Publications

2018

  • Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson. Averaging Weights Leads to Wider Optima and Better Generalization. Uncertainty in Artificial Intelligence (UAI) 2018 - link
  • Artyom Gadetsky, Ilya Yakubovskiy, Dmitry Vetrov. Conditional Generators of Words Definitions. 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018) - link
  • Rémi Leblond, Jean-Baptiste Alayrac, Anton Osokin, Simon Lacoste-Julien. SEARNN: Training RNNs with global-local losses. International Conference on Learning Representations (ICLR) 2018 - link
  • Valentin Khrulkov, Alexander Novikov, Ivan Oseledets. Expressive power of recurrent neural networks. International Conference on Learning Representations (ICLR) 2018 - link
  • Sergey Bartunov, Dmitry P. Vetrov. Fast Adaptation in Generative Models with Generative Matching Networks. The 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018 - link
  • Pavel Izmailov, Alexander Novikov, Dmitry Kropotov. Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition. The 21st International Conference on Artificial Intelligence and Statistics (AISTATS) 2018 - link
  • Pavel Spesivtsev, Konstantin Sinkov, Ivan Sofronov, Anna Zimina, Alexey Umnov, Ramil Yarullin, Dmitry Vetrov. Predictive model for bottomhole pressure based on machine learning. Journal of Petroleum Science and Engineering - link
  • Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry Vetrov. Uncertainty Estimation via Stochastic Batch Normalization. International Conference on Learning Representations Workshop (ICLR Workshop) 2018 - link
  • Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov. Bayesian Incremental Learning for Deep Neural Networks. International Conference on Learning Representations Workshop (ICLR Workshop) 2018 - link
  • Alexander Chistyakov, Ekaterina Lobacheva, Alexander Shevelev, Alexey Romanenko. Monotonic models for real-time dynamic malware detection. International Conference on Learning Representations Workshop (ICLR Workshop) 2018 - link

2017

2016

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

2015

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

2014

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

2013

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

2012

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

2011

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

2010

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

2009

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

2008

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

2007

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