Arsenii Ashukha

According to our database, Arsenii Ashukha authored at least 13 papers between 2017 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Resolution-robust Large Mask Inpainting with Fourier Convolutions.

Roman Suvorov, Elizaveta Logacheva, Anton Mashikhin, Anastasia Remizova, Arsenii Ashukha, Aleksei Silvestrov, Naejin Kong, Harshith Goka, Kiwoong Park, Victor Lempitsky

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2021

Automating Control of Overestimation Bias for Continuous Reinforcement Learning.

Arsenii Kuznetsov, Alexander Grishin, Artem Tsypin, Arsenii Ashukha, Dmitry P. Vetrov

CoRR, 2021

Mean Embeddings with Test-Time Data Augmentation for Ensembling of Representations.

Arsenii Ashukha, Andrei Atanov, Dmitry P. Vetrov

CoRR, 2021

2020

Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation.

Alexander Lyzhov, Yuliya Molchanova, Arsenii Ashukha, Dmitry Molchanov, Dmitry P. Vetrov

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning.

Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry P. Vetrov

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Semi-Conditional Normalizing Flows for Semi-Supervised Learning.

Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry P. Vetrov

CoRR, 2019

Variance Networks: When Expectation Does Not Meet Your Expectations.

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 7th International Conference on Learning Representations, 2019

The Deep Weight Prior.

Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

The Deep Weight Prior. Modeling a prior distribution for CNNs using generative models.

Andrei Atanov, Arsenii Ashukha, Kirill Struminsky, Dmitry P. Vetrov, Max Welling

CoRR, 2018

Bayesian Incremental Learning for Deep Neural Networks.

Max Kochurov, Timur Garipov, Dmitry Podoprikhin, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 6th International Conference on Learning Representations, 2018

Uncertainty Estimation via Stochastic Batch Normalization.

Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry P. Vetrov

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Structured Bayesian Pruning via Log-Normal Multiplicative Noise.

Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Dropout Sparsifies Deep Neural Networks.

Dmitry Molchanov, Arsenii Ashukha, Dmitry P. Vetrov

Proceedings of the 34th International Conference on Machine Learning, 2017