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Dmitry Vetrov
Dmitry Vetrov
Higher School of Economics, AI Research Institute, Moscow
Bestätigte E-Mail-Adresse bei hse.ru - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Tensorizing neural networks
A Novikov, D Podoprikhin, A Osokin, DP Vetrov
Advances in neural information processing systems 28, 2015
7162015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
International Conference on Machine Learning, 2498-2507, 2017
6962017
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
6572018
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
3812019
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
3682006
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
3252018
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
2722017
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
artificial intelligence and statistics, 130-138, 2016
1812016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, DP Vetrov
Advances in Neural Information Processing Systems 30, 2017
1622017
Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova, DP Vetrov, P Kohli
Advances in neural information processing systems 29, 2016
1592016
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
1552020
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
1512016
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
1432018
Variational autoencoder with arbitrary conditioning
O Ivanov, M Figurnov, D Vetrov
arXiv preprint arXiv:1806.02382, 2018
992018
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
82*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
792020
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
482019
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
482013
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
432020
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
432018
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