Zobrazeno 1 - 10
of 306
pro vyhledávání: '"A. A. Volokhova"'
Autor:
Yu. V. Khoronko, M. A. Kozyrevskiy, A. V. Dmitriev, A. A. Volokhova, G. Yu. Spiridenko, K. R. Bogomolova
Publikováno v:
Российский журнал гастроэнтерологии, гепатологии, колопроктологии, Vol 31, Iss 1, Pp 39-46 (2021)
Aim. Improvement of refractory ascites (RA) outcomes in underlying cirrhotic portal hypertension (PH) through optimising the transjugular intrahepatic portosystemic shunting (TIPS) procedure among therapy measures.Materials and methods. The survey in
Externí odkaz:
https://doaj.org/article/89cdcb7305294e8d8f2945411c411bd5
Autor:
AI4Science, Mila, Hernandez-Garcia, Alex, Duval, Alexandre, Volokhova, Alexandra, Bengio, Yoshua, Sharma, Divya, Carrier, Pierre Luc, Benabed, Yasmine, Koziarski, Michał, Schmidt, Victor
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis. Discovering new solid-state materials such as electrocatalysts, super-ionic conductors or photovoltaic materials can have a crucial impact, for instance,
Externí odkaz:
http://arxiv.org/abs/2310.04925
Autor:
Lahlou, Salem, Deleu, Tristan, Lemos, Pablo, Zhang, Dinghuai, Volokhova, Alexandra, Hernández-García, Alex, Ezzine, Léna Néhale, Bengio, Yoshua, Malkin, Nikolay
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects. A key limitation of GFlowNets until this time has been that they are res
Externí odkaz:
http://arxiv.org/abs/2301.12594
Autor:
Bijan Barghi, Tanel Mõistlik, Anastassia Raag, Maria Volokhova, Indrek Reile, Liis Seinberg, Valdek Mikli, Allan Niidu
Publikováno v:
ACS Omega, Vol 9, Iss 22, Pp 23329-23338 (2024)
Externí odkaz:
https://doaj.org/article/2586b5571528424eb56d24704cfddf7b
Autor:
Zhang, Dinghuai, Malkin, Nikolay, Liu, Zhen, Volokhova, Alexandra, Courville, Aaron, Bengio, Yoshua
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data. Building upon the theory of generative flow networks (GFlowNets), we model the generation process by a stochastic
Externí odkaz:
http://arxiv.org/abs/2202.01361
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 models, such as neural ordinary differential equation (ODE), usually r
Externí odkaz:
http://arxiv.org/abs/2002.09779
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 distribution over objects and labels. Semi-conditional architecture of the
Externí odkaz:
http://arxiv.org/abs/1905.00505
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 features of the simulated detector events based on input observables o
Externí odkaz:
http://arxiv.org/abs/1903.11788
Autor:
O. Kashchenko, O. Stoyanov, G. Volokhova, V. Berbek, D. Voloshchuk, O. Pryshchepa, L. Zayats, S. Tatarko
Publikováno v:
Journal of Education, Health and Sport, Vol 13, Iss 3 (2023)
The cholinergic mechanisms role determination in epileptogenesis attracts the attention of researchers. Pilocarpine administration in rats contributes to chronic form of epileptiform activity development characterized by the presence of a pronounced
Externí odkaz:
https://doaj.org/article/e53694e96f6145de8581bb33bfd1a4b4
Publikováno v:
TEM Journal. 10(3):1155-1165
Externí odkaz:
https://www.ceeol.com/search/article-detail?id=977780