Publications

2023

paper To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning by Ildus Sadrtdinov, Dmitrii Pozdeev, Dmitry Vetrov, Ekaterina Lobacheva. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
paper code Star-Shaped Denoising Diffusion Probabilistic Models by Andrey Okhotin, Dmitry Molchanov, Vladimir Arkhipkin, Grigory Bartosh, Victor Oganesyan, Aibek Alanov, Dmitry Vetrov. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
paper code Entropic Neural Optimal Transport via Diffusion Processes by Nikita Gushchin, Alexander Kolesov, Alexander Korotin, Dmitry Vetrov, Evgeny Burnaev. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
paper code StyleDomain: Efficient and Lightweight Parameterizations of StyleGAN for One-shot and Few-shot Domain Adaptation by Aibek Alanov, Vadim Titov, Maksim Nakhodnov, Dmitry Vetrov. International Conference on Computer Vision (ICCV 2023)
paper code UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model by Anastasiia Iashchenko, Pavel Andreev, Ivan Shchekotov, Nicholas Babaev, Dmitry Vetrov. 24th INTERSPEECH Conference (Interspeech 2023)
paper HIFI++: A Unified Framework for Bandwidth Extension and Speech Enhancement by Pavel Andreev, Aibek Alanov, Oleg Ivanov, Dmitry Vetrov. 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
paper Iterative autoregression: a novel trick to improve your low-latency speech enhancement model by Pavel Andreev, Nicholas Babaev, Ivan Shchekotov, Azat Saginbaev, Aibek Alanov. 24th INTERSPEECH Conference (Interspeech 2023)
paper code MARS: Masked Automatic Ranks Selection in Tensor Decompositions by Maxim Kodryan, Dmitry Kropotov, Dmitry Vetrov. The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)

2022

paper code HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Network by Aibek Alanov, Vadim Titov, Dmitry Vetrov. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
paper Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes by Maxim Kodryan, Ekaterina Lobacheva, Maksim Nakhodnov, Dmitry Vetrov. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
paper Study on precoding optimization algorithms in massive MIMO system with multi-antenna users by Evgeny Bobrov, Dmitry Kropotov, Sergey Troshin, Danila Zaev. Optimization Methods and Software 2022
paper FFC-SE: Fast Fourier Convolution for Speech Enhancement by Ivan Shchekotov, Pavel Andreev, Oleg Ivanov, Aibek Alanov, Dmitry Vetrov. 23rd INTERSPEECH Conference (INTERSPEECH 2022)
paper Variational Autoencoders for Precoding Matrices with High Spectral Efficiency by Evgeny Bobrov, Alexander Markov, Sviatoslav Panchenko, Dmitry Vetrov. 21st International Conference Mathematical Optimization Theory and Operations Research: Recent Trends (MOTOR 2022)
paper Massive MIMO Adaptive Modulation and Coding Using Online Deep Learning Algorithm by Evgeny Bobrov, Dmitry Kropotov, Hao Lu, Danila Zaev. IEEE Communications Letters (Volume: 26, Issue: 4, April 2022)

2021

paper On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay by Ekaterina Lobacheva, Maxim Kodryan, Nadezhda Chirkova, Andrey Malinin, Dmitry Vetrov. Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
paper Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces by Kirill Struminsky, Artyom Gadetsky, Denis Rakitin, Danil Karpushkin, Dmitry Vetrov. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021)
paper A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code by Nadezhda Chirkova and Sergey Troshin. 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)
paper On the Embeddings of Variables in Recurrent Neural Networks for Source Code by Nadezhda Chirkova. 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021)

2020

paper video code On Power Laws in Deep Ensembles by Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry Vetrov. Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS 2020)
paper A Randomized Coordinate Descent Method with Volume Sampling by Anton Rodomanov and Dmitry Kropotov. SIAM Journal on Optimization
paper On Power Laws in Deep Ensembles by Ekaterina Lobacheva, Nadezhda Chirkova, Maxim Kodryan, Dmitry Vetrov. Workshop on Uncertainty and Robustness in Deep Learning, ICML 2020
paper Involutive MCMC: One Way to Derive Them All by Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov. Thirty-seventh International Conference on Machine Learning (ICML 2020)
paper video code Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics by Arseny Kuznetsov, Pavel Shvechikov, Alexander Grishin, Dmitry Vetrov. Thirty-seventh International Conference on Machine Learning (ICML 2020)
paper Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation by Dmitry Molchanov, Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Vetrov. Conference on Uncertainty in Artificial Intelligence (UAI 2020)
paper code Deterministic Decoding for Discrete Data in Variational Autoencoders by Daniil Polykovskiy and Dmitry Vetrov. The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)
paper Stochasticity in Neural ODEs: An Empirical Study by Victor Oganesyan, Alexandra Volokhova, Dmitry Vetrov. Integration of Deep Neural Models and Differential Equations, ICLR 2020 Workshop
paper code Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning by Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov. Eighth International Conference on Learning Representations (ICLR 2020)
paper code Structured Sparsification of Gated Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova, Aleksandr Markovich, Dmitry Vetrov. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
paper code Low-variance Black-box Gradient Estimates for the Plackett-Luce Distribution by Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry Vetrov. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)

2019

Structured Sparsification of Gated Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova, Alexander Markovich, Dmitry Vetrov. Context and Compositionality in Biological and Artificial Neural Systems, NeurIPS 2019 Workshop
paper Unsupervised Domain Adaptation with Shared Latent Dynamics for Reinforcement Learning by Evgenii Nikishin, Arsenii Ashukha, Dmitry Vetrov. Bayesian Deep Learning, NeurIPS 2019 Workshop
Low-variance Gradient Estimates for the Plackett-Luce Distribution by Artyom Gadetsky, Kirill Struminsky, Christopher Robinson, Novi Quadrianto, Dmitry Vetrov. Bayesian Deep Learning, NeurIPS 2019 Workshop
Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning by Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov. Bayesian Deep Learning, NeurIPS 2019 Workshop
paper A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models by Maxim Kuznetsov, Daniil Polykovskiy, Dmitry Vetrov, Alexander Zhebrak. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
paper video A Simple Baseline for Bayesian Uncertainty in Deep Learning by Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
paper The Implicit Metropolis-Hastings Algorithm by Kirill Neklyudov, Evgenii Egorov, Dmitry Vetrov. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
paper video code Importance Weighted Hierarchical Variational Inference by Artem Sobolev and Dmitry Vetrov. Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019)
paper code Deep learning enables rapid identification of potent DDR1 kinase inhibitors by Alex Zhavoronkov, Yan Ivanenkov, Alex Aliper, Mark Veselov, Vladimir Aladinskiy, Anastasiya Aladinskaya, Victor Terentiev, Daniil Polykovskiy, Maxim Kuznetsov, Arip Asadulaev, Yury Volkov, Artem Zholus, Rim Shayakhmetov, Alexander Zhebrak, Lidiya Minaeva, Bogdan Zagribelnyy, Lennart H. Lee, Richard Soll, David Madge, Li Xing, Tao Guo, Alán Aspuru-Guzik. Nature Biotechnology
paper code Subspace Inference for Bayesian Deep Learning by Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson. The 35th Uncertainty in Artificial Intelligence Conference (UAI 2019)
paper Semi-Conditional Normalizing Flows for Semi-Supervised Learning by Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry Vetrov. Workshop on Invertible Neural Nets and Normalizing Flows, International Conference on Machine Learning (ICML) 2019
paper video Variational Autoencoder with Arbitrary Conditioning by Oleg Ivanov, Michael Figurnov, Dmitry Vetrov. Seventh International Conference on Learning Representations (ICLR 2019)
paper video code Variance Networks: When Expectation Does Not Meet Your Expectations by Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov. Seventh International Conference on Learning Representations (ICLR 2019)
paper video The Deep Weight Prior by Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry Vetrov, Max Welling. Seventh International Conference on Learning Representations (ICLR 2019)
paper video Doubly Semi-Implicit Variational Inference by Dmitry Molchanov, Valery Kharitonov, Artem Sobolev, Dmitry Vetrov. The 22st International Conference on Artificial Intelligence and Statistics (AISTATS 2019)

2018

paper Probabilistic Adaptive Computation Time by Michael Figurnov, Artem Sobolev, Dmitry Vetrov. Bulletin of the Polish Academy of Sciences: Technical Sciences 2018
paper Bayesian Sparsification of Gated Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov. Compact Deep Neural Network Representation with Industrial Applications NIPS 2018 Workshop
paper Joint Belief Tracking and Reward Optimization through Approximate Inference by Pavel Shvechikov, Alexander Grishin, Arseny Kuznetsov, Alexander Fritzler, Dmitry Vetrov. Reinforcement Learning under Partial Observability NeurIPS 2018 Workshop
paper Importance Weighted Hierarchical Variational Inference by Artem Sobolev and Dmitry Vetrov. Bayesian Deep Learning NIPS 2018 Workshop
paper Variational Dropout via Empirical Bayes by Valery Kharitonov, Dmitry Molchanov, Dmitry Vetrov. Bayesian Deep Learning NIPS 2018 Workshop
paper Subset-Conditioned Generation Using Variational Autoencoder With A Learnable Tensor-Train Induced Prior by Maksim Kuznetsov, Daniil Polykovskiy, Dmitry Vetrov, Alexander Zhebrak. Bayesian Deep Learning NIPS 2018 Workshop
paper Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery by Daniil Polykovskiy, Alexander Zhebrak, Dmitry Vetrov, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, Artur Kadurin. Molecular Pharmaceutics Journal.
paper video Quantifying Learning Guarantees for Convex but Inconsistent Surrogates by Kirill Struminsky, Simon Lacoste-Julien, Anton Osokin. Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018)
paper video code Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs by Timur Garipov, Pavel Izmailov, Dmitry Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson. Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018)
paper Bayesian Compression for Natural Language Processing by Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov. 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)
paper Fast Uncertainty Estimates and Bayesian Model Averaging of DNNs by Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson. Uncertainty in Artificial Intelligence Workshop (UAI Workshop) 2018
paper Improving Stability in Deep Reinforcement Learning with Weight Averaging by Evgenii Nikishin, Pavel Izmailov, Ben Athiwaratkun, Dmitrii Podoprikhin, Timur Garipov, Pavel Shvechikov, Dmitry Vetrov, Andrew Gordon Wilson. Uncertainty in Artificial Intelligence Workshop (UAI Workshop) 2018
paper Averaging Weights Leads to Wider Optima and Better Generalization by Pavel Izmailov, Dmitry Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson. Conference on Uncertainty in Artificial Intelligence 2018 (UAI 2018)
paper Conditional Generators of Words Definitions by Artyom Gadetsky, Ilya Yakubovskiy, Dmitry Vetrov. 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)
paper SEARNN: Training RNNs with global-local losses by Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien. International Conference on Learning Representations (ICLR) 2018
paper Concorde: Morphological Agreement in Conversational Models by Daniil Polykovskiy, Dmitry Soloviev, Sergey Nikolenko. 10th Asian Conference on Machine Learning (ACML 2018)
paper ReSet: Learning Recurrent Dynamic Routing in ResNet-like Neural Networks by Iurii Kemaev, Daniil Polykovskiy, Dmitry Vetrov. 10th Asian Conference on Machine Learning (ACML 2018)
paper Expressive power of recurrent neural networks by Valentin Khrulkov, Alexander Novikov, Ivan Oseledets. Sixth International Conference on Learning Representations (ICLR 2018)
paper Fast Adaptation in Generative Models with Generative Matching Networks by Sergey Bartunov and Dmitry P. Vetrov. The 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018
paper Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition by Pavel Izmailov, Alexander Novikov, Dmitry Kropotov. The 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018
paper Predictive model for bottomhole pressure based on machine learning by Pavel Spesivtsev, Konstantin Sinkov, Ivan Sofronov, Anna Zimina, Alexey Umnov, Ramil Yarullin, Dmitry Vetrov. Journal of Petroleum Science and Engineering
paper video Uncertainty Estimation via Stochastic Batch Normalization by Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry Vetrov. International Conference on Learning Representations Workshop (ICLR Workshop) 2018
paper Bayesian Incremental Learning for Deep Neural Networks by Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov. International Conference on Learning Representations Workshop (ICLR Workshop) 2018
paper Monotonic models for real-time dynamic malware detection by Alexander Chistyakov, Ekaterina Lobacheva, Alexander Shevelev, Alexey Romanenko. International Conference on Learning Representations Workshop (ICLR Workshop) 2018

2017

paper Exponential Machines by Alexander Novikov, Mikhail Trofimov, Ivan Oseledets. Bulletin of the Polish Academy of Sciences: Technical Sciences 2018
paper video code Structured Bayesian Pruning via Log-Normal Multiplicative Noise by Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov. Thirty-first Conference on Neural Information Processing Systems (NIPS 2017)
paper video Spatially Adaptive Computation Time for Residual Networks by Michael Figurnov, Maxwell Collins, Yukun Zhu, Li Zhang, Jonathan Huang, Dmitry Vetrov, Ruslan Salakhutdinov. Conference on Computer Vision and Pattern Recognition 2017 (CVPR 2017)
paper video code Variational Dropout Sparsifies Deep Neural Networks by Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov. International Conference on Machine Learning (ICML 2017)
paper Bayesian Sparsification of Recurrent Neural Networks by Ekaterina Lobacheva, Nadezhda Chirkova, Dmitry Vetrov. International Conference on Machine Learning (ICML) Workshop 2017
paper Fast Adaptation in Generative Models with Generative Matching Networks by Sergey Bartunov and Dmitry P. Vetrov. International Conference on Learning Representations (ICLR) Workshop 2017
paper Semantic embeddings for program behaviour patterns by Alexander Chistyakov, Ekaterina Lobacheva, Arseny Kuznetsov, Alexey Romanenko. International Conference on Learning Representations (ICLR) Workshop 2017

2016

paper A Superlinearly-Convergent Proximal Newton-Type Method for the Optimization of Finite Sums by Anton Rodomanov and Dmitry Kropotov. International Conference on Machine Learning (ICML) 2016
paper One-shot Learning with Memory-Augmented Neural Networks by Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, Timothy Lillicrap. International Conference on Machine Learning (ICML) 2016
paper Breaking Sticks and Ambiguities with Adaptive Skip-gram by Sergey Bartunov, Dmitry Kondrashkin, Anton Osokin, Dmitry Vetrov. International Conference on Artificial Intelligence and Statistics (AISTATS) 2016
paper Deep Part-Based Generative Shape Model with Latent Variables by Alexander Kirillov, Mikhail Gavrikov, Ekaterina Lobacheva, Anton Osokin, Dmitry Vetrov. British Machine Vision Conference 2016 (BMVC 2016)
paper A New Approach for Sparse Bayesian Channel Estimation in SCMA Uplink Systems by Kirill Struminsky, Stanislav Kruglik, Dmitry Vetrov, Ivan Oseledets. International Conference on Wireless Communications and Signal Processing (WCSP) 2016
paper Tensor Train polynomial model via Riemannian optimization by Alexander Novikov, Mikhail Trofimov, Ivan Oseledets. (ICML) 2016. Advances in non-convex analysis and optimization Workshop
paper PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions by Michael Figurnov, Dmitry Vetrov, Pushmeet Kohli. International Conference on Learning Representations (ICLR) 2016 Workshop track
paper Dropout-based Automatic Relevance Determination by Dmitry Molchanov, Arseniy Ashuha, Dmitry Vetrov. Advances in Neural Information Processing Systems (NIPS) 2016
paper Robust Variational Inference by Michael Figurnov, Kirill Struminsky, Dmitry Vetrov. Advances in Neural Information Processing Systems (NIPS) 2016
paper Ultimate tensorization: convolutions and FC alike by Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry Vetrov. Advances in Neural Information Processing Systems (NIPS) 2016

2015

paper A Newton-type Incremental Method with a Superlinear Convergence Rate by A. Rodomanov and D. Kropotov. NIPS 2015 Workshop on Optimization for Machine Learning
paper Inferring M-Best Diverse Labelings in a Single One by A. Kirillov, B. Savchynskyy, D. Schlesinger, D. Vetrov, C. Rother. Proceedings of the International Conference on Computer Vision (ICCV). 2015
paper Joint Optimization of Segmentation and Color Clustering by Ekaterina Lobacheva, Olga Veksler, Yuri Boykov. 2015 International Conference on Computer Vision (ICCV 2015)
paper Tensorizing Neural Networks by Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov. In Advances in Neural Information Processing Systems 28 (NIPS). 2015
paper M-Best-Diverse Labelings for Submodular Energies and Beyond by A. Kirillov, D. Schlesinger, D. Vetrov, C. Rother, B. Savchynskyy. In Advances in Neural Information Processing Systems 28 (NIPS). 2015
paper Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions by R. Shapovalov, A. Osokin, D. Vetrov, P. Kohli. In Proceedings of International Workhop on Energy Minimization Methods (EMMCVPR2015), 2015
paper Learning representations in directed networks by Oleg Ivanov and Sergey Bartunov. 4th Conference on Analysis of Images, Social Networks, and Texts (AIST), 2015. Best conference paper award

2014

paper IEEE Transactions on Pattern Analysis and Machine Intelligence by A. Osokin and D. Vetrov. Submodular relaxation for inference in Markov random fields. (TPAMI). Accepted. 2014
paper Perceptually Inspired Layout-aware Losses for Image Segmentation by A. Osokin and P. Kohli. European Conference on Computer Vision (ECCV), 2014
paper Variational Inference for Sequential Distance Dependent Chinese Restaurant Process by S. Bartunov and D. Vetrov. Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32
paper Putting MRFs on a Tensor Train by A. Novikov, A. Rodomanov, A. Osokin, D. Vetrov. Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. JMLR: W&CP volume 32

2013

paper Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data by B. Yangel and D. Vetrov. In Proceedings of International Workhop on Energy Minimization Methods (EMMCVPR2013), 2013
paper In Computer Vision and Pattern Recognition by R. Shapovalov, D. Vetrov, P. Kohli. Spatial Inference Machines. (CVPR), 2013
paper A Principled Deep Random Field Model for Image Segmentation by P. Kohli, A. Osokin, S. Jegelka. In Computer Vision and Pattern Recognition (CVPR), 2013
paper Automatic Determination of Cell Division Rate Using Microscope Images by K. Nekrasov, D. Laptev, D. Vetrov. Pattern Recognition and Image Analysis, 23(1):1–6, 2013
paper An Approach to Segmentation of Mouse Brain Imagesvia Intermodal Registration by P. Voronin, D. Vetrov, K. Ismailov. Pattern Recognition and Image Analysis, 23(2):335–339, 2013

2012

paper Fast Approximate Energy Minimization with Label Costs by A. Delong, A. Osokin, H. Isack, Y. Boykov. International Journal of Computer Vision (IJCV), 96(1):1–27, 2012
paper Submodular Relaxation for MRFs with High-Order Potentials by A. Osokin and D. Vetrov. HiPot: ECCV 2012 Workshop on Higher-Order Models and Global Constraints in Computer Vision, 2012
paper Minimizing Sparse High-Order Energies by Submodular Vertex-Cover by A. Delong, O. Veksler, A. Osokin, Y. Boykov. Advances in Neural Information Processing Systems (NIPS), 2012

2011

paper MRF Energy Minimization Approach with Epitomic Textural Global Term for Image Segmentation Problems by D. Elshin and D. Kropotov. In Proceedings of Bilateral Russian-Indian Workshop on Emerging Applications of Computer Vision, 2011
paper Submodular Decomposition Framework for Inference in Associative Markov Networks with Global Constraints by A. Osokin, D. Vetrov, V. Kolmogorov. Proceedings ofInternational Conference on Computer Vision and Pattern Recognition (CVPR), 2011
paper Image Segmentation with a Shape Prior Based on Simplified Skeleton by B. Yangel and D. Vetrov. Proceedings of International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), LNCS 6819, 2011
paper Graph Preserving Label Decomposition in Discrete MRFs with Selfish Potentials by D. Vetrov and A. Osokin. Proceedings of NIPS Workshop on Discrete Optimization in Machine learning (DISCML NIPS), 2011
paper An Interactive Method of Anatomical Segmentation and Gene Expression Estimation for an Experimental Mouse Brain Slice by A. Osokin, D. Vetrov, A. Lebedev, V. Galatenko, D. Kropotov, K. Anokhin. Lecture Notes in Computer Science (LNCS), vol. 6685, pp. 86–97, 2011

2010

paper Fast Approximate Energy Minimization with Label Costs by A. Delong, A. Osokin, H. Isack, Y. Boykov. Proceedings of International Conference on Computer Vision and Pattern Recognition (CVPR), 2010
paper Variational Relevance Vector Machine for Tabular Data by D. Kropotov, D. Vetrov, L. Wolf, T. Hassner. Proceedings of Asian Conference on Machine Learning (ACML), JMLR Workshop & Conference Proceedings, vol. 13, pp. 79-94, 2010
paper The Algorithm for Detection of Fuzzy Behavioral Patterns by V. Vishnevsky and D. Vetrov. Proceedings of International Conference on Methods and Techniques in Behavioral Research, ISBN 978-90-74821-86-5, 2010
paper 3D Reconstruction of Mouse Brain from a Sequence of 2D Brain Slices in Application to Allen Brain Atlas by A. Osokin, D. Vetrov, D. Kropotov. Lecture Notes in Computer Science (LNCS), vol. 6160, pp. 291-303, 2010
paper Variational Segmentation Algorithms with Label Frequency Constraints by D. Kropotov, D. Laptev, A. Osokin, D. Vetrov. Pattern Recognition and Image Analysis, vol. 20, no. 3, pp. 324-334, 2010
paper Intermodal Registration Algorithm for Segmentation of Mouse Brain Images by P. Voronin and D. Vetrov. Proceedings of International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 2, pp. 377-381, 2010
paper Automatic Detection of Cell Division Intensity in Budding Yeast by K. Nekrasov, D. Laptev, D. Vetrov. Proceedings of International Conference on Pattern Recognition and Image Analysis (PRIA), vol. 2, pp. 335-339, 2010

2009

paper Relevant Regressors Selection by Continuous AIC by D. Kropotov, N. Ptashko, D. Vetrov. Pattern Recognition and Image Analysis, vol. 19, no. 3, pp. 456-464, 2009
paper General Solutions for Information-Based and Bayesian Approaches to Model Selection in Linear Regression and Their Equivalence by D. Kropotov and D. Vetrov. Pattern Recognition and Image Analysis, vol. 19, no. 3, pp. 447-455, 2009
paper Video Tracking and Behaviour Segmentation of Laboratory Rodents by E. Lomakina-Rumyantseva, P. Voronin, D. Kropotov, D. Vetrov, A. Konushin. Pattern Recognition and Image Analysis, vol. 19, no. 4, pp. 616-622, 2009

2008

paper An Automatic Relevance Determination Procedure Based on Akaike Information Criterion for Linear Regression Problems by D. Kropotov and D. Vetrov. Proceedings of ICML Workshop on Sparse Optimization and Variable Selection, 2008
paper Automatic segmentation of mouse behavior using hidden markov models by D. Vetrov, D. Kropotov, A. Konushin, E. Lomakina-Rumyantseva, I. Zarayskaya, K. Anokhin. Proceedings of International Conference on Methods and Techniques in Behavioral Research, 2008
paper Automated distinguishing of mouse behavior in new environment and under amphetamine using decision trees by A. Konushin, E. Lomakina-Rumyantseva, D. Kropotov, D. Vetrov, A. Cherepov, K. Anokhin. Proceedings of International Conference on Methods and Techniques in Behavioral Research, 2008

2007

paper On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning by D. Kropotov and D. Vetrov. Proceedings of International Conference on Machine Learning (ICML), pp. 457-464, 2007
paper Fuzzy Rules Generation Method for Pattern Recognition Problems by D. Kropotov and D. Vetrov. Lecture Notes in Computer Science (LNCS), vol. 4578, pp. 203–210, 2007