Arsenii Ashukha
Arsenii Ashukha
PhD Candidate at Bayesian Methods Research Group & Samsung AI
Verified email at samsung.com - Homepage
TitleCited byYear
Variational Dropout Sparsifies Deep Neural Networks
D Molchanov*, A Ashukha*, D Vetrov
Proceedings of the 34th International Conference on Machine Learning (ICML 2017), 2017
1932017
Structured Bayesian Pruning via Log-Normal Multiplicative Noise
K Neklyudov, D Molchanov, A Ashukha, D Vetrov
Advances in Neural Information Processing Systems 30 (NIPS 2017), 2017
692017
Uncertainty Estimation via Stochastic Batch Normalization
A Atanov, A Ashukha, D Molchanov, K Neklyudov, D Vetrov
International Conference on Learning Representations, Workshop Track (ICLR 2018), 2018
132018
Variance Networks: When Expectation Does Not Meet Your Expectations
K Neklyudov*, D Molchanov*, A Ashukha*, D Vetrov
International Conference on Learning Representations (ICLR 2019), 2018
72018
Bayesian Incremental Learning for Deep Neural Networks
M Kochurov, T Garipov, D Podoprikhin, D Molchanov, A Ashukha, ...
International Conference on Learning Representations, Workshop Track (ICLR 2018), 2018
72018
The Deep Weight Prior
A Atanov*, A Ashukha*, K Struminsky, D Vetrov, M Welling
International Conference on Learning Representations (ICLR 2019), 2018
5*2018
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
A Atanov, A Volokhova, A Ashukha, I Sosnovik, D Vetrov
arXiv preprint arXiv:1905.00505, 2019
32019
Dropout-based automatic relevance determination
D Molchanov, A Ashuha, D Vetrov
Bayesian Deep Learning workshop, NIPS, 2016
22016
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Articles 1–8