Volkhonskiy; Daniil Yashkov; Evgeny Burnaev; Dmitry P. Vetrov (VISIGRAPP (4: VISAPP), 2020)

Stochasticity in Neural ODEs: An Empirical Study. - Viktor Oganesyan; Alexandra Volokhova; Dmitry P. Vetrov (CoRR, 2020)

Deep Ensembles on a Fixed Memory Budget: One Wide Network or Several Thinner Ones? - Nadezhda Chirkova; Ekaterina Lobacheva; Dmitry P. Vetrov (CoRR, 2020)

Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks. - Viktor Yanush; Alexander Shekhovtsov; Dmitry Molchanov; Dmitry P. Vetrov (CoRR, 2020)

On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay. - Ekaterina Lobacheva; Maxim Kodryan; Nadezhda Chirkova; Andrey Malinin; Dmitry P. Vetrov (NeurIPS, 2021)

Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces. - Kirill Struminsky; Artyom Gadetsky; Denis Rakitin; Danil Karpushkin; Dmitry P. Vetrov (NeurIPS, 2021)

Towards Practical Credit Assignment for Deep Reinforcement Learning. - Vyacheslav Alipov; Riley Simmons-Edler; Nikita Putintsev; Pavel Kalinin; Dmitry P. Vetrov (CoRR, 2021)

Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations. - Arsenii Ashukha; Andrei Atanov; Dmitry P. Vetrov (CoRR, 2021)

Quantization of Generative Adversarial Networks for Efficient Inference: a Methodological Study. - Pavel Andreev; Alexander Fritzler; Dmitry P. Vetrov (CoRR, 2021)

Automating Control of Overestimation Bias for Continuous Reinforcement Learning. - Arsenii Kuznetsov; Alexander Grishin; Artem Tsypin; Arsenii Ashukha; Dmitry P. Vetrov (CoRR, 2021)

Machine Learning Methods for Spectral Efficiency Prediction in Massive MIMO Systems. - Evgeny Bobrov; Sergey Troshin 001; Nadezhda Chirkova; Ekaterina Lobacheva; Sviatoslav Panchenko; Dmitry P. Vetrov; Dmitry Kropotov (CoRR, 2021)

FFC-SE: Fast Fourier Convolution for Speech Enhancement. - Ivan Shchekotov; Pavel K. Andreev; Oleg Ivanov; Aibek Alanov; Dmitry P. Vetrov (INTERSPEECH, 2022)

HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks. - Aibek Alanov; Vadim Titov; Dmitry P. Vetrov (NeurIPS, 2022)

Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes. - Maxim Kodryan; Ekaterina Lobacheva; Maksim Nakhodnov; Dmitry P. Vetrov (NeurIPS, 2022)

HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement. - Pavel Andreev; Aibek Alanov; Oleg Ivanov; Dmitry P. Vetrov (CoRR, 2022)

StyleDomain: Analysis of StyleSpace for Domain Adaptation of StyleGAN. - Aibek Alanov; Vadim Titov; Maksim Nakhodnov; Dmitry P. Vetrov (CoRR, 2022)

MARS: Masked Automatic Ranks Selection in Tensor Decompositions. - Maxim Kodryan; Dmitry Kropotov; Dmitry P. Vetrov (AISTATS, 2023)

HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement. - Pavel Andreev; Aibek Alanov; Oleg Ivanov; Dmitry P. Vetrov (ICASSP, 2023)

StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation. - Aibek Alanov; Vadim Titov; Maksim Nakhodnov; Dmitry P. Vetrov (ICCV, 2023)

UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model. - Anastasiia Iashchenko; Pavel Andreev; Ivan Shchekotov; Nicholas Babaev; Dmitry P. Vetrov (INTERSPEECH, 2023)

Entropic Neural Optimal Transport via Diffusion Processes. - Nikita Gushchin; Alexander Kolesov; Alexander Korotin; Dmitry P. Vetrov; Evgeny Burnaev (NeurIPS, 2023)

Star-Shaped Denoising Diffusion Probabilistic Models. - Andrey Okhotin; Dmitry Molchanov; Vladimir Arkhipkin; Grigory Bartosh; Viktor Ohanesian; Aibek Alanov; Dmitry P. Vetrov (NeurIPS, 2023)

To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning. - Ildus Sadrtdinov; Dmitrii Pozdeev; Dmitry P. Vetrov; Ekaterina Lobacheva (NeurIPS, 2023)

Star-Shaped Denoising Diffusion Probabilistic Models. - Andrey Okhotin; Dmitry Molchanov; Vladimir Arkhipkin; Grigory Bartosh; Aibek Alanov; Dmitry P. Vetrov (CoRR, 2023)

Gradual Optimization Learning for Conformational Energy Minimization. - Artem Tsypin; Leonid Ugadiarov; Kuzma Khrabrov; Manvel Avetisian; Alexander Telepov; Egor Rumiantsev; Alexey Skrynnik; Aleksandr I. Panov; Dmitry P. Vetrov; Elena Tutubalina; Artur Kadurin (CoRR, 2023)

Large Learning Rates Improve Generalization: But How Large Are We Talking About? - Ekaterina Lobacheva; Eduard Pockonechnyy; Maxim Kodryan; Dmitry P. Vetrov (CoRR, 2023)

Differentiable Rendering with Reparameterized Volume Sampling. - Nikita Morozov; Denis Rakitin; Oleg Desheulin; Dmitry P. Vetrov; Kirill Struminsky (AISTATS, 2024)

Generative Flow Networks as Entropy-Regularized RL. - Daniil Tiapkin; Nikita Morozov; Alexey Naumov; Dmitry P. Vetrov (AISTATS, 2024)

The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN Inversion and High Quality Image Editing. - Denis Bobkov; Vadim Titov; Aibek Alanov; Dmitry P. Vetrov (CVPR, 2024)

Guide-and-Rescale: Self-guidance Mechanism for Effective Tuning-Free Real Image Editing. - Vadim Titov; Madina Khalmatova; Alexandra Ivanova; Dmitry P. Vetrov; Aibek Alanov (ECCV (71), 2024)

Gradual Optimization Learning for Conformational Energy Minimization. - Artem Tsypin; Leonid Ugadiarov; Kuzma Khrabrov; Alexander Telepov; Egor Rumiantsev; Alexey Skrynnik; Aleksandr Panov; Dmitry P. Vetrov; Elena Tutubalina; Artur Kadurin (ICLR, 2024)

Neural Diffusion Models. - Grigory Bartosh; Dmitry P. Vetrov; Christian A. Naesseth (ICML, 2024)

Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract). - Andrey Okhotin; Dmitry Molchanov; Vladimir Arkhipkin; Grigory Bartosh; Viktor Ohanesian; Aibek Alanov; Dmitry P. Vetrov (KI, 2024)

Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. - Grigory Bartosh; Dmitry P. Vetrov; Christian Andersson Naesseth (NeurIPS, 2024)

HairFastGAN: Realistic and Robust Hair Transfer with a Fast Encoder-Based Approach. - Maxim Nikolaev; Mikhail Kuznetsov; Dmitry P. Vetrov; Aibek Alanov (NeurIPS, 2024)

Where Do Large Learning Rates Lead Us? - Ildus Sadrtdinov; Maxim Kodryan; Eduard Pokonechny; Ekaterina Lobacheva; Dmitry P. Vetrov (NeurIPS, 2024)

TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings. - Alexander Shabalin; Viacheslav Meshchaninov; Tingir Badmaev; Dmitry Molchanov; Grigory Bartosh; Sergey Markov; Dmitry P. Vetrov (CoRR, 2024)

Diffusion on language model embeddings for protein sequence generation. - Viacheslav Meshchaninov; Pavel V. Strashnov; Andrey Shevtsov; Fedor Nikolaev; Nikita Ivanisenko; Olga L. Kardymon; Dmitry P. Vetrov (CoRR, 2024)

Improving GFlowNets with Monte Carlo Tree Search. - Nikita Morozov; Daniil Tiapkin; Sergey Samsonov; Alexey Naumov; Dmitry P. Vetrov (CoRR, 2024)

Regularized Distribution Matching Distillation for One-step Unpaired Image-to-Image Translation. - Denis Rakitin; Ivan Shchekotov; Dmitry P. Vetrov (CoRR, 2024)

TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings. - Alexander Shabalin; Viacheslav Meshchaninov; Egor Chimbulatov; Vladislav Lapikov; Roman Kim; Grigory Bartosh; Dmitry Molchanov; Sergey Markov; Dmitry P. Vetrov (AAAI, 2025)

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations. - Grigory Bartosh; Dmitry P. Vetrov; Christian A. Naesseth (CoRR, 2025)

Smoothie: Smoothing Diffusion on Token Embeddings for Text Generation. - Alexander Shabalin; Viacheslav Meshchaninov; Dmitry P. Vetrov (CoRR, 2025)

Adaptive Destruction Processes for Diffusion Samplers. - Timofei Gritsaev; Nikita Morozov; Kirill Tamogashev; Daniil Tiapkin; Sergey Samsonov; Alexey Naumov; Dmitry P. Vetrov; Nikolay Malkin (CoRR, 2025)