1 code implementation • ICML Workshop INNF 2021 • Dmitry Baranchuk, Vladimir Aliev, Artem Babenko
Normalizing flows are a powerful class of generative models demonstrating strong performance in several speech and vision problems.
1 code implementation • 7 Aug 2019 • Dmitry Nikulin, Anastasia Ianina, Vladimir Aliev, Sergey Nikolenko
We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i. e., it is "free", with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents.
no code implementations • 27 Nov 2018 • Pavel Solovev, Vladimir Aliev, Pavel Ostyakov, Gleb Sterkin, Elizaveta Logacheva, Stepan Troeshestov, Roman Suvorov, Anton Mashikhin, Oleg Khomenko, Sergey I. Nikolenko
Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors.
no code implementations • 12 Sep 2018 • Pavel Ostyakov, Elizaveta Logacheva, Roman Suvorov, Vladimir Aliev, Gleb Sterkin, Oleg Khomenko, Sergey I. Nikolenko
Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge.