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Regular version of the site

Publications

2017

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


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. papersupplementary material
  • 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. paper
  • Michael Figurnov, Aijan Ibraimova, Dmitry Vetrov, Pushmeet Kohli. PerforatedCNNs: Acceleration through elimination of redundant convolutions. Advances in Neural Information Processing Systems (NIPS) 2016. paper
  • 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. paper
  • 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.  paper
  • 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. paper
  • Alexander Novikov, Mikhail Trofimov, Ivan Oseledets. Tensor Train polynomial model via Riemannian optimization (ICML) 2016. Advances in non-convex analysis and optimization Workshop. paper
  • Michael Figurnov, Dmitry Vetrov, Pushmeet Kohli. PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions. International Conference on Learning Representations (ICLR) 2016 Workshop track. paper
  • Dmitry Molchanov, Arseniy Ashuha and Dmitry Vetrov. Dropout-based Automatic Relevance Determination. Advances in Neural Information Processing Systems (NIPS) 2016. Bayesian Deep Learning Workshop. paper, poster 
  • Michael Figurnov, Kirill Struminsky, Dmitry Vetrov. Robust Variational Inference. Advances in Neural Information Processing Systems (NIPS) 2016. Advances in Approximate Bayesian Inference Workshoppaperposter
  • Timur Garipov, Dmitry Podoprikhin, Alexander Novikov, Dmitry Vetrov. Ultimate tensorization: convolutions and FC alike. Advances in Neural Information Processing Systems (NIPS) 2016. Tensor-Learn Workshoppaperposter, pres

2015

  • A. Rodomanov, D. Kropotov. A Newton-type Incremental Method with a Superlinear Convergence Rate, NIPS 2015 Workshop on Optimization for Machine Learning.  pdf
  • 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. pdf
  • Ekaterina Lobacheva, Olga Veksler, Yuri Boykov. Joint Optimization of Segmentation and Color Clustering. Proceedings of the International Conference on Computer Vision (ICCV). 2015. pdf
  • Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov. Tensorizing Neural Networks. In Advances in Neural Information Processing Systems 28 (NIPS). 2015. pdf
  • 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. pdf
  • 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. pdf
  • 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. pdf

2014

  • A. Osokin, D. Vetrov. Submodular relaxation for inference in Markov random fields. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). Accepted. 2014. pdf + supplementary code
  • A. Osokin, P. Kohli. Perceptually Inspired Layout-aware Losses for Image Segmentation. European Conference on Computer Vision (ECCV), 2014. pdf
  • 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. pdf
  • 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. pdf supplementary

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.  pdf
  • R. Shapovalov, D. Vetrov, P. Kohli. Spatial Inference Machines. In Computer Vision and Pattern Recognition (CVPR), 2013. pdf
  • P. Kohli, A. Osokin, and S. Jegelka. A Principled Deep Random Field Model for Image Segmentation. In Computer Vision and Pattern Recognition (CVPR), 2013. pdfsupplementary
  • 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.  pdf
  • 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.  pdf

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.  pdfcode
  • 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.  pdf + supplementary
  • 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. pdf

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.  pdf
  • 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.  pdf
  • 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.  pdf
  • 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.  pdf
  • 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
  • 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.  pdfcode
  • 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.  pdf 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.  pdf
  • 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.  pdf
  • 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.  pdf

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.  pdf
  • 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.  pdf

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.  pdf 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

 

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