Mikhail Kudinov — accepted AI-conference papers

2021

Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech

Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Kudinov

ICML 2021spotlight

Recently, denoising diffusion probabilistic models and generative score matching have shown high potential in modelling complex data distributions while stochastic calculus has provided a unified point of view on these techniques allowing for flexible inference schemes. In this paper we introduce Gr…

Cited by 635 Source ↗PDF ↗code ↗Scholar ↗

2018

Distributed Fine-tuning of Language Models on Private Data

Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky

ICLR 2018poster

One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm. In language modeling, users’ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly ava…

Cited by 22 Source ↗PDF ↗Scholar ↗