Averaging weights leads to wider optima and better generalization P Izmailov, D Podoprikhin, T Garipov, D Vetrov, AG Wilson arXiv preprint arXiv:1803.05407 |
Variational dropout sparsifies deep neural networks D Molchanov, A Ashukha, D Vetrov International conference on machine learning, 2498-2507 |
Tensorizing neural networks A Novikov, D Podoprikhin, A Osokin, DP Vetrov Advances in neural information processing systems 28 |
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 |
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 |
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 … |
Spatially Adaptive Computation Time for Residual Networks M Figurnov, M Collins, Y Zhu, L Zhang, J Huang, DP Vetrov, ... |
Pitfalls of in-domain uncertainty estimation and ensembling in deep learning A Ashukha, A Lyzhov, D Molchanov, D Vetrov arXiv preprint arXiv:2002.06470 |
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 |
Structured bayesian pruning via log-normal multiplicative noise K Neklyudov, D Molchanov, A Ashukha, DP Vetrov Advances in Neural Information Processing Systems 30 |
Ultimate tensorization: compressing convolutional and fc layers alike T Garipov, D Podoprikhin, A Novikov, D Vetrov arXiv preprint arXiv:1611.03214 |
Breaking sticks and ambiguities with adaptive skip-gram S Bartunov, D Kondrashkin, A Osokin, D Vetrov artificial intelligence and statistics, 130-138 |
Perforatedcnns: Acceleration through elimination of redundant convolutions M Figurnov, A Ibraimova, DP Vetrov, P Kohli Advances in neural information processing systems 29 |
Subspace inference for Bayesian deep learning P Izmailov, WJ Maddox, P Kirichenko, T Garipov, D Vetrov, AG Wilson Uncertainty in Artificial Intelligence, 1169-1179 |
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 |
Variational autoencoder with arbitrary conditioning O Ivanov, M Figurnov, D Vetrov arXiv preprint arXiv:1806.02382 |
Fast adaptation in generative models with generative matching networks S Bartunov, DP Vetrov arXiv preprint arXiv:1612.02192 |
Conditional generators of words definitions A Gadetsky, I Yakubovskiy, D Vetrov arXiv preprint arXiv:1806.10090 |
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 |
Greedy policy search: A simple baseline for learnable test-time augmentation A Lyzhov, Y Molchanova, A Ashukha, D Molchanov, D Vetrov Conference on uncertainty in artificial intelligence, 1308-1317 |
