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Dmitry Vetrov
Dmitry Vetrov
Higher School of Economics, AI Research Institute, Moscow
Verified email at hse.ru - Homepage
Title
Cited by
Cited by
Year
Tensorizing neural networks
A Novikov, D Podoprikhin, A Osokin, DP Vetrov
Advances in neural information processing systems 28, 2015
7512015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
International Conference on Machine Learning, 2498-2507, 2017
7382017
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
7362018
A simple baseline for bayesian uncertainty in deep learning
WJ Maddox, P Izmailov, T Garipov, DP Vetrov, AG Wilson
Advances in Neural Information Processing Systems 32, 2019
4322019
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
3732006
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 31, 2018
3552018
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
2862017
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
artificial intelligence and statistics, 130-138, 2016
1852016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, DP Vetrov
Advances in Neural Information Processing Systems 30, 2017
1732017
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning
A Ashukha, A Lyzhov, D Molchanov, D Vetrov
arXiv preprint arXiv:2002.06470, 2020
1712020
Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova, DP Vetrov, P Kohli
Advances in neural information processing systems 29, 2016
1642016
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
1572016
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
1462018
Variational autoencoder with arbitrary conditioning
O Ivanov, M Figurnov, D Vetrov
arXiv preprint arXiv:1806.02382, 2018
1072018
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
87*2016
Subspace inference for Bayesian deep learning
P Izmailov, WJ Maddox, P Kirichenko, T Garipov, D Vetrov, AG Wilson
Uncertainty in Artificial Intelligence, 1169-1179, 2020
862020
Controlling overestimation bias with truncated mixture of continuous distributional quantile critics
A Kuznetsov, P Shvechikov, A Grishin, D Vetrov
International Conference on Machine Learning, 5556-5566, 2020
512020
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
492019
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
482018
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
482013
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