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.

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

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

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.