Andrei Atanov

I am a first-year PhD student at VILAB, EPFL, supervised by Amir Zamir. My research is focused on deep learning for computer vision. Previously I was a junior research fellow at BayesGroup and Samsung-HSE Lab, supervised by Dmitry Vetrov. I received my bachelor's and master's degree in Computer Science from the HSE University.

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I am broadly interested in deep learning, generative models, and Bayesian modeling. I mostly work on visual perception and learning from a small amount of data/labels.

The Deep Weight Prior
Andrei Atanov*, Arsenii Ashukha*, Kirill Struminsky, Dmitry Vetrov, Max Welling,
ICLR, 2019

We propose a flexible prior distribution over convolutional kernels of Bayesian neural networks.

Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha, Ivan Sosnovik, Dmitry Vetrov,
INNF Workshop at ICML, 2019

We apply conditional normalizing flows to semi-supervised learning. We utilize multiscale architecture for computational efficiency.

Uncertainty Estimation via Stochastic Batch Normalization
Andrei Atanov, Arsenii Ashukha, Dmitry Molchanov, Kirill Neklyudov, Dmitry Vetrov,
ICLR Workshop Track, 2018

We propose a probabilistic view on Batch Normalization and an efficient test-time averaging technique for uncertainty estimation in batch-normalized DNNs.

Credits to Jon Barron for the template.