Dmitry Vetrov
Dmitry Vetrov
Higher School of Economics, Samsung AI Center, Moscow
Подтвержден адрес электронной почты в домене hse.ru - Главная страница
НазваниеПроцитированоГод
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
3062006
Tensorizing neural networks
A Novikov, D Podoprikhin, A Osokin, DP Vetrov
Advances in neural information processing systems, 442-450, 2015
2742015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017
1652017
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
Artificial Intelligence and Statistics, 130-138, 2016
852016
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
832017
Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova, DP Vetrov, P Kohli
Advances in Neural Information Processing Systems, 947-955, 2016
692016
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
642018
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
622017
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
412013
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
372016
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
352018
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
292015
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
242018
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
21*2016
Submodular decomposition framework for inference in associative markov networks with global constraints
A Osokin, D Vetrov, V Kolmogorov
CVPR 2011, 1889-1896, 2011
202011
Putting MRFs on a tensor train
A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
182014
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
172015
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
122019
Automated atlas-based segmentation of Nissl-stained mouse brain sections using supervised learning
OV Senyukova, AS Lukin, DP Vetrov
Programming and Computer Software 37 (5), 245, 2011
122011
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
112018
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