Dmitry Vetrov
Dmitry Vetrov
Higher School of Economics, Samsung AI Center, Moscow
Verified email at - Homepage
TitleCited byYear
Evaluation of stability of k-means cluster ensembles with respect to random initialization
LI Kuncheva, DP Vetrov
IEEE transactions on pattern analysis and machine intelligence 28 (11), 1798 …, 2006
Tensorizing neural networks
A Novikov, D Podoprikhin, A Osokin, DP Vetrov
Advances in neural information processing systems, 442-450, 2015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
Artificial Intelligence and Statistics, 130-138, 2016
Averaging weights leads to wider optima and better generalization
P Izmailov, D Podoprikhin, T Garipov, D Vetrov, AG Wilson
arXiv preprint arXiv:1803.05407, 2018
Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova, DP Vetrov, P Kohli
Advances in Neural Information Processing Systems, 947-955, 2016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, DP Vetrov
Advances in Neural Information Processing Systems, 6775-6784, 2017
Loss surfaces, mode connectivity, and fast ensembling of dnns
T Garipov, P Izmailov, D Podoprikhin, DP Vetrov, AG Wilson
Advances in Neural Information Processing Systems, 8789-8798, 2018
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
Entangled conditional adversarial autoencoder for de novo drug discovery
D Polykovskiy, A Zhebrak, D Vetrov, Y Ivanenkov, V Aladinskiy, ...
Molecular pharmaceutics 15 (10), 4398-4405, 2018
Inferring M-best diverse labelings in a single one
A Kirillov, B Savchynskyy, D Schlesinger, D Vetrov, C Rother
Proceedings of the IEEE International Conference on Computer Vision, 1814-1822, 2015
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
Submodular decomposition framework for inference in associative markov networks with global constraints
A Osokin, D Vetrov, V Kolmogorov
CVPR 2011, 1889-1896, 2011
A simple baseline for bayesian uncertainty in deep learning
W Maddox, T Garipov, P Izmailov, D Vetrov, AG Wilson
arXiv preprint arXiv:1902.02476, 2019
Putting MRFs on a tensor train
A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
M-best-diverse labelings for submodular energies and beyond
A Kirillov, D Shlezinger, DP Vetrov, C Rother, B Savchynskyy
Advances in Neural Information Processing Systems, 613-621, 2015
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
Predictive model for bottomhole pressure based on machine learning
P Spesivtsev, K Sinkov, I Sofronov, A Zimina, A Umnov, R Yarullin, ...
Journal of Petroleum Science and Engineering 166, 825-841, 2018
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