29 citations · 29 across the 1 of their papers we have counts for
3 papers
Stochasticity in Neural ODEs: An Empirical Study
Viktor Oganesyan, Alexandra Volokhova, Dmitry Vetrov
Stochastic regularization of neural networks (e.g. dropout) is a wide-spread technique in deep learning that allows for better generalization. Despite its success, continuous-time…
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
Andrei Atanov, Alexandra Volokhova, Arsenii Ashukha +2
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint dist…
Cherenkov Detectors Fast Simulation Using Neural Networks
Denis Derkach, Nikita Kazeev, Fedor Ratnikov +2
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details. This network is trained to reproduce high level…