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, D Vetrov
arXiv preprint arXiv:1509.06569, 2015
6122015
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
International Conference on Machine Learning, 2498-2507, 2017
5602017
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
4402018
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
3622006
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
2352019
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
2352017
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
2282018
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
artificial intelligence and statistics, 130-138, 2016
1722016
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
1482016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, D Vetrov
arXiv preprint arXiv:1705.07283, 2017
1392017
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
1212016
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
1092018
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
842020
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
64*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
512020
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
482013
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
392019
Putting MRFs on a tensor train
A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
332014
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