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, DP Vetrov
Advances in neural information processing systems, 442-450, 2015
3962015
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
3302006
Variational dropout sparsifies deep neural networks
D Molchanov, A Ashukha, D Vetrov
arXiv preprint arXiv:1701.05369, 2017
3282017
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
1752018
Breaking sticks and ambiguities with adaptive skip-gram
S Bartunov, D Kondrashkin, A Osokin, D Vetrov
artificial intelligence and statistics, 130-138, 2016
1352016
Spatially Adaptive Computation Time for Residual Networks
M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ...
1342017
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, 8789-8798, 2018
1142018
Perforatedcnns: Acceleration through elimination of redundant convolutions
M Figurnov, A Ibraimova, DP Vetrov, P Kohli
Advances in Neural Information Processing Systems, 947-955, 2016
1012016
Structured bayesian pruning via log-normal multiplicative noise
K Neklyudov, D Molchanov, A Ashukha, DP Vetrov
Advances in Neural Information Processing Systems, 6775-6784, 2017
882017
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, 13153-13164, 2019
752019
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
642018
Ultimate tensorization: compressing convolutional and fc layers alike
T Garipov, D Podoprikhin, A Novikov, D Vetrov
arXiv preprint arXiv:1611.03214, 2016
642016
Spatial inference machines
R Shapovalov, D Vetrov, P Kohli
Proceedings of the IEEE conference on computer vision and pattern …, 2013
422013
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
362015
Fast adaptation in generative models with generative matching networks
S Bartunov, DP Vetrov
arXiv preprint arXiv:1612.02192, 2016
32*2016
Variational autoencoder with arbitrary conditioning
O Ivanov, M Figurnov, D Vetrov
arXiv preprint arXiv:1806.02382, 2018
302018
Putting MRFs on a tensor train
A Novikov, A Rodomanov, A Osokin, D Vetrov
International Conference on Machine Learning, 811-819, 2014
222014
Submodular decomposition framework for inference in associative markov networks with global constraints
A Osokin, D Vetrov, V Kolmogorov
CVPR 2011, 1889-1896, 2011
222011
Uncertainty estimation via stochastic batch normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Symposium on Neural Networks, 261-269, 2019
202019
M-best-diverse labelings for submodular energies and beyond
A Kirillov, D Shlezinger, DP Vetrov, C Rother, B Savchynskyy
Advances in Neural Information Processing Systems, 613-621, 2015
192015
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