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
5532015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
International Conference on Machine Learning, 2498-2507, 2017
4882017
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
3532006
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
3452018
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
2192017
Loss surfaces, mode connectivity, and fast ensembling of dnns
T Garipov, P Izmailov, D Podoprikhin, D Vetrov, AG Wilson
arXiv preprint arXiv:1802.10026, 2018
1862018
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
1792019
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
artificial intelligence and statistics, 130-138, 2016
1652016
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
1362016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, D Vetrov
arXiv preprint arXiv:1705.07283, 2017
1222017
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
1022016
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
992018
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
612020
Variational autoencoder with arbitrary conditioning
O Ivanov, M Figurnov, D Vetrov
arXiv preprint arXiv:1806.02382, 2018
572018
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
57*2016
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
492013
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
422015
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
372019
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
352020
Putting MRFs on a tensor train
A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
302014
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