Dmitry Vetrov
Dmitry Vetrov
Higher School of Economics, Samsung AI Center, Moscow
Verified email at hse.ru - Homepage
Title
Cited by
Cited by
Year
Tensorizing neural networks
A Novikov, D Podoprikhin, A Osokin, D Vetrov
arXiv preprint arXiv:1509.06569, 2015
5932015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
International Conference on Machine Learning, 2498-2507, 2017
5442017
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
4072018
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
3592006
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
2272017
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, 13153-13164, 2019
2172019
Loss surfaces, mode connectivity, and fast ensembling of dnns
T Garipov, P Izmailov, D Podoprikhin, D Vetrov, AG Wilson
Proceedings of the 32nd International Conference on Neural Information …, 2018
2172018
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
artificial intelligence and statistics, 130-138, 2016
1692016
Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova, DP Vetrov, P Kohli
Advances in neural information processing systems 29, 947-955, 2016
1442016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, D Vetrov
arXiv preprint arXiv:1705.07283, 2017
1372017
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
1152016
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
1032018
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
832020
Variational autoencoder with arbitrary conditioning
O Ivanov, M Figurnov, D Vetrov
arXiv preprint arXiv:1806.02382, 2018
702018
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
62*2016
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
482013
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
462020
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
452015
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
382019
Putting MRFs on a tensor train
A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
322014
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